Relationship between Bitcoin Exchange Rate and Other Financial Indexes in Time Series

Department of Fashion Business and Merchandising, Ling Tung University, Taichung, Taiwan Department of International Business, Providence University, Taichung 43301, Taiwan Department of Leisure and RecreationManagement, National Taichung University of Science and Technology, Taichung, Taiwan Department of Finance, Providence University, Taichung, Taiwan Department of Finance, National Taichung University of Science and Technology, Taichung, Taiwan Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan Department of Aeronautical Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan

Introduction
e Bitcoin exchange rate (BER) is extremely volatile. As shown in Figure 1, BER to the US dollar from October 01, 2013, to June 22, 2018, increased by nearly 2 million times from less than USD 0.01 to 19,345.49 on December 16, 2017. Kurka concluded that BER is independent of any financial asset classes in the system, but its spillover effect affected the traditional financial markets [1]. e pricing models of stock price or exchange rate have assumptions of transactions by rational investors. However, researchers pointed out that investment decisions made by investors are not necessarily rational. us, behavioral financial theories emerged to explain irrational decisions. For example, Barber and Odean proposed the "Attention eory" that explained investors' intention of buying stocks without having time to interpret the disclosure of massive information on the stocks [2]. Merton suggested the "Investor Recognition Hypothesis" that stated the information spillover effect of investors' stock buying on the firm's visibility [3]. e higher the visibility of a firm, the more the interest investors have in the stock. It yields higher price returns and greater trading volume. Cai et al. proved that Google SVI of Bitcoin had a significant impact on its price and transaction volume as the proxy explanatory variable of "investor focus" [4].
is research employs the vector autoregression (VAR) model to explore the long-and short-term relationship between the Google SVI and BER.
e key variables are studied by applying the theoretical basis of "investor attention" that influences BER. Based on the findings of the current research, the important variables of traditional financial assets are selected in consideration of the volatility index (VIX), the S & P500 index, and the Google SVI. e vector autoregression (VAR) analysis of the short-and longterm impacts and Granger causal relationship ( [5][6][7]) are employed to develop a prediction model for BER change for providing a reference for Bitcoin hedging operation to investors.
e BER is a new investment tool with higher volatility to the traditional financial product. Investors have little hedge and others reference target to avoid the risk of BER. e forecasting model of BER can offer mostly relation financial products and be applied for hedge, arbitrage for investors. e result expected the SVI should have higher relationship in the short and long periods.
is article is organized as follows: Section 2 reviews previous references and discusses the relation of the Google SVI and VIS, in variable explanation to Bitcoin. Section 3 presents the VAR for the data analysis as the research method. Section 4 describes the analysis results. Finally, Section 5 concludes this article.
Since "Google Trends" was first introduced, the number of searches was queried by entering the targeted word. Later on, Google launched "Google Insights for Search" which divided the data into detailed categories such as time and geographical regions. "Google Insights for Search" became the current Google Trend in September 2012. e time range for searching is customized or selected in the past 1, 4 hours, 1, 7, 30, 90, and 5 years. Up to 5 keywords are analyzed simultaneously. e search items are searched for their trends in a single country or all over the world. e categories are selected by the industry of interest or all categories. After determining the keywords, region, periods, and categories, Google Trends generate a trend chart with a standard quantitative range from 0 to 100 and calculate the SVI data based on the average time series, but the data frequency is a week. SVI calculation formula is as follows: SVI � total search volume maximum total search volume in a given time period × 100.
(1) e SVI may yield different results for the same keywords according to different query, time, date, and geographic location. When Google compiles SVI data, it does not search all data but randomly selects samples. However, different results did not change the research outcomes [12]. Takeda and Wakao present the SVI has a positive impact on the stock price and volume [13]. Aouadi et al. using Google search volume to evidence the relationship among the attention of French stock market investors and trading volume, stock market illiquidity, and volatility [10].
Zhang et al. pointed out that the long-term prices converged to the mean reversion when stock prices rose solely with increased investor attention [14]. To understand whether the mean reversion of BER is related to Google SVI, we selected Google SVI as the proxy interpretation variable for "investor focus". Details are shown in Figure 2.

VIX.
e Volatility Index (VIX) is compiled by the COBE Exchange that implies volatility of future options on the S&P500 and reflects the degree of risk in the stock market over the next 30 days. e VIX greater than 40 indicates that market investors expect a strong fluctuation of stock index in the future, that is, irrational panic. When the index is less than 15, it means the investors believe the volatility in the future stock market to be mild which shows irrational exuberance. As it reflects the traders' expectations on future stock price change, it is also called the "Fear Index" or " e Investor Fear Gauge." Qadan et al. believed that VIX reflected the investors' sentiment. In a period of extreme uncertainty, investors tend to be more risk-averse and therefore need higher idiosyncratic volatility (IVOL) premium at a high level of VIX [15]. Under the circumstance, investors avoid stocks of high IVOL, which results in lower investment returns with high IVOL than with low IVOL. Tsai et al. also found the reversed prices in the future led to significant negative investment loss when investors overestimated stock prices positively [16]. Simon and Wiggins showed that the VIX index was used to predict the future stock market [17]. Lee pointed the new sentiment of the current period has a positive relationship with investment reports, and the new sentiment of the lagged period has a negative relationship with investment aspirations [18]. Copeland and Copeland noted that COBE Volatility Index (VIX) can be treated as leading index of stock price index [19]. Dennis et al. treated VIX is a proxy variable of stock return volatility [20]. When the VIX soars and the market shows extreme fear, it would be the best opportunity to enter the stock market.
is research includes the VIX by Chicago Board Options Exchange (COBE) as a proxy explanatory variable for investor sentiment in an empirical study to understand the impact of investor sentiment on the BER.

Bitcoin.
Kurka studied the interaction between Bitcoin and traditional finance assets [1]. e results showed that BER was independent of any financial system assets. ough traditional financial assets had little impact on the BER trend, the spillover effect of BER was significant in the traditional financial market. Cai et al. adopted the Google SVI as the proxy explanatory variable for investors' decisions [4]. e results supported that the SVI had a vital influence on the price and volume of Bitcoin. Dastgir et al. surveyed Google Trends search flow on Bitcoin [21]. It employed the Granger causality to test the causal relationship between the interest in Bitcoin and investment returns. e conclusion supported the existence of bidirectional causality between Bitcoin and investment returns.
is study analyzed the short-and long-term relationship between Google SVI and BER by the VAR model. e longterm relationship among BER, VIX, and the S&P500 index was also investigated for a co-integration phenomenon.

Methodology and Model
e research process was divided into two aspects as shown in Figure 3. For the short-term impact analysis, the VAR model and Granger causality were employed to test the explanatory variables of BER and verify their relationship, respectively. For the long-term impact, Nelson and Plosser argued that the variable difference was lost in the implicit information on the long-term equilibrium through the stationary sequence [22]. erefore, the co-integration analysis and vector error correction model (VECM) validated the long-term relationship between the variables.

VAR Model and VECM.
e VAR model in this study is defined as follows: where y t is a (n×1) vector of endogenous variables, c � (c 1 , .. .., c n ) is the (n×1) intercept vector of the VAR, ∅ t is the i-th (n×n) matrix of autoregressive coefficients for i � 1, 2, ..., n, and ε t � (ε 1 . . . ε t ) is the (n×1) generalization of a white noise process [24]. e following equation is the co-integration transformation of equation (2). .
If y t has a co-integration relationship, then Πy t−1 ∼ I(0) and equation (3) is expressed as follows: where β ′ y t−1 � ECM t−1 is the error correction term for a long-term equilibrium relationship between variables. Equation (4)

Mathematical Problems in Engineering
Equation (5) is used for the vector error correction model (VECM) in which each equation belongs to an error correction model [25].
Before establishing the VAR model, it is necessary to choose the optimal lagged periods. e criteria include Akaike's Information Criterion (AIC), Hannan-Quinn Criterion (HQ), Schwarz Criterion (SC) likelihood ratio (LR), and so on. However, the Akaike proposed the AIC method and SC method which are most used [26]. e equations of the AIC and SC criteria are defined as follows: where k is the number of all parameters to be estimated by the VAR model. e number of periods corresponding to the minimum value of the measurement of AIC or SC is the optimal number of lagged terms. e AIC is more consistent than SC. However, as the estimated parameters of the AIC are less and the number of samples is larger than the SC, the SC is better than the AIC for this research.

Multivariate Granger Causality
Analysis. Multivariate Granger causality analysis is performed by fitting a VAR model to the time series. Let Χ(t) ∈ R d×1 (t � 1, . . . , T) be a d-dimensional multivariate time series. Multivariate Granger causality is measured by fitting a VAR model to L time lags as follows: where ] t is a white Gaussian random vector and Φ λ is a matrix for every λ. A time series X i is called a Granger cause of another time series X j , if at least one of the elements Φ λ (i, j) is significantly larger than zero.

Data Source and Sample Collection Period.
is research mainly discussed the key factors that affect the changes in BER by referring to Cai et al. [4], Kurka [1], and Hsieh [27]. e main variables included Google SVI, the VIX of Chicago Board of Exchange, S&P500 index, gold prices, US dollar index, and Japanese exchange rate. e period for sampling was from April 29, 2013, to June 22, 2018. e data sources were Yahoo Finance and Bloomberg. e data frequency was a day, and the data processing method did not affect the final results. If there was no observation value for any variable on the same day, the data of that day were deleted [28]. A total of 1,299 daily observation values for each variable were used.

Analysis Results and Discussion
e analysis was carried out in two processes as shown in Figure 4. We used the VAR model and Granger causality to test the explanatory stationary time series variables for the short-term impact. en we used the co-integration analysis and vector error correction model (VECM) to validate the long-term relationship between the nonstationary variables. e descriptive statistics, VAR model analysis, and the cointegration relationship were performed and illustrated below. Table 1 shows standard deviation, and maximum and minimum BER, gold price (GOLD), USD/JPY exchange rate (JPY), S&P500 index (SP500), Google SVI, US dollar index (USD), and VIX during the study period.

Unit Root Verification.
e Augmented Dickey-Fuller (ADF) and the Phillips-Perron (PP) tests ( [29,30]) were applied to the unit root sequence in this study. By taking the natural logarithm and first-order difference, all the variables were in a significant stationary sequence as shown in Table 2.

Optimal Lagging Period Selection.
e research employed the AIC to determine the optimal number of two lagged periods. Table 3 shows the details of the optimal lagged periods.

Results of Analysis of the VAR Model.
e first-order difference in the variables presents a stationary sequence and is used to analyze the short-term influence of the variables with the VAR model. e results are shown in Table 4. BER of the two lagged periods had a positive impact (0.108278) on the overall BER at a significant level of 1%. e Google SVI had a negative effect (−0.100983) on the BER at a significant level of 1%, while Google SVI has a positive impact (0.064947) on the BER at a significant level of 1%. e S&P500 index had a negative impact (−0.108234) on the leading two-period S&P500 index at a significant level of 1%. p < 1, 5, or 10% of the significant level indicates that the coefficient is not equal to zero, that is, the S&P500 index Granger causality causes the lag two-period S&P500 index. e Granger causality diagram of the short-term variables affected the BER. e results demonstrated that the feedback relationship appeared only in the BER and the Google SVI among the Granger causality as shown in

Johansen Maximum Likelihood Co-Integration Test.
Nelson and Plosser supposed that a different process caused the time series to lose long-term information [22]. at is, all nonstationary time series become stationary after the first-order difference operation, so it is used to study the short-term influence between variables.
Johansen co-integration test was used to test long-term relationship among the variables, and the Trace test to obtain the number of co-integration vector (CE) equation sets for variables of no difference ( [31,32]). As shown in Table 6, the hypothesis assumes that there is none CE, and the trace statistics shows 226.9521 and larger than the critical value 134.6780 which means reject that there is none CE. erefore, Table 5 shows that there are two sets of co-integration equations under 5% critical value that are most relevant to the goodness-of-fit test because the trace statistics shows 75.8209 and smaller than the critical value 76.9727 at most 2, which means that we cannot reject that there are two co-integration vectors.

Vector Error Correction Estimates Model.
rough the Johansen Co-integration test, two sets of co-integration vector equations were obtained by using the trace test method. e co-integration vector equation and the VECM are shown in Table 6. e co-integration vector equations     Mathematical Problems in Engineering 5 show that equations (2) and (3) are based on the BER and Google SVI, respectively. e coefficients of the equation variables are highly significant to each other.

Long-Term Granger Causality.
Granger causality is a statistical concept of causality that is based on VAR. e coefficient of granger causality test is assumed that does not influence between two variables under null hypothesis as shown in Table 7. If the probability is larger than 0.1, then we do not reject the null hypothesis, and these two variables do not have granger causality. e results of the granger causality test are shown in Table 7. e BER influenced the S&P500 index and Google SVI. e Google SVI Granger caused the BER index. erefore, the BER and SVI presents intergranger causality. e S&P500 SVI Granger influenced Google SVI. e VIX Granger contributed to the S&P500 index, the BER index, and the Google SVI. Significant level of * * * 1%, * * 5%, * 10% and the coefficients means the estimated coefficients.  Figure 5 shows all variables that affect BER in the long-term. e "feedback relationship" of the Granger causality appears in BER and the Google SVI. e VIX has a moderately significant Granger causality on the Bitcoin price in the long term.

Conclusions
e analysis results of this research suggested the co-integrated equilibrium and feedback relationship of the Granger causality between BER and Google SVI in the long term. e VIX influences mostly the BER in the long-term cointegration.
In addition to confirming the results of Cai et al. [4] and Dastgir et al. [21], the result confirmed the Granger causality between Google SVI and BER and the existence of the short-and long-term feedback between Google SVI and the BER.
e result also showed that there is a long-term co-integration relationship between the BER, VIX, and Google SVI. By observing Google SVI and the VIX, investors predict the future trend of the BER as BER has a long-term leading relationship with the S&P500 index. e  Significant level of * * * 1%, * * 5%, and * 10%. crypto-economy affects the real economy as the spillover effect of BER influences the S&P500 [1]. When BER rises, the S&P index should be viewed with optimism. Also, the VIX has a long-term leading relationship with BER as it drops with the rise of BER. e co-integration equilibrium between BER and Google SVI in the long-term leads to the feedback relationship of the Granger causality, which impacts Bitcoin price and causes the following: the prohibition of the Bitcoin trading exchange, the issuer's implementation of hard fork policy that damages investors' rights and confidence with no compensation, and information security issues. e price of Bitcoin fell back to the equilibrium value, which verifies the investor attention theory. e results help investors hedging or arbitrating engaged Bitcoin-related risks.

Data Availability
e data used to support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest
e authors declare that they have no conflicts of interest.


Data on a synthetic farm population of the German federal state of North Rhine-Westphalia

Farm-scale and agent-based models draw typically on detailed and preferably spatially explicit single farm data. Data protection standards however restrict or exclude their access, as for example in Germany. We provide data on a synthetic farm population of the German federal state of North Rhine-Westphalia, mainly based on the German Farm Structure Survey 2016 and plot specific crop data from 2019/2020. The population is derived from farm typology at administrative unit level to which the observed plots are allocated afterwards. The data contains 25,858 farms and covers 1.3 million ha of agricultural land, provided at plot scale in a geospatial vector and at farm scale in tabular format. For each plot, the managing farm (including the estimated farm's location), the number of livestock, the cultivated crop, as well as the corresponding administration units are indicated. Furthermore, spatial data such as yield information, soil characteristics, as well as monitoring data on environmental status are attached. The provided data allows for diverse analysis on the farm population in the federal state of North Rhine-Westphalia with farm, agent-based or different bio-physical models. Furthermore, it can serve as a test data set for models which require detailed and spatially explicit farm data.

a b s t r a c t
Farm-scale and agent-based models draw typically on detailed and preferably spatially explicit single farm data. Data protection standards however restrict or exclude their access, as for example in Germany. We provide data on a synthetic farm population of the German federal state of North Rhine-Westphalia, mainly based on the German Farm Structure Survey 2016 and plot specific crop data from 2019/2020. The population is derived from farm typology at administrative unit level to which the observed plots are allocated afterwards. The data contains 25,858 farms and covers 1.3 million ha of agricultural land, provided at plot scale in a geospatial vector and at farm scale in tabular format. For each plot, the managing farm (including the estimated farm's location), the number of livestock, the cultivated crop, as well as the corresponding administration units are indicated. Furthermore, spatial data such as yield information, soil characteristics, as well as monitoring data on environmental status are attached. The provided data allows for diverse analysis on the farm population in the federal state of North Rhine-Westphalia with farm, agent-based or different bio-physical models. Furthermore, it can serve as a test data set for models which require detailed and spatially explicit farm data.  Table   Subject Agricultural Economics Specific subject area Bio-economic modeling using farm and agent-based models Type of data Table  Map How data were acquired The data was acquired by combining different agricultural farm-level surveys as secondary data sources and spatial data on farmland characteristics. Data format Analyzed Parameters for data collection Providing and analysing secondary data (1) From farm structure survey, number of farms in different size classes and farm types, agricultural land use in hectares, livestock numbers; both at county and commune level for the year 2016.
(2) From the Integrated Administration and Control System (IACS/INVEKOS) agricultural land use for the year 2020.
Description of data collection The underlying secondary data are extracted from open access excel files, literature and from open access spatial data in shape file format. Data source location All input and final data set cover the German federal state of North Rhine-Westphalia All used data sources are listed in Table 3 .

Data accessibility
The farm typology is available at Mendeley Data at farm and plot scale: https://doi.org/10.17632/75wngh8x4j.1

Value of the Data
• We provide spatial explicit data of an entire farm population. This is useful for studies considering farm heterogeneity, neighboring effects, and population-wide analysis. • The spatial explicit single farm data records are especially valuable for the use in farm-and agent-based models. • The farm population can feed into different types of assessment at farm-scale such as of policy impacts or technology adaption. • The provided farm population can be used as a test data set for models requiring detailed and spatial explicit farm-level data. • The data is not only of value for economic modeling exercises, but also for scientific work in fields such as landscape ecology or regional development where spatial explicit farm data are needed. • The methodology to create the farm population can be transferred to other regions where access to individual farm data records is restricted.

Data Description
The data set provides a synthetic farm population with single farm data of the German federal state of North Rhine-Westphalia, derived by combining different secondary data sources. This is particularly useful for single farm and agent-based models (ABM) that often require spatially explicit and highly detailed single farm data. The resulting population covers 25,858 single farms and 1.3 million ha of agricultural land in the state. This corresponds to approximately 77% of all farms and 89% of all agricultural land. The state covers a diverse farm population, comprising approximately 8600 specialized arable farms, 4800 specialized pig farms, 9500 specialized cattle farms, and 3100 mixed farms, of varying sizes. They are distributed over different landscapes, such as fertile plains dominated by specialized arable farms, sandy plains with are large share of intensive animal production, and low-mountain ranges characterized by permanent grassland and cattle production. For every farm, estimations of its location, of its managed plots with observed crops and of its livestock numbers are provided. Single farm data at this level of detail is required for spatial explicit or population-wide analysis. However, it is usually not available in Germany due to data protection guidelines. If access is granted, publication of results is restricted, and the handling of the data is governed by complex rules. The synthetic population presented here provides an alternative which reflects key characteristics of the actual farm population without drawing on detailed single farm, data protected information. The provided farms, including their location, do not correspond to observed real-world farms. Instead, they reflect the distribution of key characteristics in the true population and correspond, in their entirety, to observed statistical measurements. All underlying data sources are published and publicly available.
Different data sources are combined to derive the presented population ( Table 3 ). The core sources are a farm typology from Kuhn & Schäfer [1] and frequency tables of farms at commune level (LAU -Local Administrative Units), both based on the German Farm Structure Survey 2016. It is complemented by spatial explicit land use for the crop year 2019/2020, taken from the Integrated Administration and Control System (IACS) for the direct payments of the EU Common Agricultural policy. This land use data is linked to further spatial data such as yield information, soil characteristics, or monitoring data on environmental status.
The derived farm population is supplied at two scales and data formats at a Mendeley repository ( http://dx.doi.org/10.17632/75wngh8x4j.1 ). First, single farm data for the population is provided in CSV format, with one row per farm. The variables, reported in the columns ( Table 1 ), cover a unique farm ID, administrative units, longitude and latitude of the hypothetical farm location, livestock numbers, land use, information on plot size and plot-farmstead-distance, and a list of the managed plots. Second, data for each plot is provided in Shapefile format, reporting its exact spatial location as a polygon. The related attribute table contains additional information per plot ( Table 2 ), covering among others plot and farm ID, plot size, cultivated crop, administrative units, soil parameters, environmental parameters, and regional crop yields. Linkage of the data set can draw on the unique farm ID provided for every plot in the shapefile, or the list of plot IDs reported for each farm in the CSV file.

Materials and Methods
In the following, we document the methodology for creating the synthetic farm population for the German federal state of North Rhine-Westphalia. The population generation can be understood as descriptive research as we aim at characterizing and depicting the farm population in North Rhine-Westphalia without testing any hypothesis or drawing conclusions. It consists of three major steps as outlined in Fig. 1 . First, we generate farm frequency tables at LAU level. Second, we process the contingency tables sourced from the farm typology by Kuhn and Schäfer [1] , match it to a fitting farm from the previously generated frequency tables at LAU level, and create a spatially implicit farm population. Third, the farms are assigned random locations within the boundaries of their LAU. Finally, the observed plots, sourced from the publicly available IACS dataset, are assigned to the individual farms, based on their aspired farm size, crop cultivation specialization, and grassland endowment. The latter steps then turn the spatially implicit into a spatially explicit farm population. The created synthetic farm population is linked to further spatial data on yields, soil characteristics, and monitoring data on environmental status.
The data sources used in our methodology are outlined in Table 3 . The creation of the synthetic population draws on two sources, the German Farm Structure Survey and the IACS data   [1] for details). IACS data on land use for each plot are reported annually by the farmers to determine direct payments from the EU Common Agricultural Policy. The agency collecting the data offers public access to them, however without information on the farmer managing the plot, and aggregating single crop information mostly to group of crops. As the data are spatially explicit, further spatial data relevant for agricultural land use can be attached as reported in Table 3 . Note that except for the farm typology created by Kuhn and Schäfer [1] , most dataset are readily available in other German federal states as well.

Generation of frequency tables at municipality level
This section presents the development of frequency tables which contain the frequencies of different farm types and size classes at the LAU level. The underlying code in the programming language GAMS is provided in a software versioning system. 1 Official statistics provide frequency tables for farm type and size classes, total utilized agricultural land, and agricultural land differentiated by land use at NUTS 3 and higher level, only. At LAU level, only vectors on the frequency of different farm types and size classes are reported. Our approach estimates probable frequency tables at LAU level, drawing on the frequency tables at NUTS 3 level and the vectors at LAU level. We use solely data from IT NRW 2018 for the estimation ( Table 3 ). The constraints of the estimation framework relate to adding up conditions at LAU level. Let x s,c,t denote the unknown number of farms of a certain size class s and type of specialization t in each of the 396 LAU. c, d s,c and d t,c are the given data on the number of farms of a certain size class, respectively, type, and d c on the total number of farms. The following adding up conditions eq. (1) -(3) should hold for any estimated frequency table of the farming population at LAU level x s,c,t : After defining the adding up conditions on LAU level, we apply the same approach on NUTS 3 and 2 level as sometimes cells in frequency tables are left blank due to data protection rules. Let k denote county (NUTS 3, Kreis, 29 units) and r district (NUTS 2, Regierungsbezirk, 5 units) which are the two administrative units above LAU level where frequency tables on the number of farms by size class and type are available. Taking this additional information into account, we can add the following adding up conditions eq. (4) -(5) from LAU to NUTS 3 and from NUTS 3 to NUTS 2 to the estimation framework.
The estimation problem is defined as a highest posterior density problem (HPD). 2 We assume a-priori the relative, but unknown shares of the distribution by size class and type at LAU level are equal to the observed one at NUTS 3 level. Thus, s in the objective function ( eq. (6) ) denotes the shares describing the empirical distribution observed at NUTS 3 level: The resulting estimates for x are real numbers and not counts, as required for the frequency tables. In order to convert them into a distribution of integers, we introduce bounds around each estimated x s,t,c representing its floor and ceiling values. We next construct a new estimator where eq. (6) is replaced by an objective function which shifts the value towards an integer. To do that the estimator minimizes the squared difference between the estimates of x s,t,c and a number which is smaller than its lower bound if it is closer to the lower bound or higher than its upper bound otherwise. Any x s,t,c which is already an integer is automatically fixed as its floor and ceiling are identical. The additional estimation is repeated several times until all estimates x take on integer values

Generation of the farm population
Given the number of farms at LAU level in each category, we start to generate the spatially explicit farm population. The corresponding code, as well as the linkage of spatial data to the farm population is written in the programming language Node.js, and provided in a software versioning repository. 3 As stated previously, the analysis builds on the North Rhine-Westphalian farm typology published by Kuhn & Schäfer [1] . It differentiates farm types according to (1) type of farming, (2) size class in ha und (3) livestock density in livestock units (LU) per ha in different classes, and reports their numbers at the level of so-called soil-climate-regions (SCRs, generally consists of multiple NUTS 3 regions), which reflect zones of similar farming conditions. The typology reports for each farm type the number of farms as well as statistics (mean, median, standard deviation) on core farm characteristics, including among others farm size in ha, arable and grassland endowment, and livestock density in LU, in total and for the animal categories pigs, sows, dairy cows, and other cattle. Based on the frequency tables, a sampling approach generates a matching farm population, and attaches characteristics according to the information found in the farm typology to each farm. The information on size and livestock density in the farm typology refers to certain classes (e.g. 0-50 ha, or 0-1 LU), accordingly, a truncated normal distribution is assumed when drawing these characteristics for a hypothetical farm in a cell of the typology.
In order to determine the nonparametric skew ( S) of each farm characteristics, we calculate the skew using where μ is the populations mean, ν the populations median, and σ is the populations standard deviation for the given variable. Due to data protection, statistics on farm characteristic for some farm types has been blackened in the farm typology. In these cases, average values given the farms size cluster are assumed for the variables with missing distribution data. For instance, farms in a size cluster between 0 ha and 50 ha would be assigned to 25 ha. Detailed comments in the relevant code sections report further assumptions made in the sampling approach. Following this sampling approach, a spatially implicit farm population containing a list of farms with specific values for each farm characteristic is generated ( Fig. 1 ).
So far, the location of a farm can only be assigned at SCR level. To advance here, the farm frequency tables at LAU level described in the Section 2.1 are used. For each farm in the farm population, a farm at LAU level is matched, considering a) its farm type, b) its size class (e.g. 10-30 ha) and c) that the LAU falls in the SCR of the farm. Once a matching farm from the frequency tables is found, the LAU property from the match is added to the respective farm of the farm population. Thereby, the number of farms in the frequency table of this LAU that are not yet distributed is decreased.. The LAU frequency tables and the farm typology are not completely harmonized, even if they stem originally from the same raw data source. Therefore, few farms cannot be matched, as each farm in the frequency table is at SCR level only used once in the matching procedure. In these cases, a farm is chosen for which solely the SCR and size cluster matches.

Linking spatial data to the farm population
Once each farm is assigned to a LAU, specific farm locations are designated to the farms of the generated farm population. Farm locations are assumed to be a random vertex from an arbitrarily chosen field (polygon) within the boundaries of the LAU the farm belongs to. If more than 50% of the farm's land endowment is arable land, only arable plots are considered for the farm location. Respectively, if more than 50% of the farms land endowment is grassland, only grassland plots are considered. If a vertex has already been defined as a farm location, the algorithm is recursively called until a new, unused farm location is found.
In a final stage of the generation of the spatially explicit farm population, individual plots are assigned to the farms. The plots in the federal state are shuffled to guarantee a random order, and afterwards evaluated for their suitability for a given farm. The assignment procedure generally differentiates between grassland and arable plots.
For grassland plots, farms that are within a 30 km driving radius around the plot are filtered and sorted by distance in ascending order. To maximize the efficiency of the algorithm, filtering of the farms is done using a spatial index based on a flat k-d tree as proposed by Bentley [5] . Subsequent to the farm filtering, the closest farm is searched for where the sum of the current plot and the current grassland endowment of the farm does not exceed the aspired grassland endowment of the farm (including a buffer of 5%). Also, a check is incorporated prohibiting a farm to exceed its farm size cluster. If a matching farm is found, the current plot is added to the farm, otherwise the plot is added to a list of unused plots.
For arable plots, farms within close proximity (5 km) are filtered and sorted based on their suitability for the given crop cultivated on the current plot. This is done to increase the probability of e.g. a specialized cereal producing farm to obtain plots cultivated with cereals. Again, a farm is searched for where the sum of the current plot and the current arable land endowment of the farm does not exceed the aspired arable land endowment of the farm (including a buffer of 5%). In addition to the check prohibiting the farm to exceed its farm size cluster, another check is incorporated prohibiting farms above 10 ha (the threshold where the EU Greening obligation becomes binding) to exceed certain crop shares. In case no suitable farm is found within 5 km for the given plot, the radius is increased to 30 km. Also, the sorting is solely based on the farm to field distance. If a matching farm is found, the current plot is added to the farm, otherwise the plot is added to the list of unused plots.
Farms that are within 95% of their aspired farm size are labelled as finished and removed from the list of farms considered in the evaluation of the following plots. After the first round of the assignment procedure, the list of unused plots is iterated over again.
In this second round, the plots are assigned to the most proximate farm as long as adding the plot to the farm does not exceed the farms overall aspired farm size. Here, the aspired arable and grassland properties are ignored, allowing for a deviation of these values in case no sufficient arable or grassland is available in the region of the farm.
Using the approach outlined in this section, matching the approx. 70 0.0 0 0 plots and 25.500 farms in the federal state of North Rhine-Westphalia takes less than 10 min.

Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article.
were financially supported by the Ministry of Culture and Science within the framework of the NRW Strategieprojekt BioSc (No. 313/323-40 0-0 02 13 ).


Local Action Group - a pillar of the development of the Romanian village

The Local Action Groups have the capacity to act in a complex way, so as to cover the entire economic and social issues of the regions where they act. The actions carried out by the LAGs have direct consequences on rural development by encouraging those projects that develop alternative solutions in obtaining revenues; diversification of the market for products and services; ensuring sustainable development, by protecting the environment and ensuring the security and well-being of life. This article makes an analysis of the impact of European funding in supporting the development of the rural environment of the area of influence of a Local Action Group in Olt County. It describes the situation of European funding at LAG level and the proposal of development directions that need to be considered in the future: increasing competitiveness in agricultural activities, sustainable use of resources and balanced development of economic activities and local communities in rural areas.

Introduction
In the European Union about 90% of its territory is found in rural areas, also living here more than half of the European population. The Rural Development Program has been a very important component of the Common Agricultural Policy, promoting the sustainable development of rural areas in Europe and addressing socio-economic and environmental issues. [8].
In recent years, sustainable rural development has become a hot topic in rural areas due to the complex interactions between natural resources, agricultural production and local communities. [5,12].
The Common Agricultural Policy (CAP) is a partnership between agriculture and society, between Europe and its farmers. The CAP aims to: support farmers and improve agricultural productivity; to protect farmers in the European Union so that they can ensure a decent living; to contribute to the fight against climate change and the sustainable management of natural resources; to preserve rural areas throughout its territory; maintain the competitiveness of the rural economy by promoting jobs in the agricultural sector and its upstream and downstream sectors [13,1].
The CAP is a common ground for all EU member states. It is coordinated and funded at Union level from the EU budget. The financing of agricultural policies is based on two funds, namely: for market measures, payments are made through the European Agricultural Guarantee Fund (EAGF) and rural development programs are carried out through funding provided by the European Agricultural Fund for Rural Development ( EAFRD) [9].
Through the Rural Development Fund, the EU aimed to support Member States in developing rural areas in order to reduce existing socio-economic gaps [6]. This fund is the second pillar of the CAP, the first pillar being the one that grants direct payments to farmers. [7].
Under the development pillar, the LEADER Axis is applied to all Member States through the National Rural Development Programs.
Through these national rural development programs, each EU Member State attracts the financial resources allocated by the EU for agriculture and rural development, which is structured in priority axes, each addressing a specialized area of intervention. The LEADER program is Axis 4 of the RDP, but differs from it in its specific approach. [14,15].
Since the advent of Axis 4 (LEADER axis), whose name was chosen from the expression that characterizes it: "Links between rural development actions", it has had an innovative vision on the implementation of rural development policies, namely : involvement of local partners in decision-making, in order to establish the steps to be taken for the future development of the area in which they live. [3,16]. Subsequently, the LEADER axis brought a number of positive changes in rural areas and played an innovative role in solving problems in rural space [4].
The LEADER Axis aims to offer rural residents the opportunity to take the initiative and get proactively involved in implementing and then implementing rural development strategies at the local level to increase the quality of life and standard of living.. [11,2,10].

Methods
As working methodology, the method of direct observation, comparative analysis and case study conducted within the Local Action Group "Vedea -Gavanu -Burdea" was used. This article aims to study the existing opportunities within the NRDP at the level of Romania, regarding the second pillar: "rural development", as a support element for increasing the economic competitiveness of rural areas.

Results and Discussions
Existing rural development programs in all EU Member States pursue actions and budget allocation for the development of their specific territories. In this way, European policy for rural development aims to provide support for the implementation of rural development actions at local, regional or even national level.
Each EU Member State receives a budget allocation through the National Rural Development Programs of each Member State, in order to implement projects and investments that contribute to the implementation of rural development objectives in that territory.
Within the RDP, the LEADER axis was encouraged in order to increase the development potential of the rural area, by capitalizing on local initiatives, by the possibility of acquiring knowledge on local development and by popularizing them in other rural areas. The creation of local associations, under the name of "Local Action Groups (LAGs)" is an original feature of the LEADER approach.
This partnership applies SWOT analysis to identify the key elements that will be included in the future development strategy, to which it will allocate the available budget. Thus, the LAG has the task of managing the financial resources in order to guarantee the development of the territory it represents.
In the current programming period 2014-2020, the LAGs could be set up through submeasure 19.1 -which provided support for the preparation of public-private partnerships in order to develop local development strategies -which was launched in 2015. The budget allocated to this sub-measure was 2.4 million euros and was used in a proportion of 99.1% (2,379 million euros). 179 projects were submitted, of which 175 were declared eligible and selected for funding. In order to follow the way in which the LEADER Program was implemented, how a LAG is established and operates, as well as the impact that this public-private partnership has on the life of the rural community, we chose a case study, relevant for the analyzed region: LAG "Vedea -Gavanu -Burdea".
The Local Action Group "Vedea -Găvanu -Burdea" was established in the programming period 2007-2013 and even if it benefited from LEADER funding to implement a series of projects at the territorial level, the local development capacity reached did not respond to local needs and consequently the activity of this LAG continued for the 2014-2020 programming period.
The difficulties that the LAG territory encountered were: the aging trend of the population, the lack of employment opportunities, the large discrepancy with the degree of development of urban areas, the degree of fragmentation of small farmers' lands and their lack of financing for penetration to the market, the lack of agricultural equipment and the moral wear and tear of the existing ones, the lack of specialized agricultural knowledge among farmers, the existence of different marginalized communities or at risk of poverty.
Taking into account the recommended indices on component A, respectively 985.37 Euro / km² and 19.84 Euro / inhabitant, on the one hand, and the area and population of the LAG "Vedea -Găvanu -Burdea", on the other hand, the allocation allocated to this LAG was 2,317,360 euros.
The value related to the quality level (Component B) obtained following the evaluation and selection process was 679,550.31 euros.
Subsequently, following the supplementation of the SDL financial allocation, following the approval of the Report on the result of the SDL evaluation, the financial allocation of the LAG was increased by 128,759.03 euros. One of the main advantages of a local action group in a region is to focus, together with the actors that make it up, on the problems of that territory and to be able to make guides of the proposed measures in order to solve the identified needs. in the territory of the LAG.
For the correct identification of the needs of the members of the LAG "Vedea -Găvanu -Burdea" it is necessary an assessment at the level of the community within the LAG, of the economic agents in this region, of the local public administration, as well as of the active members of the LAG existing.
The community has an important role in determining the needs, but also the strategic directions of local development of micro-regions, as only those who live here can identify these needs because they face them daily.
Following the consultations carried out by the Local Action Group "Vedea -Găvanu -Burdea" with all important actors in the territory, based on the diagnostic analysis and SWOT analysis, were identified the objectives, priorities, areas of intervention and measures that were introduced in within the LAG's Local Development Strategy.
The "Vedea -Găvanu -Burdea" LAG chose to grant funding for 3 priorities, aiming at no less than 9 measures, allocating approximately 39.7 euro / inhabitant, respectively 2,304.68 euro / km 2 .  The local development strategy of the Local Action Group "Vedea -Găvanu -Burdea" through the measures introduced in it, contributes to the mission of the Europe 2020 Strategy, by promoting sustainable rural development within the LAG territory, obtaining a more balanced agricultural sector in terms of territorial and ecological, more beneficial for the climate, more resilient, more competitive and more innovative.

Conclusions
Rural development is a very important issue in the European Union and is currently promoted by the EAFRD.
The role of LAGs is a major one in improving rural development, with regard to increasing the competitiveness of agriculture and forestry, the natural and rural environment, as well as the quality of life and the management of economic activities in rural areas.
Under these conditions, the LAGs, through the local development strategies they implement, offer an integrated management solution, which is related to the following objectives: diversification of traditional agricultural activities; the multifunctionality of agriculture in terms of landscape use and the improvement of cultural and historical heritage; improving food security and promoting local food; involving the local community in preserving social and cultural traditions; employment and income generation in agriculture.
The LEADER program represents for the territory the Local Action Group "Vedea -Găvanu -Burdea" not only a set of measures that are implemented through the Local Development Strategy, but is a method of mobilization and promotion of the development of the territory and local communities living here , a tool that works efficiently by adapting decisions on the rural environment to the needs identified within the territory.


Russian Society's Civilizational Transit

This paper deals with the issue of specific civilizational process formation in Russia as a new figuration and configuration of the forms of modernity. It is argued that explanations of the dynamics of Russian society are often based on the idea of predestination of the trajectory of its development (path dependency) that is caused either by the universal logic of modernization and globalization or by persistence of the basic cultural program or matrix which is reproduced in social practices. A civilizational transit of Russian society phenomena is discussed within multiple modernities theory as a new perspective that allows overcoming some of the problems with the predominant approaches to Russian modernization. Civilizational approaches to analyses of Russian culture, politics and society have been associated mostly with theories of ‘historical cycles’ worked out in late nineteenth century and the first half of the twentieth century. More recently a considerable attention has been devoted to different versions of ‘neoEurasianism’. In addition, the notion of ‘clash of civilizations’ proposed by Samuel Huntington became prominent in the political discourse in Russia at the end of 1990s. At the same time, the concept of civilization is discussed widely in today’s historical sociology. A specific school of ‘civilizational analysis’ that draws on the ideas of Shmuel Eisenstadt emerged at the end of the 1990s. This perspective has already influenced some new trends in sociology of religion, political sociology and international relations theory. The panel seeks to demonstrate the relevance of a new wave of civilizational analysis for understanding contemporary Russian culture, politics and society.

Introduction
The turn held in the last quarter of the XX-beginning of XXI century to the analysis of the development of civilization in world history and modern societies denotes a continual breakaway from classical schemes and designs disciplinary socio-humanitarian knowl- and humanities. Socio-humanitarian knowledge lags behind in understanding the complexities of identity, crises and conflicts of modern life at both the regional and global levels.
Firstly, a broad palette and disciplinary nature of social knowledge turned out to be very hard to explain the limiter problems that go beyond the scientific field of a particular discipline. Progress in the emergence and strengthening of a plurality of paradigms and approaches in the social sciences was temporary, and brought another Second, the pursuit of social scientists to give a universal explanation of the sociocultural processes on the basis of allocation of key concepts, such as the widely understood social structure, culture, institutions, and networks sets a fairly narrow corridor in the description of communities and individual fragments of changing social reality. The principle of multilevel (from macro to micro level) in the social sciences allows only opportunistic remove of contradictions and gaps in understanding the mechanisms and the determination of large-scale change, regional and global transformations, for example, modernization of individual societies.
In other words, the social sciences and humanities were unable to progress profound changes both within individual countries and in the planetary scale. This syndrome is lagging socio-humanitarian knowledge and it can hardly be considered as overcome at the beginning of XXI century. Neither mono-or multiparadigm of the social sciences development, no their disciplinary progress has led to a satisfactory explanatory and predictive potential.
Of course, social sciences and humanities have been an essential element of a holistic societal world and national process. However, their contribution to the economy and management of, various industries at local and regional level was significantly lower in comparison with natural, technical sciences and health sciences. Utility and professional suitability as the socio-humanitarian knowledge and expert reviews in the field of Information Sciences are constantly exposed to criticism and revision. Since the end of the twentieth century, crisis of the social sciences, their weakness and peripherals

Methodology and Methods
An example of the use of the civilizational approach is the study of the transformation of social inequalities in the context of multiple modernities based on the concept of Norbert Elias [17]. Civilizational approach of Norbert Elias [17], S. Eisenstadt and the concept of multiple modernities by J. Arnason [1], P. Wagner [8],  There is a need for a world language, world economy, world government, world religion, international law, the world of science and the world of art, etc. The internal space of national cultures flows into the environment where global and local culture co-exists.

Results / Findings and Discussion
We can conclude that the imminent modernization, stagnation and loss of traditional forms of Russian culture and civilization emerge in the beginning of the XXI century [9].
Quasi-natural changes in the socio-cultural and civilizational development of Russian society can hardly elude the global trend [11,12]. The autonomous existence of sociocultural types ended with the destruction of the national government, political, and economic boundaries. Economic, financial, migration flows in different regions of the world are the direct evidence of folding a new civilizational order based on mixing and coexistence of different cultures and forms of civilization [16]. In particular, the observed types of regional and global integration demonstrate the dynamic cultural and civilizational exchange.
The emerging global space of human resources (migration, outsourcing, and transnational networks) in business, politics, science and education can be considered as formation of a new socio-cultural and civilizational shift map of the Russian society [13][14][15]. The main task is to study these new phenomena.
Modern societies are in the grip of permanent transformation, continuous innovation and undulating crises. These facts were noted PA Sorokin in the concept of social and cultural dynamics [18]. Globalization, post-modernization and virtualization recede into the background, because they only aggravate the frantic search for answers to situational and strategic problems [8]. The global nature of the economic and financial turmoil dramatically increases the cost and risks of taking the political and economic decisions. There has been an obvious failure of civilization as well as quite stable socio-cultural matrix of modern society such as 'risk' , 'second modernity' , 'consumption' , 'knowledge' and 'creative actions' to social order new forms.
There is a powerful transformation of the civilized world order, which will draw into it the largest possible number of participants. The majority of people faces different kinds pf transformations such as ecstasy of omnipotence and impotence of political power, fascination with the power of transnational networks and the weakness in the economy and culture, the magic of personal will and lack of will, faith and fanaticism of unbelief.
Modern Russia faces all these problems.
Some scholars write about overweighed interpretation of civilization. [3,4] Meanwhile, Ferguson [10] develops an understanding of civilization as (trans) forming process based on its own socio-cultural grounds. Features of cultural and spiritual situation manifest in the rate, and the nature of the structural change. Political and economic changes in the Russian society is hampered by mental structures, legal and value systems, patterns of behavior and ideologies. In general, the whole range of cultural and spiritual practices accumulated in the Russian society and the authorities, significantly modifies the social structures, institutions, and actions and gives national and regional specificities of social life, economy, law and government.

Conclusions
The research has shown that the turn held in the last quarter of the XX-beginning of XXI century to the analysis of the development of civilization in world history and modern societies denotes a continual breakaway from classical schemes and designs disciplinary socio-humanitarian knowledge. Linear, structuralized, constructivists, phenomenological focus on the construction of certain important aspects of the social changes of different levels are one-sided and even false in their explanation and unproductive in the projections.
There is a powerful transformation of the civilized world order, which will draw into it the largest possible number of participants. The majority of people faces different kinds pf transformations such as ecstasy of omnipotence and impotence of political power, fascination with the power of transnational networks and the weakness in the economy and culture, the magic of personal will and lack of will, faith and fanaticism of unbelief.
Modern Russia faces all these problems.
In Russia the evolution of civilization is based on co-existing or competitive contemporary forms of sociality and culture. Adequate diagnosis sociological modes of modern Russian society civilized order and assessments of various social, economic and cultural practices is possible only in the broader context of world civilization sociocultural processes.

Funding
The paper was funded by RFBR according to the research project No.18-011-01254.


s2fieldsofstudy: Economics Business

text:
The Role of IILM in The Liquidity Risk Management of Islamic Banking Institutions

Efficient and effective liquidity management is vital to Islamic banking institutions to prevent banks from running the risk of inability to meet deposit demands. However, banking institutions face a dilemma if they have excess liquidity funds but do not invest them in generating more profits. Consequently, establishing the International Islamic Liquidity Management Corporation (IILM) became necessary to address liquidity issues in Islamic banking institutions. Yet, there is still some ambiguity about the role played by IILM. Thus, this study aims to examine the role of IILM in the liquidity risk management of Islamic banking institutions. This qualitative study using a content analysis approach, while the data analysis method is descriptive. The study found that IILM plays a role in developing short-term liquidity instruments and acquiring the underlying assets. IILM also ensures that the instruments issued for Islamic banking liquidity risk management must comply with Shariah principles and support the development of Islamic liquidity management infrastructure by encouraging more efficient cross-border investment flows globally. IILM plays an essential role in improving liquidity risk management by issuing short-term sukuk instruments that benefit Islamic banking institutions.

Introduction
Liquidity is one of the crucial aspects in the management of banking institutions, either Islamic or conventional banking. Efficient and effective liquidity management will prevent the occurrence of liquidity risks that affect the management and performance of a banking institution due to the bank's inability to manage funds properly. The failure to manage funds properly will have a detrimental effect on the Islamic finance industry.
The establishment of the Islamic Liquidity Management Corporation (IILM) is an initiative taken by the regulatory authorities to further strengthen the development of Islamic liquidity management infrastructure in addressing the key issue Islamic financial institutions face, namely the lack of Shariah-compliant short -term liquidity instruments. The existence of IILM can assist Islamic banking institutions in managing their liquidity risk more efficiently. In addition, cross-border liquidity management is also improved through the issuance of high quality, liquid and Shariah-compliant IILM sukuk instruments (Abdullah, 2010;Archer & Rifaat, 2014;Bank Negara Malaysia, 2010a;Bello et al., 2017).
IILM has successfully issued its first short-term sukuk in August 2013 with a three-month maturity of USD 490 million. The amount continued to increase to US dollars 3 billon in 2016, indicating an increase in demand for IILM sukuk domestically and globally. This is also due to the increase in collateral assets for IILM with the increase in participating investors and allows IILM to increase the size of its sukuk issuance (Securities Commission Malaysia, 2020).
One of the typical known roles played by IILM is focusing on the issuance of investment instruments for liquidity management, for example IILM only receives excess liquidity funds from Islamic banking institutions who wish to subscribe for IILM sukuk issued for investment purposes. Due to the limited information and knowledge regarding IILM -issued sukuk, Islamic banking institutions in Indonesia, in particular, have not made IILM sukuk as their bank investment instrument (Nadia & Rifki, 2018). Thus, this study will discuss the role of the International Islamic Liquidity Management (IILM) in improving the liquidity risk management of Islamic banking institutions.

The Background of IILM
The International Islamic Liquidity Management is a supranational organization established globally to further strengthen Islamic liquidity management infrastructure development in the face of the global financial crisis. The idea of establishing IILM arose due to the discussions of the High-Level Task Force on Liquidity Management in March 2009. The team was formed by the Islamic Financial Services Board Council (IFSB) (Bank Negara Malaysia, 2010a; Archer & Rifaat, 2014).
As an international organization, IILM enjoys several privileges and grants immunity in its governance through an act passed by the Parliament of Malaysia, namely the International Islamic Liquidity Management Corporation Act 2011. The corporation is based in Malaysia and is headquartered in downtown Kuala Lumpur. The establishment of IILM aims to facilitate cross-border liquidity management among IIFS by offering a range of new Shariah-compliant and globally adopted instruments. IILM was also established to foster cooperation among the member countries involved to build a viable Islamic liquidity management infrastructure at the regional and international levels (Archer & Rifaat, 2014;Bank Negara Malaysia, 2010a;Muljawan et al., 2014).
IILM successfully issued its first sukuk on 26 August 2013 worth USD 490 million with a maturity of three months and backed by the sovereign assets of the member countries involved. In fact, this sukuk has also been rated as A-1 by Standard and Poor's Rating Services and fully subscribed by its network of major distributors including two Islamic financial institutions from Malaysia namely Maybank Islamic Berhad and CIMB Islamic Bank Berhad (Archer & Rifaat, 2014;Muljawan et al., 2014;Rizkiah, 2018). With the existence of short -term sukuk IILM is able to complement the existing medium and long -term sukuk in the market (IILM, 2015).
Over the past few years, the size of IILM's sukuk issuance programs in the market has increased from USD490 million to USD3.51 billion today for each of its issuance programs. This is driven by increasing market demand for high quality assets from Islamic financial institutions. This increase indicates the encouraging performance of IILM and the increase in the size of this amount is also due to the increase in collateral assets acquired for the issuance of IILM sukuk. IILM continues to play its role by issuing short -term liquidity instruments in the market to meet the needs of Islamic banking institutions in managing their excess liquidity. For 2019 alone, IILM sukuk issuances reached USD9 billion through its series of issuances for different maturities according to market demand and these sukuk issuances accounted for 26 per cent of the total global sukuk issuances in 2019 (IILM, 2020).
During the seven years of IILM's involvement in issuing short -term sukuk to meet IIFS's liquidity requirements, IILM has been selected to receive several awards domestically and internationally such as the Islamic Deal of the Year award on 29 May 2014 (The Asset) Outstanding Contribution to the Development of Islamic Liquidity Management Markets on 3 June 2015 (London Sukuk Summit Awards) and Best Cross-Border Sukuk Liquidity Provider on 24 May 2016 (The Asset). IILM has also successfully recorded 99 sukuk issuances since the inaugural issuance of IILM sukuk was issued in 2013 with a cumulative total of USD52 billion. These sukuk are issued with maturities of two weeks to six months to meet the short-term liquidity needs of Islamic banks. IILM sukuk can also be traded in the international market through platforms based in Asia, Europe and the Middle East such as the Gulf Cooperation Council (GCC) involving countries such as Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates (IILM, 2020).

The Role of IILM in Enhancing the Efficiency of Liquid Risk Management of Islamic Banking Institutions
International Islamic Liquidity Management Corporation Act 2011 defines IILM as a supranational organization established to improve liquidity risk management among institutions offering Islamic financial services (IIFS) especially Islamic banking institutions with the objective to: (a) facilitate cross -border liquidity management among IIFS by providing various Shariah -compliant liquidity instruments to meet IIFS liquidity requirements and (b) fostering regional and international cooperation more closely in supporting the development of a sound liquidity management infrastructure at the domestic, regional and international levels.
To achieve these objectives, IILM needs to play its role to address liquidity issues that arise among IIFS, especially Islamic banking institutions, through the issuance of new Shariah-compliant liquidity instruments. The developed instruments will be used by Islamic banks to improve their liquidity risk management at the domestic, regional and international levels. This means that the prominent role of IILM in enhancing the liquidity risk management of Islamic banking institutions is as follows:

The Role of IILM in Developing New Shariah-Compliant Liquidity Instruments
The role of IILM in developing shariah-compliant instruments can be divided into three, namely: (a) the role in creating short-term instruments; (b) the role of IILM in obtaining collateral assets; (c) the role in ensuring that the instruments issued comply with Shariah principles. a. The role in creating short -term instruments Short -term liquidity instruments are financial instruments traded in the money market that aim to increase the liquidity level of banking institutions. These financial instruments have a short maturity of between three months or less than one year. Usually, these instruments are easy to redeem, and their value does not change much when it reaches its maturity. Conventional banking institutions usually manage liquidity needs by using short-term debt securities such as treasury bills, commercial papers and banker's acceptance (Abdul Rahman, 1999).
These treasury bills are short-term debt securities issued by the government of a country; for example in the United States the maturity of these instruments is 30 days and 90 days. These instruments are widely used for liquidity management and they are sold at a discount and redeemed at face value. These US treasury bills are also known as less risky instruments. However, these instruments cannot be used by Islamic banking institutions for liquidity management because there is an element of riba which is prohibited by shariah (Abdul Rahman, 1999).
As these US treasury bills do not comply with Shariah principles, IILM has taken the initiative by creating short-term sukuk to meet the liquidity needs of Islamic banks at the domestic, regional and international levels (Najibah & Kamaruzaman, 2018a). IILM has also played a key role in addressing the issue of lack of liquidity instruments in the Islamic money market, by taking the approach of creating new liquidity instruments that can help Islamic banking institutions address the excess liquidity they face. With the issuance of this IILM sukuk, further diversification of shariah compliant short term liquidity instruments in the Islamic money market. This gives an advantage to Islamic banking institutions because they have a variety of options in choosing the appropriate liquidity instruments for the investment of their liquidity funds thus being able to generate profits at the maximum level.
For this short term sukuk issuance, IILM uses Asset Backed Commercial Paper (ABCP) as a model in the structuring of IILM sukuk issuance. In western European and American countries, ABCP is a short-term financial instrument with maturities ranging from 90 days to 270 days used to manage liquidity. Usually, these instruments are issued by banks or finance companies based on the institution's physical assets and used for short-term financing purposes, but these instruments are not widely used by countries that offer Islamic banking systems (Archer & Rifaat, 2014).
Although IILM as an institution that issues sukuk is not rated by any credit rating agency, the short-term sukuk program issued by IILM is rated and given an A-1 rating by Standard and Poor's Rating Services. IILM issues sukuk with maturities of less than one year as IILM only offers short -term liquidity instruments for liquidity management among IIFS. This coincides with the fulfilment objective of IILM to meet the liquidity needs of IIFS, especially Islamic banking institutions internationally (Archer & Rifaat, 2014).
IILM Sukuk is issued every three months to ensure that IILM receives deposits at low market prices from investors and IILM does not have to pay a profit rate to investors with high value. In addition, with the issuance of short -term sukuk, it will be easier for Islamic banking institutions to invest in short -term financial instruments, thus generating maximum profits. This is because, one of the main sources of banks obtaining their liquidity funds is through deposits from customers who only have a short period of time for the bank to carry out investment activities (Najibah & Kamaruzaman, 2018b). b. The role of IILM in obtaining collateral assets The underlying asset is one of the important elements in the structuring of a sukuk. This is because sukuk is a financial instrument based on sharia compliant assets and the profit yield of sukuk issued depends on the underlying asset. In the IILM sukuk issuance process, the underlying assets used must be 51 percent tangible assets such as buildings, land, etc. and another 49 percent are non-tangible assets, for example assets that cannot be clearly seen but have value such as copyright, financial assets etc.
Based on the provisions of Article 5 Agreement, among the roles of IILM is to collect assets from each shareholder or central bank of member countries participating in IILM including the Islamic Corporation for the Development of the Private Sector which is a multilateral development institution. As for the assets involved not only from the assets owned by the central bank but it also involves the assets of the country (Najibah & Kamaruzaman, 2018a).
The assets that have been collected are placed in a pool of assets that is accountable to the SPV appointed by IILM, namely BNP Paribas Securities and Services. Then the assets involved are valued as a whole and the amount obtained is used as the underlying asset for the issuance of IILM sukuk on a regular basis. The governing board of IILM has stipulated that the underlying assets for sukuk issuance must be sovereign or quasi sovereign assets of member states and supranational organizations that are shareholders of IILM (Archer & Rifaat, 2014;Bello et al., 2017).
The underlying assets must also comply with the principles outlined by sharia, this means that assets used to carry out Shariah non -compliant activities cannot be used as collateral assets for the issuance of sukuk. Close cooperation and commitment between the central banks of member countries is crucial for the issuance of sukuk, as each series of sukuk to be issued depends on the underlying assets received by IILM shareholders (Archer & Rifaat, 2014;Muljawan et al., 2014;Najibah & Kamaruzaman, 2018a).
As the issue raised by Rizkiah (2018) who stated that sukuk issued by IILM faces Shariah issues related to the underlying assets used for sukuk issuance. This is because the investor does not know clearly about the asset, this situation leads to the occurrence of an element of gharar (uncertainty) in the ijārah contract. Whereas in the ijārah contract, it is stated that among the asset conditions that must be complied with is that the ijārah asset must be clearly defined. If the conditions regarding the ijārah asset cannot be resolved, let alone the ownership status of the asset that should have belonged to the sukuk holder will also not be implemented.
However, based on a study conducted by Archer and Rifaat (2014); Muljawan et al (2014) as well as Najibah and Kamaruzaman (2018b), clearly state that IILM uses a wakālah contract structure in its short term sukuk issuance process. This is because, according to Archer and Rifaat (2014) the concept of wakālah applied in the process of issuing IILM sukuk involves two SPVs who act as representatives in performing their respective duties. BNP Paribas Securities and Services which is an SPV appointed as a representative in holding the pool of assets collected by IILM. While IILM will also act as a representative to issue sukuk that will provide cash flow to investors.
According to Najibah and Kamaruzaman (2018b) the use of wakālah contracts in the structuring of IILM sukuk is also the best way that will further facilitate the issuance of IILM sukuk. This is because wakālah contracts can combine the use of tangible and non-tangible assets for the issuance of sukuk and to resolve the issue of shortage of underlying assets faced by IILM. The wakālah contract is seen as more flexible than the ijārah contract because in the ijārah contract, the assets used must be assets or goods that are eligible for rent only. Furthermore, the use of wakālah contracts will reduce the reliance on tangible assets for the issuance of IILM sukuk.
Sukuk holders or investors generally do not want the underlying asset, instead, investors only look at the profit that will be obtained from the underlying asset. At the same time investors need more cash flow to meet their short-term liquidity. The sukuk issuer, for example IILM or the original owner of the asset (asset contributing countries), rationally does not intend to sell or hand over its assets to the sukuk holder, instead the sukuk issuer only agrees to give temporary ownership to the sukuk holder by taking advantage or profit from the underlying asset (Islamic Interbank Money Market, n.d.).
Apart from the responsibility of procuring assets, IILM also needs to identify assets contributed by member countries with high ratings as outlined by Basel III and IILM itself. This is to ensure that the sukuk instruments issued by IILM are of high quality and liquid. The underlying assets will be held by the SPV until maturity and will not be traded based on the agreement reached by both parties (Archer & Rifaat, 2014;Najibah & Kamaruzaman, 2018a). Currently, IILM can issue highquality sukuk and has been rated A-1 by Standard and Poor's Rating Services. The underlying assets for IILM issued sukuk also meet the high quality liquid asset standards (HQLA) set by Basel III to meet the liquidity risk management of Islamic banking institutions. The role that IILM has played can also help Islamic banking institutions to address the challenges in meeting the liquidity requirements provided in Basel III. c. The role in ensuring that the instruments issued comply with Shariah principles Most of the Islamic financial institutions globally faced liquidity risk management issues due to the lack of shariah -compliant short -term liquidity instruments in the market. This causes most Islamic banking institutions to have a large cash surplus and the holding assets of the institutions are not profitable and get low returns (Bank Negara Malaysia, 2010a; Raudhah & Abdul Ghafar, 2013;Ramadhan & Nasir, 2014).
Thus, IILM seeks to improve the liquidity management of Islamic banking institutions through the issuance of Shariah-compliant short-term sukuk instruments to help Islamic banks overcome their liquidity risk. Profit generation through the fund's investment in IILM sukuk also has the potential to provide high returns, as IILM-issued sukuk is rated A-1 and complies with widely adopted Shariah principles thus improving the efficiency of cross-border liquidity management between banking and Islamic financial institutions at the international level.
Sukuk issued by IILM does not follow any guidelines including the guidelines provided by AAOFI, this is because IILM has its own Shariah Advisory Committee that will monitor and ensure that the principles and processes of issuing IILM sukuk comply with Shariah requirements. (Bank Negara Malaysia, 2010a;Najibah & Kamaruzaman, 2018a;Ramadhan & Nasir, 2014). However, this does not mean that IILM sukuk cannot be widely marketed globally because it does not follow AAOFI guidelines. This is because some member countries do not adopt the AAOFI guidelines, such as Qatar and Indonesia. Shariah Advisers who evaluate IILM sukuk are also appointed by IILM's own shareholders comprising of qualified and extensively experienced Islamic financial experts from member countries such as Saudi Arabia, Malaysia, Nigeria and Kuwait (Najibah & Kamaruzaman, 2018a).
In performing its role in developing short -term liquidity instruments, IILM also ensures that instruments issued for Islamic banking liquidity risk management must comply with Shariah principles. By carrying out this role, Shariah issues that are disputed by Islamic scholars in search instruments such as the concepts of bayͨ al-dayn and bayͨ al-ͨ inah can be addressed by IILM. This is because the structuring of sukuk issued by IILM is based on the concept of wakālah which is widely adopted globally.
The concept of wakālah is an agency contract that involves one party giving a mandate to another party as a representative to perform certain tasks. The application of the wakālah principle used in this sukuk involves two special purpose vehicles (SPV) which makes IILM act as SPV in issuing sukuk and also a representative in collecting the underlying assets as well as BNP Paribas Security Services appointed by IILM as the representative entrusted to hold the assets (Archer & Rifaat, 2014;Bello et al., 2017;Ramadhan & Nasir, 2014).

The Role of IILM in Developing Islamic Liquidity Management Infrastructure
Islamic banking institutions need shariah -compliant liquidity instruments to manage their liquidity by generating income through investments in short -term instruments for profit. Among the liquidity instruments that Islamic banks often use for liquidity risk management such as commodity murābahah, wakālah investment, sukuk ijārah or salām and so on. However, there are constraints faced by Islamic banking institutions in generating short -term liquidity funds as a result of the lack of shariah -compliant short -term liquidity instruments in the market. Existing liquidity instruments also face acceptance problems in the international Islamic financial market as interbank transactions are relatively limited. Futhermore, Shariah instruments introduced also lack uniformity and differ between countries (Alfisyahrin, 2014;Rizkiah, 2018).
Therefore, the short-term sukuk offering from IILM can assist Islamic banking institutions at the domestic, regional and international levels in improving their liquidity risk management more efficiently and effectively. In addition to IILM's role in developing shariah-compliant liquidity instruments and meeting the liquidity needs of Islamic banking institutions, IILM also supports the development of Islamic liquidity management infrastructure by promoting more efficient crossborder investment flows globally. This is because IILM-issued sukuk uses the wakālah principle in its structuring and is adopted internationally especially in the Middle East and is dominated by the US dollar which further facilitates transactions between Islamic banks (Bank Negara Malaysia, 2010a; Muljawan et al., 2014;Rizkiah, 2018).
IILM plays a vital role in developing the Islamic liquidity management infrastructure. At the same time IILM also activates the Islamic money market by offering shariah -compliant short -term liquidity instruments that can be traded in the international money market. The initiative taken by IILM can resolve the issue of inactive Islamic money market for Islamic banking institutions such as in Sudan for example or other countries where their Islamic money market is inactive. Furthermore, this initiative also helps create a specific Islamic liquidity management platform for countries like Nigeria where Islamic banking institutions do not have a platform to manage their bank liquidity effectively. The existence of a globally marketable offering of shariah -compliant liquidity instruments will facilitate cross -border Islamic liquidity management and provide opportunities for investors to conduct sukuk transactions between Islamic banking institutions from various countries.
Over the past few years, the amount size for the IILM sukuk issuance program has shown an increase with amounts starting from USD 490 million in 2013 increasing to USD 3.51 billion in September 2020 with sukuk maturity offerings varying from one month, three months and six months. As a result of the increase in the size of the amount to meet the liquidity needs of Islamic banks, IILM has successfully increased the number of participating investors. The major distributor network for IILM sukuk has successfully activated the secondary market by conducting sukuk buying and selling transactions to other investors interested in investing, with a turnover of around USD 212 million in the first quarter of 2020. Despite the current worsening economic situation with the fall in world oil prices in the first quarter of 2020, IILM remains committed to offering its services by reissuing additional subscriptions of USD 1.6 billion due to the encouraging response from investors (IILM, 2020).
Furthermore, in accordance with the provisions in Article 5 of the Agreement, IILM also provides advisory services and technical support in the development and management of Islamic liquidity among Islamic banking institutions and member countries. This will help create a conducive environment to foster the integration of regional and global Islamic financial markets (Bank Negara Malaysia, 2010b). In addition, IILM has also organized seminars and discussions with international financial agencies to obtain views and solutions on liquidity management issues at the global level. In 2013 and 2014, IILM with Thomson Reuters organized two roundtable discussions on Shariah issues and IILM also collaborated with international regulatory bodies such as the IFSB and IIFM in organizing seminars on Islamic capital markets on 13 November 2018 at Abu Dhabi. The seminar was organized with the objective to strengthen further the economic development and resilience of the Islamic financial sector at the international level (IFSB, 2018).

Conclusion
To conclude, the role that IILM has played in improving the liquidity risk management of Islamic banking institutions can indirectly resolve the issue of lack of short -term liquidity instruments that comply with Shariah principles in the market. The sequence of the establishment of the International Islamic Liquidity Management Corporation (IILM) can further strengthen the robust, Islamic liquidity management infrastructure at the domestic and international levels. IILM has also improved crossborder liquidity management among IIFS (institutions offering Islamic financial services) through the issuance of short -term liquidity instruments that comply with Shariah principles. With the issuance of this IILM sukuk will further increase the number of short -term liquidity instruments in the market and can provide an opportunity for investors to diversify their liquid asset portfolio. Similarly, IILM seeks to improve cross-border liquidity management by internationally strengthening the Islamic liquidity management infrastructure through cooperation between member countries' central banks. In addition, Islamic banking institutions also have the opportunity to conduct IILM sukuk transactions in the secondary market with investors from various countries.