Banks should have adequate systems for measuring, monitoring and controlling liquidityrisk (BIS, 2006). Importance of riskmanagement in banking system has increased due to the fact that the more loans they start to place, the greater the risk they expose. Among these risks, liquidityrisk arises when a given financial obligation could not be fulfilled by the banks, for a certain period of time. After global financial crisis, the liquidityrisk is one of the issues that has taken the priority in the agenda of regulatory bodies and Basel Committee on Banking Supervision defines main principles of liquidityriskmanagement in 2008.
Potentially, the banking institution might face difficulty in fulfilling the requests of depositors when there are issues with liquidity which would impact the performance and reputation of the bank (Jenkinson, 2008). Hence, when funds requested by depositors are not disbursed timely, confidence towards the bank will be affected. In fact, poor liquidity situation may result in action to penalize the bank to be taken by the regulators (Jenkinson, 2008). In addition, changes in the structure of financing and riskmanagement due to fierce competition to at tract deposits, various financing products as well as advancement in technology would impact the bank (Akhtar, 2007). A bank that does not maintain sufficient liquidity can cause instability to its institution even with high quality assets, sufficient capital and stable earnings (Crowe, 2009; Andrew, 2012). It is submitted that poor LiquidityRiskManagement (LRM) can be established from events in economic crisis relating to weaknesses in the corporate governance system, riskmanagement and internal control. Therefore, it is incumbent on the bank to adopt a regulatory framework (RF) as a tool to discipline compliance to manage liquidityrisk effectively.
Liquidityriskmanagement in day-to-day operations is typically achieved through the management of a bank‟s assets while, in the medium term, it is achieved through management of the structure of the bank‟s liabilities (Mueller, 1998; Myers and Rajan, 1998; Motyka, Leuca and Fawson, 2005; Poorman and Blake, 2005; BIS, 2006; BIS, 2008b). Asset and liability management can be viewed as the proactive management of both sides of the bank‟s financial statement position with special emphasis on the management of interest rates and the liquidityrisk (Heffernan, 2005). Liquiditymanagement thus encompasses both asset and liability management dimensions. When viewed in this context, liquiditymanagement may be appropriately viewed as an important part of the entire funds management programme and the overall financial condition of the bank. It is clear, then, that strategies to cope with pressures arising from the banking environment are executed in the form of Asset Liability Management (ALM) practices. An efficient ALM technique aims to manage the volume, mix, maturity, rate, sensitivity, quality and liquidity of the assets and liabilities as a whole so as to achieve a predetermined acceptable risk reward ratio (Rose, Kolari and Fraser, 1993; Bhattacharya and Thackor, 1993; Gabbi, 2004). The implication is that the sophistication of a bank's liquiditymanagement process depends on its business activities and overall level of risk. Most banking activity depends on the bank‟s ability to provide liquidity to its customers. The majority of financial transactions and commitments have implications for a bank‟s liquidity. In line with this, various authors (Bhattacharya and Thackor, 1993; Freixas and Rochet, 1999; Van Greuning and Bratavonic, 2003; RBM, 2007) have indicated that effective liquidityriskmanagement by banks serves some of the following important functions:
the crisis, increasing credit concerns and feeble market liquidity animated a cycle of deteriorating asset market values and deleveraging. Authorities around the world sort for a solution as inter-bank lending came to a halt, credit risk and capital flight became common-place, and banks were on their knees in search of liquidity. Many financial institutions were bailed-out or restructured. The inability of a bank to meet up with its financial obligation/liability is a premise on which crisis may result. This issue may be due to deterioration in asset quality or general loss of confidence in the financial institution due to circumstances more or less related to the bank in question. It therefore becomes imperial for banks to develop policies and standards that best measure and manage their liquidity positions on an on-going basis. More so, it is also necessary to project funding liquidity issues that could crop-up during a crisis event (stress testing and scenario analyses). In this paper we attempt to piece together standard practices of bank LRM, while keeping a close on ‘Basel II pillar 3’ disclosure criteria. The reason we look up to Basel principles is, in February 2008, the Basel Committee on Banking Supervision published ‘LiquidityRiskManagement and Supervisory Challenges’ 1 which somewhat
Does the upper-bracket of financial institutions disclose easily accessible information? If it does, what and how much information does this “too big to fail” category provide in terms of liquiditymanagement? This debate has taken on added significance with increasing global financial integration and shortening of intervals between financial crises. LiquidityRiskManagement (LRM) has become increasingly vital in the banking industry, especially with the recent financial meltdown and economic down-turn. During the crisis, increasing credit concerns and feeble market liquidity animated a cycle of deteriorating asset market values and deleveraging. Authorities around the world sort for a solution as inter-bank lending came to a halt, credit risk and capital flight became common-place and banks were on their knees in search of liquidity. Many financial institutions were bailed-out or restructured. The inability of a bank to meet up with its financial obligation/liability is a premise on which crisis may result. This issue may be due to deterioration in asset quality or general loss of confidence in the financial institution due to circumstances more or less related to the bank in question. It therefore becomes imperial for banks to develop policies and standards that best measure and manage their liquidity positions on an on-going basis. More so, it is also necessary to project funding liquidity issues that could crop-up during a crisis event (stress testing and scenario analyses). As pointed out by Goodhart (2008), “liquidity and solvency are the heavenly twins of banking, frequently indistinguishable. An illiquid bank can rapidly become insolvent and an insolvent bank illiquid”. As an extension the management of information asymmetry resulting from bank liquidity issues is crucial for the solvency and survival of the financial institution.
years, and the financial disturbance it brings has widely and rapidly reached every corner of the economy and taken effect. Liquidityriskmanagement has thus become an important subject for commercial banks to make sure safety. Liquidity has always been taken as the basis for commercial banks to conduct all forms of activities, and liquidityrisk has accompanied all along with the whole development process of banks and directly con- nected with the survival of banks as well as the stability of the financial system. The liquidityriskmanagement of commercial banks in China is still in its infancy, we have to take measures based on our own characteristics in addition to successful experience from foreign banks. This paper detailedly narrates the status quo for the li- quidity riskmanagement of commercial banks in our country below the setting of global financial crisis through data analysis and qualitative research method after some brief introductions. Then it identifies the existing prob- lems that we need to pay attention to, establishes the ARMA model based on the monthly data of liquidity gap and does the forecast. Finally, several corresponding suggestions have been brought up for the liquidityriskmanagement of China’s commercial banks, which is expected to provide some references for the research and practice of liquidityriskmanagement for commercial banks.
We now show that the insights of the three-period example extend to an inﬁnite-horizon binomial model. Our model is a normative model of liquidityriskmanagement in the spirit of textbook models of interest-rate riskmanagement. Through our model, we are able to give some guidance on the optimal maturity structure of bonds that minimizes rollover risk. Time is discrete and indexed by t . The continuously compounded riskless interest rate at all maturities is constant and equal to r . There is an inﬁnitely lived project whose discounted value is a martingale,
Association for Microfinance Institutions (AMFI) observes that 60% of the Kenyan population is out of the scope of the formal banking services. At least 35.2% are in need of financial services and unable to access the formal financial services and another 30.2% are entirely excluded from accessing financial services. As a result of this, the number of MFIs has been increasing over time. As of June 2003, the Central Bank of Kenya reported the existence of 3,460 legal microfinance service providers including 3,397 savings and credit co-operatives (SACCOs) and co-operative-like community-based intermediaries, 56 MFIs, 4 commercial banks (K-Rep, Equity, Post Bank and Co-operative Bank), 2 building societies, and the Kenya Post Office Savings Bank. By 2007, there were 5122 registered SACCOs, 45 banking institutions, 42 of which were commercial banks, 2 mortgage finance companies and 1 non-banking financial institution. Liquidity is a bank’s capacity to fund increase in assets and meet both expected and unexpected cash and collateral obligations at reasonable cost and without incurring unacceptable losses. Liquidityrisk is the inability of a bank to meet such obligations as they become due, without adversely affecting the bank’s financial condition. Effective liquidityriskmanagement helps ensure a bank’s ability to meet its obligations as they fall due and reduces the probability of an adverse situation developing.
The study analyzed the relationship between Size of the bank, Nonperforming loan, Return on asset, Return on equity, Capital adequacy ratio, Investment to deposit ratio with liquidityrisk by ratio analysis and descriptive statistics, correlation ,regression analysis. It is prevalent from the data analysis that liquidityrisk is attached with Islamic banks. Ratio analysis shows the ratio of Size of the bank, NPL ROA, ROE, Investment to deposit ratio have increased in 2016 from 2015.On the other hand ,CAR and Cash to cash equivalent assets are decreasing in 2016 from 2015. Islamic banks should follow techniques to reduce liquidity risks. They are:
Nowadays banks follow stricter rules and procedures when it comes to struggle for staying sound in financial services market. International financial institutions proposed several recommendations for ensuring favorable liquidity parameters for banks. Although all of their recommendations are not evenly applied by all central banks, some of them are successfully disseminated. Liquidity buffers have already become a widely used tool for minimizing liquidityrisk by keeping particular amount of financial reserves at particular more stabile foreign or local banks with a condition of receiving back in case of liquidity failure.
But this takes us back to our starting point, how far should a Central Bank allow the commercial banks to put liquiditymanagement onto Central Banks. Clearly if commercial banks can always rely on the Central Bank, they will undertake maximum maturity transformation, i.e. hold 20 year advances against overnight wholesale funds, in order to take advantage of all liquidity premia and the normally upwards sloping yield curve. One essential requirement is to ensure that the Central Bank and the taxpayer do not take the downside, and the commercial bank the upside, of such a liquidityrisk play, and the ‘two-name’ paper proposal above goes in that direction. Even so, it is surely undesirable for Central Banks to face the prospect of holding billions of assets for quite long periods of time as the Bank of England has had to do with Northern Rock. By October 24, the total had reached GBP 20 billion and was still rising; not a satisfactory state of affairs.
from an individual perspective as they should allow banks to increase profitability with- out increasing the likelihood of bankruptcy, due to the explicit or implicit commitment of the lender of last resort. Using data for European and North-American banks in the run up to the global financial crisis of the last few years, we empirically assess whether there is evidence of collective herding behavior of these banks in their liquidityriskmanagement choices. This analysis is very relevant from a policy perspective, as it may contribute to the discussion on how regulation can provide the correct incentives to minimize negative externalities. In case of evidence of herding on risk-taking strategies in the run up to the financial crisis, future regulation should include harsher penalties for banks with riskier liquidity positions. The new Basel III package on liquidityrisk already includes some features that constitute a positive step in this direction. Nevertheless, this regulation is still dominantly microprudential. Hence, when there is evidence of collective risk-taking behaviors on liquidityrisk, additional macroprudential policy tools may need to be con- sidered, such as additional liquidity buﬀers on the entire banking system or limits to certain types of exposures, in order to mitigate contagion and systemic risks.
— A “Liquidity Implementation Task Force” joined by 19 banks worldwide is aiming to provide a forum in which common requirements arising from regulatory changes can be identified and agreed, and appropriate responses identified – leveraging existing SWIFT services. It is also looking at identifying opportunities to enhance, amend or supplement existing SWIFT products and services to help members meet their liquidityriskmanagement requirements. Finally the group is also dedicated to encouraging a consistent and standardised global implementation of liquiditymanagement services, for example the provision of historical transaction and liquidity usage data by RTGS systems, thereby reducing implementation costs and complexity for international banking groups. The first deliverable agreed by the group is the development of best practices for the usage of the intraday cash reporting messages.
In this topic there will be addressed the liquidityrisk with a special focus to the Albanian case. Liquidityriskmanagement has become a vital responsibility to conduct the business of banks in particular and the economy in general. The technical side of managing balance sheet items, planning and realization of budgets, missmatches of maturities etc., have now a special importance in the activities of financial institutions in the world, but also in Albania, mainly in the banking system. In this context, it is worth mentioning that in recent years, liquidityrisk has obtained a special importance in Albania. This is reflected in the strengthening of the regulatory requirements and increased reporting to the Bank of Albania, particularly in the context of recent developments in the global financial market.
The main source of financing the activities of a commercial bank they predominantly have been deposits, at the very low-level Loans between commercial banks and the other part debt instruments. Moreover, due to this model of financing, exposure to liquidityrisk even during the crisis period was minimal. Commercial banks in Kosovo from 2008 to 2018 have had liquidity reserves at a higher level than the level which has been required by CBK. Liquidityriskmanagement is the responsibility of the bank, therefore, the bank should establish a robust liquidityriskmanagement framework to maintain liquidity at sufficient levels, including the coverage of high quality liquid assets (BCBS, 2008). According to the (Korean Institute of Finance, 2010), the primary objective of liquidityriskmanagement is to provide sufficient liquidity and reliability at any time and in all circumstances.
The first chapter, co-authored with Katerina-Chara Papioti, provides a tool for central banks to measure liquidityrisk in their financial sector using the bidding behavior of banks in bond auctions. First, we build up a model combining the auction literature and the financial economics literature to understand precisely the eﬀect of the liquidityrisk aﬀecting banks on their bidding strategies in those auctions. We develop a benchmark version of the model with no insurance against the liquidity shock, and another with a lender of last resort to see how the behavior of the banks is aﬀected by this policy. Second, we use these theoretical results and a unique dataset collected at Central Bank of Chile containing all the details of its open market operation auctions (where it sells bonds to drain money from the banking sector) between 2002 and 2012 to estimate the distribution of the liquidityrisk across Chilean banks and its changes over time. The evolution of the estimated distribution seems to capture well the main episodes of liquidity stress of the last decade in the Chilean banking sector. This measuring tool could be used by other
Our model suggests that the regulator needs to impose comprehensive liquidity requirements ex ante to all financial institutions in order to prevent the incentive to free-ride on liquidity in good states. The extent of liquidity requirement depends on the probability π of the good state. In our model, π is known to all agents. The regulator does not need to be better informed than other market participants. Out of distrust for regulator’s abilities and incentives, however, economists usually favor private market arrangements. Kashyap, Rajan and Stein (2008) recently proposed the idea of private insurance against systemic risk (forcing banks to hold some type of contingent capital). One of their proposals was to require that systemically important, and leveraged, financial firms buy fully collateralized insurance policies (from unleveraged firms, foreigners, or the government) that will capitalize these institutions when the system is in trouble. In that case, instead of the regulator defining some liquidity threshold α, he would impose the need to buy insurance against systemic liquidityrisk. As insurance premium, the shadow value which gives banks incentives to hold the required minimum level α would need to be charged.
One of the first studies that empirically links asset pricing and liquidity is Ami- hud and Mendelson (1986), who show that shares’ excess returns increase in the size of the average bid-ask spread, a well-known measure of an asset’s level of liquidity. Recent research has provided further important empirical ev- idence on the relevance of time-varying market-wide liquidity on asset pricing and of the effects of monetary expansions on liquidity during crisis periods. Pastor and Stambaugh (2003) measure market liquidity as the equally weighted average of individual shares’ expected return reversal. The authors start from the idea that a sell (buy) order should be accompanied by a negative (posi- tive) price impact that one expects to be partially reversed in the future if the share is not perfectly liquid. Sharp declines in this measure coincide with mar- ket declines and ‘flight to quality’ or ‘flight to liquidity’ episodes in which in- vestors want to shift from relatively illiquid medium to long-term assets such as shares into safe and liquid government bonds or cash. Examples of such incidents are discussed in the following section 1.2. Market-wide liquidity as measured by Pastor and Stambaugh (2003) appears to be a state variable that is important for share prices. Shares whose returns are more sensitive to aggre- gate liquidity have substantially higher expected returns, even as the authors control for exposures to the market, size and value factors of Fama and French (1993) and a momentum factor.
there will be excess liquidity at t = 1 if the good state occurs (with a large share of type 2 projects realised early). A bank anticipating this event has a strong incentive to invest all their funds in type 2 projects, reaping the benefit of excess liquidity in the good state. As long as the music is playing, such a deviating bank gets up and dances. Having invested only in high yielding projects, the dancing bank can always credibly extract entrepreneur’s excess liquidity at t = 1, promising to pay back at t = 2 out of highly profitable projects. After all, at that stage, this bank, free riding on liquidity, can offer a capital cushion with expected returns well above what prudent banks are able to promise. Of course, if the bad state happens, there is no excess liquidity. The “dancing”banks would just bid up the interest rates, urgently trying to get funds. Rational depositors, anticipating that these banks won’t succeed, will already trigger a bank run on these banks at t = 1 2 .
Perhaps the strongest assumption is that investors need to sell all their securi- ties after one period (when they die). In a more general setting with endogenous holding periods, deriving a general equilibrium with time-varying liquidity is an onerous task. While our model is mostly suggestive, it is helpful since it provides guidelines concerning the first-order effect of liquidityrisk, showing which risks are priced. The assumption of overlapping generations can capture investors’ life- cycle motives for trade (as in Vayanos (1998), and Constantinides, Donaldson, and Mehra (2002)), or can be viewed as a way of capturing short investment horizons (as in De Long, Shleifer, Summers, and Waldmann (1990)) and the large turnover observed empirically in many markets.