Liquidity Risk Measures Research Articles

Discerning Non-Stationary Market Microstructure Noise and Time-Varying Liquidity in High Frequency Data

In this paper, we investigate the implication of non-stationary market microstructure noise to integrated volatility estimation, provide statistical tools to test stationarity and non-stationarity in market microstructure noise, and discuss how to measure liquidity risk using high frequency financial data. In particular, we discuss the impact of non-stationary microstructure noise on TSRV (Two-Scale Realized Variance) estimator, and design three test statistics by exploiting the edge effects and asymptotic approximation. The asymptotic distributions of these test statistics are provided under both stationary and non-stationary noise assumptions respectively, and we empirically measure aggregate liquidity risks by these test statistics from 2006 to 2013. As byproducts, functional dependence and endogenous market microstructure noise are briefly discussed. Simulation studies corroborate our theoretical results. Our empirical study indicates the prevalence of non-stationary market microstructure noise in the New York Stock Exchange.

Banking and Insurance Recapitalization During the 2007-2009 Credit Crisis

This paper explores the feedback loop between the intensity of financial intermediary recapitalization and a number of measures of credit and liquidity risk. We cover the entire 2007–2009 subprime crisis with a sample that includes the 97 largest banks and insurance companies on both sides of the Atlantic. We model the intensity of recapitalization by implementing a scale measure inspired by the Corrective Action system used by U.S. banking regulators. The proposed ordering starts with inaction, followed by dividend cuts, to issues of new equity, to bailouts. Our risk measures include toxic asset exposures, credit default swap spreads, and Merton-style probabilities of default. We show that there is a monotone relationship between the intensity of risk and the intensity of recapitalization across all firms and jurisdictions in the sample. We are able to simplify the large heterogeneity of regulations and business models across sectors and countries within a fairly simple and general classification scheme. This scheme distinguishes between individual bailouts, targeted at bankruptcy prevention, and more nuanced collective bailouts, which were more related to the prevention of systemic risk arising from potentially solvent institutions.

Liquidity Commonality in the Secondary Corporate Loan Market

Abstract This study is the first to examine liquidity commonality, a measure of liquidity risk, in the U.S. secondary corporate loan market. Liquidity commonality varies substantially across market states, being completely absent during more benign market conditions. The results have implications for banking portfolio management, the pricing of liquidity risk and for regulators interested in the time-variation of liquidity risk in illiquid markets.

Why does higher variability of trading activity predict lower expected returns?

Abstract The paper shows that controlling for the aggregate volatility risk factor eliminates the puzzling negative relation between variability of trading activity and future abnormal returns. I find that variability of other measures of liquidity and liquidity risk is largely unrelated to expected returns. Lastly, I show that the low returns to firms with high variability of trading activity are not explained by liquidity risk or mispricing theories.

Why Does Higher Variability of Trading Activity Predict Lower Expected Returns?

The paper shows that controlling for the aggregate volatility risk factor eliminates the puzzling negative relation between variability of trading activity and future abnormal returns. I also find that variability of other measures of liquidity and liquidity risk is largely unrelated to expected returns. Lastly, I show that the low returns to the firms with high variability of trading activity are not explained by liquidity risk and mispricing theories.

The Application of Asymmetric Liquidity Risk Measure in Modelling the Risk of Investment

Abstract The article analyses the relationship between investment risk (as measured by the variance of returns or standard deviation of returns) and liquidity risk. The paper presents a method for calculating a new measure of liquidity risk, based on the characteristic line. In addition, it is checked what is the impact of liquidity risk to the volatility of daily returns. To describe this relationship dynamic econometric models were used. It was found that there was an econometric relationship between the proposed measure liquidity risk and the variance of returns.

The Influence of Earnings Quality and Liquidity on the Cost of Equity

This study uses sample companies listed in Taiwan Stock Exchange and GreTai Securities Market during 2000 to 2011 to investigate the influence of earnings equality and liquidity on the cost of equity. We define discretionary accruals with three measures and real earnings management with three measures as indicators of earnings quality; trading volume, individual stock liquidity and market liquidity as liquidity measures and individual stock and market liquidity risk as liquidity risk measures. Panel data is suggested for this analysis. Firms manipulating discretionary accruals increase in the cost of equity, but ones operating real earnings management decrease in it when considering that the earnings quality and liquidity directly impact on it. The cost of equity is indirectly influenced by earnings quality and liquidity through information asymmetry measured by bid-ask spreads. The results show that no matter firms engaging in discretionary accruals or real earnings can decrease the cost of equity under higher levels of information asymmetry. The higher the trading volume or the individual stock liquidity risk, the lower the cost of equity when information asymmetry is low.

Turnover: Liquidity or Uncertainty?

I show that turnover is unrelated to several alternative measures of liquidity risk and in most cases negatively, not positively, related to liquidity. Consequently, neither liquidity nor liquidity risk explains why higher turnover predicts lower future returns. I find that the aggregate volatility risk factor explains why higher turnover predicts lower future returns. This paper shows that the negative relation between turnover and future returns is stronger for firms with option-like equity, and this regularity is also explained by the aggregate volatility risk factor. This paper was accepted by Wei Jiang, finance.

The information content of Basel III liquidity risk measures

We present a comprehensive analysis to calculate the Basel III liquidity coverage ratio (LCR) and the net stable funding ratio (NSFR) of U.S. commercial banks using Call Report data over the period 2001–2011, and provide indirect empirical evidence on net cash outflow rates of certain liability categories. In addition, we examine potential links between Basel III liquidity risk measures and bank failures using a model that differentiates between idiosyncratic and systemic liquidity risks. We find that while both the NSFR and the LCR have limited effects on bank failures, the systemic liquidity risk is a major contributor to bank failures in 2009 and 2010. This finding suggests that an effective framework of liquidity risk management needs to target liquidity risk at both the individual level and the system level.

Measuring systemic risk-adjusted liquidity (SRL)—A model approach

Little progress has been made so far in addressing—in a comprehensive way—the negative externalities caused by excessive maturity transformation and the implications for effective liquidity regulation of banks. The SRL model combines option pricing theory with market information and balance sheet data to generate probabilistic measure of systemic liquidity risk. It enhances price-based liquidity regulation by linking a bank’s maturity mismatch impacting the stability of its funding with those characteristics of other banks, subject to individual changes in risk profiles and common changes in market conditions impacting funding and market liquidity risk. This approach can then be used (i) to quantify an individual institution’s time-varying contribution to expected losses from system-wide liquidity shortfalls and (ii) to price insurance premia that provide incentives for banks to internalize the social cost of their individual funding decisions.

Measurement of Liquidity Risk in Keshavarzi Bank (With Value at Risk Approach)

One of the most critical competitive fields of companies and financial institutions and banks to provide optimal financial sources to survive in the turbulent business environment is financial decision making. It`s safe to say any decision should be taken to consider the risks and dangers associated with it. Of course specialized risk management is also one of the most important and fundamental fields that need financial institutions adaptation with new conditions to understand increasing complexity of the rules, technology and customers and it can act better. To make this issue happen, the risk measurement would be the banks and institutions today challenge. In this study the liquidity risk of one second rate branch of Keshavarzi Bank during 2008 to 2012years and with usage of the bank's financial statements, including a variety of deposit accounts, savings, revenue, cost and convenience, have been calculated. Writer hypothesis that significant of trends in the data research (liquidity risk) has been over these years. Should mention to demonstrate this method the value at risk has been used. The reasons for using this method are low cost and fast pace. The results show that this hypothesis along 2008, 2009, 2011 and 2012 years will be rejected and over the years 2008and 2009 are accepted. Generally liquidity risk trend towards an equilibrium (to reach a certain point). Finally, it is proposed that the risk would be tested for other branches as well in varying degrees.

Retracted: Basel III Liquidity Risk Measures and Bank Failure

Retracted: Basel III Liquidity Risk Measures and Bank Failure

A Parametric Approach to Counterparty and Credit Risk

In this paper, we present the results of a business solution on how to measure credit and counterparty risk with the main focus on OTC derivatives. Moreover, we use this approach to include the measurement of liquidity risk exposure. We explain how we measure the exposure for each counterparty with netting arrangements and collaterals. Further we introduce the concept of PFE (potential future exposure) and explain why we opted for a parametric approach. We then develop the concepts of credit loss and default probability as a result of a Poisson process. Further we use the concept of unexpected loss in order to derive the economic capital as the difference between the unexpected loss and the credit loss. Finally we show how this approach can be applied as a refinement of liquidity risk measurement by considering collateral requirements, so as to enhance the monitoring of liquidity congruence between funds' asset and liability, especially under stressed market conditions.

The Information Value of Basel III Liquidity Risk Measures

We calculate the approximate measures of the liquidity coverage ratio and the net stable funding ratio of the Basel III liquidity risk framework using the call report data of U.S. banks. We find that the new measures have little predicting power of bank failures when compared with traditional liquidity risk measures. We also find that marketwide liquidity risk was the major predictor of bank failures in 2009 and 2010. Finally, our study sheds light on the assumptions on net cash outflow rates in the Basel III liquidity risk standards.

Measurement of Dynamic Liquidity Risk Based on High Frequency Data and Empirical Research

Measurement of Dynamic Liquidity Risk Based on High Frequency Data and Empirical Research

Basel III Liquidity Risk Measures and Bank Failure

Basel III banking regulation emphasizes the use of liquidity coverage and nett stable funding ratios as measures of liquidity risk. In this paper, we approximate these measures by using global liquidity data for 391 hand-selected, LIBOR-based, Basel II compliant banks in 36 countries for the period 2002 to 2012. In particular, we compare the risk sensitivity of the aforementioned Basel III liquidity risk measures to those of traditional measures such as the nonperforming assets ratio, return-on-assets, LIBOR-OISS, Basel II Tier 1 capital ratio, government securities ratio, and brokered deposits ratio. Furthermore, we use a discrete-time hazard model to study bank failure. In this regard, we find that Basel III risk measures have limited ability to predict bank failure when compared with their traditional counterparts. An important result is that a higher liquidity coverage ratio is associated with a higher bank failure rate. We also find that market-wide liquidity risk (proxied by LIBOR-OISS) was the major predictor of bank failures in 2009 and 2010 while idiosyncratic liquidity risk (proxied by other liquidity risk measures) was less. In particular, our contribution is the first to achieve these results on a global scale over a relatively long period for a variety of banks.

Liquidity and Liquidity Risk in the Cross-Section of Stock Returns

This paper examines the relative importance of liquidity level and liquidity risk for the cross-section of stock returns. A portfolio analysis is implemented to make inferences about the pricing ability of liquidity as a characteristic or as a risk. I find that the ratio of absolute returns-to-volume, the Amihud liquidity measure, is able to explain more variance in stock returns than a battery of liquidity risk measures. My results suggest that trading cost and frictions impact financial markets more than the systemic components of liquidity.

Liquidity Risk Management after the Crisis

This paper discusses challenges that financial institutions face in the area of liquidity risk measurement and management. The SAS response to these challenges is to deliver an integrated risk solution, SAS® Risk Management for Banking, that can meet the immediate requirements banks have while providing a framework to support future business needs. Lately, liquidity risk has received serious attention from regulators, banks and investors alike. And rightly so, as everyone witnessed how an exogenous credit crisis compounds itself into a major liquidity crisis (or funding problem), propagating very quickly through the global financial markets, leading to insolvency of major financial institutions. Management of liquidity risk has therefore become a subject of vivid discussion in bank boardrooms. The Basel Committee, the Committee of European Banking Supervisors and the UK Financial Services Authority are among the regulatory bodies that have taken the initiative to define a sound framework for its supervision. The objective of all these discussions is to equip banks with adequate liquidity going forward as per their funding profiles as well as to create a liquidity buffer comprising highly liquid assets that can be used to counterbalance a period of liquidity stress. To comply with the latest liquidity risk regulation in Basel III, banks need to prepare for a paradigm shift – moving from routine liquidity management (cash management) activity to a sophisticated liquidity risk management practice in an analytical framework that takes into consideration the stochastic nature of its expected cash flows as well as stress testing of behavioral and market components and the quality and liquidity of the pool of counterbalancing capacity assets. This paper puts in perspective the analytical components of liquidity risk management that are needed to address the new Basel III era of liquidity risk management. Its key components are the cash flow scenarios as a method to measure cash outflow of encumbered assets at any given time horizon, hedging capacity of the unencumbered counterbalancing portfolio, and creating and implementing the stress testing of market and behavioral features that may negatively affect the access to liquidity.

Quantitative measurement and management of liquidity risk in a banking context

Quantitative measurement and management of liquidity risk in a banking context

Funding liquidity risk: Definition and measurement

Funding liquidity risk has played a key role in all historical banking crises. Nevertheless, a measure for funding liquidity risk based on publicly available data remains so far elusive. We address this gap by showing that aggressive bidding at central bank auctions reveals funding liquidity risk. We can extract an insurance premium from banks’ bids which we propose as a measure of funding liquidity risk. Using a unique data set consisting of all bids in all auctions for the main refinancing operation conducted at the ECB between June 2005 and October 2008 we find that funding liquidity risk is typically stable and low, with occasional spikes especially around key events during the recent crisis. We also document downward spirals between funding liquidity risk and market liquidity. As measurement without clear definitions is impossible, we initially provide definitions of funding liquidity and funding liquidity risk.