Measurement and Interaction of Bank Funding Liquidity Risk and Market Liquidity Risk - Assignment Example

? Measurement and Interaction of Bank Funding Liquidity Risk and Market Liquidity Risk Measurement of Bank Funding Liquidity Risk and Market Liquidity Risk Before going ahead with the discussion relating to measurement of funding liquidity risk, it is pertinent to describe what funding liquidity is. According to the Basel Committee of Banking Supervision, funding liquidity refers to the ability of banking institutions to discharge their respective liabilities as and when they stand due (BIS, 2008). On the other hand, as per the definition of International Monetary Fund (IMF), funding liquidity is the ability of financial institutions to discharge their promises regarding payments as per the agreed terms and conditions, which are meant to be referring to time of payment (International Monetary Fund, 2008). Having considered these two versions of definition for funding liquidity, it is also worth noting here that some experts (Brunnemeier and Pedersen, 2007; Strahan, 2008) have defined liquidity from traders and investors’ perspectives, by stating that it refers to their capability and potential to raise funds in short term. In cases when banks are unable to make timely payments or traders or investors are unable to generate funds from the market, as readily as they could have, there is a situation involving funding liquidity risk. International Monetary Fund (2008) defines funding liquidity risk by stating that it is the lack of capability of a financial institution to discharge its liabilities or financial obligations in due time. Normally, funding liquidity risk emerges from availability issues pertaining to the following sources of funding liquidity: Trading of Assets; Securitization; Loan Syndication; and Obtaining loans from Secondary Market. Having considered these factors, it is not a simple task to measure funding liquidity risk. Analysts make use of a variety of funding liquidity ratios so as to determine the possibility and sources of raising funding in a given future time period. However, the measurement and forecasting of funding liquidity risk through ratios is a tiresome process and often requires complex processes and calculations. To simplify the risk measurement process for funding liquidity, Drehmann and Nikolaou (2008) have suggested a more simplistic measure for funding liquidity risk while keeping in view the central bank as the source for funding liquidity. The adjusted bid is denoted by the following expression: On the basis of this adjusted bid determination expression, Drehmann and Nikolaou (2008) then constructed a proxy for the funding liquidity risk, which is the sum of all bids made by all banks. The proxy is presented as follows: Or in other words: The review of theoretical and empirical literature pertaining to funding liquidity risk shows that increased risk associated with funding liquidity reflects an increased valuation of bids in the market, as investors and traders seek more return for higher risk assets. In order to normalize the bid price, Drehmann and Nikolaou (2008) have introduced the concept of adjusted bid, which is ultimately used in the measurement of liquidity funding risk. Having discussed funding liquidity, funding liquidity risk and its measurement, it is now relatively a simple task to describe and understand market liquidity, which in a similar manner, refers to the ability of traders to sell and/or buy assets in the market with no or little influence on its price and at lowest possible costs (Hooker & Kohn, 1994). Market liquidity relates directly to the cost of an asset in the market. It is the bid-ask spread aimed at determining the loss caused to sellers upon selling an asset in the market and purchasing it again at the same time. Another factor which relates to market liquidity is the “market depth”. Market depth is depictive of the number of units of an asset traders are willing to trade while keeping in view the existing prices, i.e. both for bid and ask, provided that no changes in the price of unit(s) take place. In this way, it is stated that when market depth is greater, prices can be moved only if orders with large quantities are placed. Lastly, market resiliency is another concept, which deals with the determination of a market’s ability to revert back in relation to the prices of units being traded. In other words, it is reflective of the time needed for the prices to revert back to original after having declined temporarily during the trade. Market liquidity is often referred to as a systematic and non-diversifiable component of the funding liquidity risk. As far as measurement of market liquidity risk is concerned, the asset pricing model put forward by Holmstrom and Tirole (2001) can be taken into consideration. The model is aimed at measuring the liquidity risk as a commonality between the two variables, which are market liquidity and stock returns. According to Acharya and Pedersen (2005), liquidity risk can be determined with stock returns and thereby making it possible to forecast future returns in turn on the basis of current liquidity risk estimates. Furthermore, the measurement of market liquidity risk is also based on the hypothesis put forward by Amihud and Mendelson (1986), in which they stated that stock return increases with the increase in illiquidity, which is higher liquidity risk. Thus,  This positive relationship has been tested by Amihud (2002) by examining it over a period of time. The study concluded that returns on stocks represent an increasing function of the illiquidity over time, which is not only positive but a significant one. Interaction of Bank Funding Liquidity and Market Liquidity The interaction between funding liquidity and market liquidity is worth understanding, as both interrelate to each other and reap favourable conditions. According to Brunnemeier and Petersen (2007), in theory there shall exist a strong relationship between funding and market liquidity risk. According to them, when banks portray as funding constraints in the market, there is a decline in the prices of assets being traded and as a result there exists a greater risk related to funding liquidity. In such a situation, declining prices of assets will ultimately result in increased margin calls by traders, which in itself signify the increase in risk associated with funding liquidity. In order to upkeep their liquidity positions, banking institutions tend to sell more assets and such practices lead to decline in the prices of assets and even more higher margin calls in the market. This interaction between funding liquidity and market liquidity risks play a significant role in stimulating a situation of financial crisis on a larger canvas. Keeping in view the lack of empirical evidences in this regard, Drehmann and Nikolaou (2008) conducted an empirical investigation to determine the impact of interactions between funding liquidity risk and market liquidity risk. For this purpose, the researchers investigated the relationship between liquidity risk proxy and an index used by the European Central Bank in 2008 as a representative of market liquidity. The empirical investigation revealed that when market liquidity risk was higher, the funding liquidity risk was also following an increasing trend on the graph, thus implying a positive and direct relationship between the two types of liquidity risks. This relationship can be put in a different manner by stating that funding liquidity risk is negatively related to the market liquidity index, that is to say when market liquidity increases, funding liquidity risk is on a declining side and when there is a decline in the market liquidity (index), the risk associated with funding liquidity increases. In this paper, a dataset pertaining to various banks operating in different parts of the world has been considered for understanding the interaction between funding liquidity risk and market liquidity risk. The data set contains information pertaining to total assets of banks, return on assets, return on equity, spreads, certificates of deposits, stock returns and Amihud index. The information contained in the dataset pertains to financial years from 2003 to 2011. First of all, a correlation analysis has been performed between Amihud index, which is representative of the market liquidity risk, and certificates of deposits for banks. The results of the correlation analysis have been presented in the table as follows: Correlations cds Amihud_index cds Pearson Correlation 1 .081** Sig. (2-tailed)   .000 N 2013 2013 Amihud_index Pearson Correlation .081** 1 Sig. (2-tailed) .000   N 2013 2013 **. Correlation is significant at the 0.01 level (2-tailed). The results presented above from correlation analysis show that there exists a positive relationship between market liquidity risk and the volume of assets made available for trading by banks during the period under consideration. The positive relationship is depicted by the coefficient value of 0.081 between the two variables. Apart from this, it is also pertinent to note above that there exists a significant 2 - tailed correlation between Amihud index and volume of assets available or included in trading by the banks. Apart from this, regression analysis between the variables included in dataset has also been performed to understand how funding and market liquidity risks interact with each other. In this regard, following regression analysis assumes Amihud index as the dependent variable and certificates of deposit as independent variable. These considerations of variables imply that volume of assets traded in the market and market liquidity risk (index) are interrelated through regression analysis. The model used for this purpose, does not show satisfactory result since the value of R square is too small to represent variations in the data. However, the relationship between two variables is concluded to be positive and a significant one, as indicated by positive values for coefficient and p value, which is less than 0.05. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .081a .007 .006 .0032988 a. Predictors: (Constant), cds ANOVA b Model Sum of Squares D f Mean Square F Sig. 1 Regression .000 1 .000 13.207 .000a Residual .022 2011 .000     Total .022 2012       a. Predictors: (Constant), cds b. Dependent Variable: Amihud_index Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) .001 .000   16.962 .000 cds .000 .000 .081 3.634 .000 a. Dependent Variable: Amihud_index Similarly, a regression analysis between certificates of deposit (dependent variable) and return on assets, Amihud index, stock returns and total assets (independent variables) has also been performed as follows: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .303a .092 .090 394.5449474 a. Predictors: (Constant), roa, Amihud_index, Stock_return_monthly, totalasset ANOVA b Model Sum of Squares df Mean Square F Sig. 1 Regression 30954361.861 4 7738590.465 49.713 .000a Residual 306350128.139 1968 155665.716     Total 337304490.001 1972       a. Predictors: (Constant), roa, Amihud_index, Stock_return_monthly, totalasset b. Dependent Variable: cds Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 255.700 12.810   19.962 .000 Amihud_index 14768.582 2871.979 .119 5.142 .000 Stock_return_monthly -93.201 15.568 -.129 -5.987 .000 totalasset .000 .000 -.132 -5.647 .000 roa -40.319 3.799 -.230 -10.613 .000 a. Dependent Variable: cds Similar to previous regression analysis, the model used in this case is also unsatisfactory because it does not provide explanation of variations in data, as indicated by the R square value of 9.2 %. In addition, the values of coefficients represent the nature of relationships independent variables have with dependent variables. As for instance, there is a positive relationship between volume (certificates of deposit) and Amihud index (coefficient value = 14768.582), a negative relationship between volume (certificates of deposit) and stock returns (coefficient value = - 93.201), a positive relationship between volume (certificates of deposit) and total assets (coefficient value = 0.00) and a negative relationship between volume (certificates of deposit) and return on assets (coefficient value = - 40.319). In addition to this, it can also be noted that there exists a significant relationship in each case, as for instance, for each of the independent variable in the above table, the p value has been determined as lower than 0.05. Following scatterplots reflect these results in a graphical illustration form: Figure 1: Scatter Plot – Certificates of Deposit vs Amihud Index Figure 2: Scatter Plot – Certificates of Deposit vs Stock Returns Monthly Figure 3: Scatter Plot – Certificates of Deposit vs Total Assets Figure 4: Scatter Plot – Certificates of Deposit vs Return on Assets Reference List Acharya, V., & Pedersen, H. (2005). Asset Pricing with Liquidity Risk. Journal of Financial Economics, 77, 375-410. Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and Time-series Effects. Journal of Financial Markets, 5, 31-56. Amihud, Y., & Mendelson, H. (1986). Asset pricing and the bid–ask spread. Journal of Financial Economics, 17, 223-249. BIS. (2008). Liquidity Risk: Management and Supervisory Challenges. Basel Committee on Banking Supervision. Brunnermeier, M., & Pedersen, H. L. (2007). Market Liquidity and Funding Liquidity. The Review of Financial Studies. Drehmanna, M., & Nikolaou, K. (2008). Funding Liquidity Risk: Definition and Measurement. Munich: Deutsche Bundesbank. Holmstrom, B., & Tirole, J. (2001). LAPM - A Liquidity Based Asset Pricing Model. Journal of Finance, 56(5). Hooker, M. A., & Kohn, M. (1994). An empirical measure of asset liquidity. Hanover: Dartmouth College. International Monetary Fund. (2008). Global Financial Stability Report. New York: Internation Monetary Fund. Strahan, P. (2008). Liquidity Production in the 21st Century. NBER Working Paper 13798. Read More