Liquidity Risk Measures Research Articles

BASEL III LIQUIDITY RISK AND KOSOVO BANKING SYSTEM

The financial crisis 2007-2009 prompted the Basel Committee on Banking Supervision (BCBS) to intensify its efforts to strengthen the principles and standards for capital, as well as for the measurement and management of liquidity risk. Risk management is very important in the financial system, especially in banks. Among various risks Banks face is a liquidity risk it’s managing enables Banks to fulfil their obligationsBasel III consists of set of measures internally agreed. The implementation of Basel III will considerably increase the quality of banks' capital and significantly raise the required level of their capital. In addition, it will provide a "macro prudential overlay" to better deal with systemic risk.Like all Basel Committee standards, Basel III standards are minimum requirements which apply to internationally active banks. Members are committed to implementing and applying standards in their jurisdictions within the time frame established by the Committee.To ensure that banks have sufficient liquidity to survive potential liquidity shocks, as happened few years ago, the Basel Committee has issued two new globally revised minimum standards under the Basel III rules for the first time in the banking history: LCR – Liquidity Coverage Ratio and NSFR – Net Stable Funding Ratio that contain new requirements for bank capital, as well as standardized rules in the liquidity area.Banks need to fully comply with LCR and NSFR rules by January 1, 2019, according to the Capital Requirements Directive & Capital Requirements Regulation (CRD IV & CRR) rules.Basel III rules, in the European Union attain their applicable judicial form through REGULATION (EU) No 575/2013. The regulatory package is due to enter into force on January 1st, 2014, but some provisions will be implemented gradually between 2014 and 2019 and will fully come into force on January 1st, 2019. But these rules are likely to undergo some revisions due to a proposal by European Union (EU), so implementation horizon could go being beyond 2019.Performance of the Kosovo banking sector continued to be positive, thus contributing in maintaining the financial and economic stability of the country. Kosovo’s financial system continues to be characterized with sustainable increase in all its constituent sectors. The banking sector in Kosovo as most successful story is developed by many international institutions, characterized by a large presence of foreign capital, where 89. 2% of all assets are managed by foreign banks and development is based on international standards.Banking sector continued to have good liquidity position, with the main liquidity indicators standing above the minimal level as a required by the regulation.The implementation of Basel III rules in Kosovo related to liquidity depends on the local regulator and Basel III standards.

Asset pricing with extreme liquidity risk

Abstract Defining extreme liquidity as the tail of the illiquidity for all stocks, I propose a direct measure of market-wide extreme liquidity risk and find that it is priced cross-sectionally in the U.S. Between 1973 and 2014, the stocks with high extreme liquidity risk beta earned value-weighted average return of 5.88% annually higher than the stocks with low extreme liquidity risk beta, adjusted for the illiquidity level premium and exposures to aggregate liquidity risk as well as the market, size and value factors. The extreme liquidity risk premium is different from that on aggregate liquidity risk documented in Pastor and Stambaugh (2003) as well as that based on the tail risk in return of Kelly and Jiang (2014). Extreme liquidity risk provides an advance warning about extreme liquidity events. I explore potential economic mechanisms through which the rare and large fluctuations in stock-level liquidity are priced.

Market pricing of liquidity risk: evidence from China

Purpose The purpose of this paper is to investigate whether liquidity risk (i.e. the returns’ vulnerability to the unexpected changes in overall market liquidity) is a priced risk factor in China. Moreover, it investigates the potential role of a stock’s information quality in reducing its liquidity risk during the period of post-non-tradable shares reforms in China. Design/methodology/approach The authors collect data of all the A-share issuing firms listed either on the Shanghai Stock Exchange or Shenzhen Stock Exchange during the period 2006–2016. The authors perform two-stage cross-sectional regression testing. First, the authors perform firm-specific time-series regressions of excess returns over Fama–French’s three-factor model and a liquidity factor. Second, to test whether firm-specific liquidity risk is a priced risk factor, the authors apply Fama and MacBeth’s regressions. Findings Firm-level asset pricing tests provide substantial evidence for market pricing of liquidity risk in China. The authors find a significant negative association between information quality and liquidity risk. The authors also find that the reduction in liquidity risk induced by better information quality is substantial enough to reduce required returns. These findings are robust to alternative measures of liquidity risk and information quality. Practical implications The study underscores that a policy initiative to enhance the information environment can significantly reduce the market volatility in China. Originality/value To the best of authors’ knowledge, this is the first study that considers the Shanghai Stock Exchange as well as Shenzhen Stock Exchange to investigate market pricing of liquidity risk in China.

Integrated measurement of liquidity risk and market risk of company bonds based on the optimal Copula model

The bond market is an important part of the capital market, reflecting the debtor-creditor relationship. Correspondingly, the P2P network lending platform, which plays a vital role in emerging Internet Finance, represents the same relationship. In both markets, the risks in investment and financing channels are complex and diverse, including liquidity risk, market risk, tax risk and so on. All the risks do not exist alone, performed as an integrated risk. This paper establishes an index for measuring corporate bonds’ liquidity, by using a Copula-GARCH approach and generating empirical distribution to integrate the liquidity risk and market risk of corporate bonds. Particularly, according to the Likelihood function test results, t-Copula is the optimum Copula function. The model can also be applied to measure the integrated risk of the P2P network lending platform after improvement.

FACTOR CROWDING AND LIQUIDITY EXHAUSTION

AbstractWell‐known anomalies and stable patterns in equity returns are widely employed to guide stock selection. The use of overlapping multifactor models built on these patterns induces correlated trade across investors. A stock with a strong signal from a parsimonious multifactor stock selection model exhibits changes in trade activity, net order imbalances, lower volatility, lower liquidity level, and changes in liquidity comovement consistent with correlated trade. These results illustrate that correlated trading among investors can affect the liquidity and risk of the securities they trade, and imply that measures of portfolio liquidity risk that ignore these changes can understate risk.

Assessment of Liquidity Risk Management in Islamic and Conventional Banks - An Empirical Study

The aim of this study was to assess liquidity risk management (LRM) practices in Jordanian Islamic and conventional banks during the period (2013-2017). Data used were comprised from six banks, which consisted of three Islamic and three conventional banks in Jordan. Data from the banks financial annual reports covering from 2013-2017 were used to calculate the ratios used as a substitute for liquidity risk in conventional banking as well as Islamic banking in Jordan. The study found the relationship of the size of financial institution, return on asset, return on equity, and capital adequacy ratio with liquidity risk measurement is positive and important both in Islamic banks and conventional banks. Researchers found the practices of LRM are not optimal, yet were based on some considerations explained in this study. Further, progressive actions needs to be taken by the regulators and the banking players to improve the LRM practices.

Twitter versus Traditional News Media: Evidence for the Sovereign Bond Markets

This paper compares news in Twitter with traditional news outlets and then emphasizes their differential impact on Eurozone's sovereign bond market for a homogeneous news topic. We find a two-way information flow between Twitter's “Grexit” tweets and the respective mentions in traditional news outlets. The influence of Twitter on the traditional news is consistently more prolonged, especially in high-activity periods. We also assess the differential impact of the two news sources on sovereign spreads over and above the impact of economic/financial fundamentals, namely measures of default risk, liquidity risk and global financial risk. Our focus is on the borrowing costs of Eurozone's periphery; for comparison reasons, we also consider France as a core Eurozone country. The effect of Twitter on the Greek sovereign spread is positive and of higher magnitude than that of traditional news outlets. Weak contagion effects are recorded primarily for the case of Portugal and Ireland.

Recurrence duration statistics and time-dependent intrinsic correlation analysis of trading volumes: A study of Chinese stock indices

The trading volume in stock markets is known as an important variable which reflects the liquidity of the financial markets and therefore is regarded to be greatly important for the measurement of market liquidity risk. In this work, a new concept called recurrence duration is introduced for study of daily trading volumes, which is inspired by idea of the volatility duration that was proposed and studied in our previous work. The recurrence duration is thought as the shortest passing time that the following days’ trading volume takes to exceed or go below the current trading volume which is time-varying. Similar to the volatility duration distribution of the price returns, the power-law function could describe the empirical probability distribution of recurrence durations of trading volumes, and their tail distributions can be fitted by two stretched exponential functions. Further, the correlation relationships of trading volumes between Chinese stock indices as well as the correlations of recurrence durations are investigated. One approach employed is a recently proposed method, time-dependent intrinsic correlation (TDIC), which is based on the empirical mode decomposition (EMD) to decompose nonlinear and nonstationary signals into the intrinsic mode functions (IMFs), the instantaneous periods of which are used then in determination of the sizes of sliding windows to compute the running correlation coefficients for the multiscale signals. The empirical results reveal rich patterns of correlations for both trading volumes and recurrence durations at different scales for different modes. Another approach is the widely-used DCCA cross-correlation coefficient, by which the level of cross-correlation is measured for both original series and IMF modes of the stock indices.

New bid-ask spread estimators from daily high and low prices

Estimating trading costs in the absence of recorded data is a problem that continues to puzzle financial market researchers. We address this challenge by introducing two low frequency bid-ask spread estimators using daily high and low transaction prices.The range of mid-prices is an increasing function of the sampling interval, while the bid-ask spread and the relationship between trading direction and the mid-price are not constrained by it and are therefore independent. Monte Carlo simulations and data analysis from the equity and foreign exchange markets demonstrate that these models (especially SHL2) significantly out-perform the most widely used low-frequency estimators, such as those proposed in Corwin and Schultz (2012) and most recently in Abdi and Ronaldo (2017). Using real world data we show that one of our estimators (SHL2)’s root mean square error (RMSE) is almost less than a half (even 20%) of the competitors. We illustrate how our models can be applied to deduce historical market liquidity in US, UK, Hong Kong and the Thai stock markets. Our estimator can also effectively act as a gauge for market volatility and as a measure of liquidity risk in asset pricing.

PENGARUH RISIKO LIKUIDITAS DAN LEVERAGE TERHADAP KINERJA BANK YANG TERDAFTAR DI BURSA EFEK INDONESIA PERIODE 2010--2015

This empirical study aims for examining the influence of liquidity risk on bank performance from the period 2010 to 2015 listed on Indonesia Stock Exchange. The measurement of liquidity risk will involve deposits or third-party fund, cash, and liquidity gap whereas the leverage will involve total debts to total assets as the independent variables. Meanwhile, the dependent variables of this study involve ROA and Tobin’s Q. This study was conducted under the purposive sampling method which involved 22 banks as samples. This study also used a panel data regression method and three testing models, including Common Effect Model, Fixed Effect Model, and Random Effect Model. The findings suggest a positive and significant influence of the third-party fund on ROA and Tobin’s Q. In contrast, the liquidity gap has a negative and significant influence on ROA and Tobin’s Q. Besides, the findings also suggest a positive and significant influence of cash on ROA and negatively significant on Tobin’s Q. The leverage has negative and significant influence on ROA and positively significant on Tobin’s Q.

A tale of two risks in the EMU sovereign debt markets

We introduce time-varying systematic yield risk (SYR) and systematic liquidity risk (SLR) measures for sovereign bond markets of the major European Monetary Union (EMU) country members. Using daily sovereign bond data, our analysis shows that trend components of both types of risk are strongly positively correlated. Vector auto-regression and generalized impulse response analysis reveal that shocks to the SLR has significant impact on SYR lasting up to 5 days, whereas shocks to the SYR has no significant impact on SLR. Since mid-2015, both risks are gradually increasing and as of 2018, they are at their highest levels over the last five years.

Liquidity Risk and Its Determinants: A Study on Bosideng International Holding Limited Company

Liquidity risk is one of the important risk in financial risk management. It is important to even a small firm until to the big financial institutions like bank. It has become increasingly important to measure, manage and assess the impact of liquidity risk on the economics of every firm. liquidity risk has been crucial for financial institutions and for the stability of the financial system. The financial crisis has promoted the adoption of much more advanced liquidity risk management policies and liquidity risk measurement methodologies. This study attempted to investigate the influence of firm-specific factors and macro-economic factors affecting liquidity risk of Bosideng International Holding Limited. This study employs time series regression analysis this firm from 2012 to 2016. The analysis shows that firm-specific factors (average collection period) and macro-economic factor (company’s beta) influence the liquidity risk of the firms. This study suggest that the firms should manage their account receivable efficiently by establishing a clear credit.

Does Liquidity Risk Explain the Time-Variation in Asset Correlations? Evidence from Stocks, Bonds and Commodities

The time-variation in asset correlations have broad implications in asset pricing, portfolio management and hedging. Numerous studies in the literature have found that the change in correlations is mainly related to the magnitude of market movements, hence volatility. However, recent research finds that the size of markets fluctuations is not necessarily the primary driver for the time-variation in correlations, but that the effect of market movements is amplified in times of high financial distress, characterised by low liquidity. This paper seeks to investigate the effect of liquidity on time-varying correlations among different asset classes, namely stocks, corporate bonds and commodities. Our findings show that liquidity indeed has a significant effect on the time-variation in asset correlations, particularly in the case of how bond returns comove with other asset classes. We observe that higher liquidity risk is associated with lower correlation of bond returns with stocks as well as commodities. Our findings suggest that measures of liquidity risk can improve models of correlations; and potentially help improve the effectiveness of risk management strategies during periods of financial distress.

Does Liquidity Risk Explain the Time-Variation in Asset Correlations? Evidence from Stocks, Bonds and Commodities

The time-variation in asset correlations have broad implications in asset pricing, portfolio management and hedging. Numerous studies in the literature have found that the change in correlations is mainly related to the magnitude of market movements, hence volatility. However, recent research finds that the size of markets fluctuations is not necessarily the primary driver for the time-variation in correlations, but that the effect of market movements is amplified in times of high financial distress, characterised by low liquidity. This paper seeks to investigate the effect of liquidity on time-varying correlations among different asset classes, namely stocks, corporate bonds and commodities. Our findings show that liquidity indeed has a significant effect on the time-variation in asset correlations, particularly in the case of how bond returns comove with other asset classes. We observe that higher liquidity risk is associated with lower correlation of bond returns with stocks as well as commodities. Our findings suggest that measures of liquidity risk can improve models of correlations; and potentially help improve the effectiveness of risk management strategies during periods of financial distress.

Stress Testing of Liquidity Maturity Transformation Risk in Banks

One of the main causes of the past crisis was the inability of financial institutions to acquire funding at appropriate costs. The importance of applying a good liquidity risk measurement system becomes apparent. The present paper provides an approach to the measurement of liquidity maturity transformation risk within a stress testing framework, for middle-sized banks. The costs of liquidity arising due to a downturn in refinancing conditions are calculated by using modern risk measures. The forward-looking way is based on a liquidity gap report, where the consideration of the counterbalancing capacity enables to gain an insight into the real liquidity needs. The measurement of both, the portfolio-value in the respective time bucket and liquidity costs, is possible. Applying the expected shortfall can easily be included into the calculation. The results show that by using historical simulation, if no sufficient data are available, expected shortfall delivers an approximate value. Still, it can serve as an indicator of insurance against extreme events. The present approach combines a scenario-based view to a possible distress with a quantitative risk measurement. Therewith, it contributes to the bank’s wide stress testing as required by the regulatory authorities.

Carry Trade Returns with Support Vector Machines

AbstractThis paper proposes a novel approach to directional forecasts for carry trade strategies based on support vector machines (SVMs), a learning algorithm that delivers extremely promising results. Building on recent findings in the literature on carry trade, we condition the SVM on indicators of uncertainty and risk. We show that this provides a dramatic performance improvement in strategy, particularly during periods of financial distress such as the recent financial crises. Disentangling the measures of risk, we show that conditioning the SVM on measures of liquidity risk rather than on market volatility yields the best performance.

Bank Liquidity Risk and Performance

This study employs an alternative measure of liquidity risk to investigate its determinants by using an unbalanced panel dataset of commercial banks in 12 advanced economies over the period 1994–2006. Dependence on liquid assets for external funding, supervisory and regulatory factors, and macroeconomic factors are all determinants of liquidity risk. Because of higher funding costs for obtaining liquidity, liquidity risk is regarded as a discount for bank profitability, yet liquidity risk shows a premium on bank performance in terms of banks’ net interest margins. Liquidity risk has reverse impacts on bank performance in a market-based financial system.

Uticaj specifičnih bankarskih faktorana likvidnost komercijalnih banaka u Srbiji

The aim of this work is to identify internal factors that affect the liquidity of commercial banks in Serbia. Research results in the observed period from 2008 to 2013 using regression analysis indicate that the liquidity of banks is positively correlated with capital adequacy ratios and interest income to total assets ratio, while negative and statistically significant relationship exists between the indicators of liquidity and the size of the bank (measured by bank assets), expense ratios compared to interest income and return on equity ratios. This research represents the first step in achieving optimization model of liquidity, because many financial institutions, although profitable, are faced with the problem of maintaining liquidity. Research question that arises is the following: Which of the observed indicators affect the liquidity of commercial banks in Serbia the most? The survey used unconsolidated balances of 23 commercial banks in the period from 2008 to 2013. In particular, using ordinary least squares technique, author takes two different measures of liquidity risk into consideration. After obtaining an answer to the main question of this work regarding the key indicators of impact on the liquidity of the banking sector in Serbia, one can define the strategies and model for improvement of the operation of banks in financial markets. The results highlight that size, capitalization and profitability of banks can have an impact on liquidity risk management.

Liquidity risk management: practice among Croatian firms

The main purpose of this paper is to explore the practice of liquidity risk management of Croatian business entities. The analysis is based on a survey of 62 business entities in Croatia. The authors investigate the existence of risk management and liquidity risk management measures among the surveyed business entities. The respondents’ knowledge of management, their use of indicators and methods for the management of liquidity risk, in addition to the cited reasons for implementation of liquidity risk measures were also subject to examination. Furthermore, the authors investigate the importance of liquidity management in business. The analysis reveals that Croatian business entities have neither sufficient knowledge regarding the majority of financial indicators, nor they tend to use liquidity management plans. Consequently, the survey’s findings indicate that the overall level of financial knowledge of Croatian managers is inadequate. This can, thus, be identified as one of the reasons for the traditionally high number of illiquid business entities in the market. Finally, this paper provides academia and policymakers with new revelations concerning the management of liquidity risk among business entities in Croatia.

An Artificial Neural Network and Bayesian Network model for liquidity risk assessment in banking

Abstract Liquidity risk represent a devastating financial threat to banks and may lead to irrecoverable consequences in case of underestimation or negligence. The optimal control of a phenomenon such as liquidity risk requires a precise measurement method. However, liquidity risk is complicated and providing a suitable definition for it constitutes a serious obstacle. In addition, the problem of defining the related determining factors and formulating an appropriate functional form to approximate and predict its value is a difficult and complex task. To deal with these issues, we propose a model that uses Artificial Neural Networks and Bayesian Networks. The implementation of these two intelligent systems comprises several algorithms and tests for validating the proposed model. A real-world case study is presented to demonstrate applicability and exhibit the efficiency, accuracy and flexibility of data mining methods when modeling ambiguous occurrences related to bank liquidity risk measurement.