and liquidity risk

As discussed in the literature review, behavioral biases might help to resolve the liquidity risk puzzle. For instance, our previous analysis of market conditions suggests that investors are not strongly averse to liquidity risk during extreme down markets. Here, we are incorporating a measure of investor sentiment and check whether it can affect the return-liquidity risk relation. Prior literature presents several measures of investor sentiment, which is referred to the level of noise traders’ beliefs relative to Bayesian beliefs (Tetlock (2007)). One of them is the VIX index, which we employ here. Some document a significantly negative relation between investor sentiment and stock return. However to our knowledge, no article provides either theoretical prediction or empirical evidence on whether investor sentiment affects liquidity risk premium. Since irrational investors behave like a herd, rushing in and out of markets together, increasing the size of the herd does little to support liquidity, indeed it could reduce it and causes those markets to exhibit even greater volatility. Therefore, the following empirical analysis might provide additional insights to the negative liquidity risk premium puzzle:

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 liquidity risk 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.

years, and the financial disturbance it brings has widely and rapidly reached every corner of the economy and taken effect. Liquidity risk management 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 liquidity risk 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 liquidity risk management 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 risk management 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 liquidity risk management of China’s commercial banks, which is expected to provide some references for the research and practice of liquidity risk management for commercial banks.

This paper examines the impact of illiquidity and liquidity risk on expected stock returns in the Turkish stock markets. Using daily data of the ISE-100 stock index from 2005 to 2012 and Amihud (2002) illiquidity measure, we test the liquidity-adjusted capital asset pricing model (L-CAPM) of Acharya and Pedersen (2005). Performing cross-sectional regression tests across test portfolios, we find supporting evidence that illiquidity is significantly and positively priced. Specifically, our results indicate that liquidity risk arising from the commonality in liquidity is the most important component of liquidity risk. The strong interrelationship between the market liquidity and the liquidity of individual stocks suggests that market-wide shocks on the Istanbul Stock Exchange might quickly affect every stock in this market. Hence, liquidity commonality might create a systemic risk in which case liquidity shocks can be perfectly correlated across all stocks.

Liquidity risk management is very important to every company to mange their company’s liquidity. The aim of this study attempts to investigate the firm-specific factor (internal factors), macroeconomic (external factors), and firm-specific factor, macroeconomic influence towards liquidity risk in Audi AG. The method of the study is regression analysis of Audi by using the SPSS Statistic 25 System. This study is based on annual report of 5 years, from 2014 to 2018. The liquidity risk of Audi AG show in the regression analysis has greater influence by operating ratio (firm-specific factor) in company and inflation rate (macroeconomic) in German.

Funding liquidity risk is not the sole risk associated with “liquidity”. Commonly one separates funding liquidity from market liquidity. Of the two, funding liquidity risk has received the major attention from researchers and especially practitioners for its obvious significance and higher tractability for banks. Market liquidity risk on the other hand has been less researched and as this study has shown has not been satisfactorily defined as a quantifiable concept. Market liquidity risk arises in the banks’ trading books. In fact as per our definition market liquidity risk always arises prior to any kind of transaction with any type of counterparty. Commonly market liquidity is discussed in the context of transactions in organized financial markets, but it does not have to be restricted to them. The main reason why theoretical literature focused on organized financial markets is the data availability. High frequency data and long time series allow robust statistical modeling whereas numerous markets generally do not provide that data. As the definition of market liquidity risk entails both the transformation of cash into assets and the assets into cash, banks are not only exposed to market liquidity risks when they intend to liquidate assets but also when they form the intention to acquire it. Thus, when following our definition we would have to apply the quantification of the risk for both buy and sale transactions. Commonly within banks the term liquidity means the availability of useable funds. Phrases such as “…free up liquidity…” or “…we need liquidity in times of crisis…” reflect the usage of the term liquidity. While the usage lends its meaning to a funding liquidity perspective, it is not correct to use it in such a way for market liquidity. Furthermore, the usage implies that market liquidity risk measurements should only be applied to asset sales and not asset acquisitions. This is a limited view and should not be allowed in our framework.

(6.4) In a portfolio with multiple derivatives netting is possible. For credit risk, e.g. in the case of CVA, one considers so-called netting sets. A netting set is the set of transactions that can be netted in the case of default of the counterparty. For liquidity risk instead of netting sets, we should consider funding sets as discussed in (Albanese and Andersen 2014). To define a funding set consider that each uncollateralized derivative is hedged with a collateralized counterpart (in the same book). The funding set is then the set of derivatives where the collateral can be rehypothecated among the hedges. A funding set is in general much larger than a netting set and may contain all derivative transactions of a legal entity. We refer to (Albanese and Andersen 2014) for a more extensive discussion of funding sets.

In particular, it is unclear whether both liquidity risk and accounting quality separately affect asset prices. Liquidity risk may arise as a result of a lack of high-quality public information about the firm (Diamond & Verrecchia, 1991), yet liquidity risk is also affected by other firm-specific factors that may or may not be associated with information quality, such as the firm’s market beta, growth rate or capital intensity (Pastor & Stambaugh, 2003; Ng, 2011). On the other hand, models that address the effect of information quality on the cost of capital (e.g. Easley & O’Hara, 2004) are sometimes criticized for having unclear or inaccurate predictions regarding whether information quality has a direct or indirect effect on returns, if at all (Lambert et al., 2007; Hughes, Liu & Liu, 2007). The empirical evidence is also mixed and some have questioned whether the negative association between information quality and realized returns observed in FLOS is a result of risk or a market anomaly (Core et al., 2008; Mashruwala & Mashruwala, 2011).

Liquidity management is the ability to assess and manage the demand and supply of liquid funds, which is one of the core functions of any bank (Ismal, 2010a; Majid, 2003). Liquidity management is the ability of the bank to fund increases in assets and meet its obligations when they fall without incurring significant loss in value (BIS, 2008). Banks deal with three types of liqudity – market liqudity, funding liqudity, and central bank liquidity (Nikolaou, 2009). ‘Market liquidity’ refers to the ability to trade an asset at short notice, at low cost and with little impact on its price. ‘Funding liquidity’ refers to the ability of banks to meet their liabilities, unwind or settle their positions should they come due. Central bank liquidity refers to the ability of the central bank to supply liquidity when the financial system is in need of funds. Rochet (2008) highlights three potential causes of liquidity risk. On the liability side, there could be large uncertainty with deposit withdrawals. On the asset side, there is an uncertainty on the volume of new requests for loans, or renewal of old loans that the bank will receive in the future. Thirdly, the uncertainty of ‘off-balance sheet’ transactions, may affect the liquidity of the bank.

The study’s aim is an attempt to determine the liquidity risk of Home Depot Inc which involved two main factors of internal (firm-specific) and external (macroeconomics) factors. These data was interpreted and collected Home Depot annual reports of five year period from 2014 to 2018. There are four risks involved which are credit risk, operational risk, profitability, and market risk. Measurement of current ratio, quick ratio, average -collection period, debt to income ratio, operational ratio, and operating margin are used to examine the overall five years liquidity risk of Home Depot. Hence, to determine the relationship of these risk factors to the company’s liquidity risk, this study used profitability, credit risk, operational risk, market risk, gross domestic products (GDP), inflation, interest rate, exchange rate, BETA, and corporate governance index. SPSS system are used to do data analysis in which by implementing stepwise method which apply the descriptive statistics, correlation, and model summary. Based on the data analysis, we can conclude that operational risk is the most significant to CR since it gives the highest impact to liquidity risk of the company. Nonetheless, the other variables give low impact to the CR and there is no significant related with.

Moreover, when measuring liquidity risk we suggest an empirical model based on a well- known one in risk management that is value at risk. By definition, it expresses the maximum loss supported by a trader when liquidating his/her position within a fixed time interval. The employed methodology was inspired by such studies as Bangia et al. (1999), Le Saout (2001) who applied a value at risk approach using an aggregated intraday data. Dionne et al. (2006), however, carried out the first research that proposed an intraday measure of the value at risk using high frequency data: the IVaR (intraday vale at risk). As a matter of fact, the current work combines the empirical contribution of the three previously cited studies to validate the H1 hypothesis above. Moreover, we propose to test whether liquidity risk is higher for less liquid stocks i.e. H2 hypothesis. In this context, we suggest the use of two subsamples: more and less liquid stocks. Accordingly and in order to validate the value at risk measure, we have to perform a back testing. In this vein, we propose to divide the sample period into two: the first six months’ data will be dedicated to the model’s estimation and forecasting. The second half of the period will be devoted to back testing and validating the value at risk approach.

descriptive statistics of the excess returns of the portfolio containing the three least sensitive currencies to innovation in global liquidity and the portfolio containing the three most sen- sitive ones. The average excess returns and the Sharpe ratios suggest that the liquidity risk premium is substantial. However, the relatively small sample size and cross section –there are now only 3 currencies in each portfolio and about 6 years of monthly observations –prevent us from conducting a statistically meaningful asset pricing test, and hence we cannot estimate the liquidity risk premium using the same methods as in the core analysis. Nevertheless, the di¤erence in excess returns across liquidity-sorted portfolios is very apparent and can be seen even more clearly in the graphical analysis of the cumulative excess returns of the two portfo- lios used in the long-short strategy, in Panel B of Figure 3. This shows that there is an evident widening in the spread of the two portfolio returns after the Lehman collapse, consistent with an increased premium required for liquidity risk and with the evidence described in Melvin and Taylor (2009) and Mancini, Ranaldo and Wrampelmeyer (2011).

Biety (2003) define Liquidity as the ability of an institution to meet demands for funds. Whereas Basel Committee on banking management states that liquidity is the ability of a finance institution to support financially increases in assets and meet debts when due (Sharara, 2014). Liquidity management of a microfinance institution is ensuring that the institution maintains sufficient cash and liquid assets to satisfy client demand for loans and withdrawals. Liquidity management involves a microfinance institution doing a daily analysis of cash inflows and outflows daily and subsequent days to reduce risk that those saving will be unable to access their deposits in the moments they demand them. Therefore, for a micro financial institution to control liquidity, it must have a management information system in place which is able to make pragmatic growth and liquidity projections. The fundamental role of microfinance banks is the maturity transformation of short-term deposits into long-term loans which makes microfinance institutions inherently accessible to liquidity risk (both of an institution-specific nature and that which affects markets in general) (Sharara, 2014).

The researchers used the financial statement analysis to identify the trend of the companies by comparing the ratios within five years period. In the company’s annual report which is in financial statement, there have three main components namely income statement, balance sheet and cash flow statement. In this study, the researchers allowed to measure the performance, liquidity risk, credit risk, operational risk and also market risk of the company.

This type of risk arises out of operational failures such as mismanagement or technical failures. Operational risk can be classified into Fraud Risk and Model Risk. Fraud risk arises due to the lack of controls and Model risk arises due to incorrect model application. (Hussaini, Abu Bakar, & Yusuf, 2019), noted that due to the bank fraud and the fall of world-leading business organizations had triggered scholars and professionals to re-examine the link between fraud risk management, and the bank's performance. They reviewed the relationship between fraud risk management, risk culture, and bank performance by suggesting future research agenda in the area. They revealed that fraud risk management has a positive relationship with bank performance. Similarly, evidence has shown that risk culture influence bank’s performance. As studies that link fraud risk management, risk culture and bank performance are rare this paper will be pioneering in the relationship; this may in a long way aid in making various business decisions.

Interbank business, which provides a new way for banks to avoid regulation, has rapid development in recent years in China. With interbank business size, type expanding, some corresponding problems and risks are exposed. The paper analyzes the main trading patterns of bank-bank cooperation and bank-trust cooperation, concludes that credit risk, operational risk and liquidity risk are particularly prominent in these patterns. Banks should establish a bank customer access and evaluation system to strengthen credit risk control. For operational risk, strict authorization and normative accounting, fine management and complete supervision system would help banks to strengthen the operational risk control. Finally, banks can reduce liquidity risk by adjusting the scale and the proportion of assets and liabilities, using effective risks mitigation tool and strengthening cooperation.

Permanent repository link: http://openaccess.city.ac.uk/13160/ Link to published version: http://dx.doi.org/10.1016/j.jimonfin.2011.11.010 Copyright and reuse: City Research Online aims [r]

Financial exposure problem absolutely will affect the financial performance of the organization, however financial exposure can be manage by using risk management assessment to decrease the losses that might face by company, risk exposure need to be identified because the risk can divided into there are can be managed and uncertainty to occur and risk of financial exposure also may affect short-term and long-term financial transaction. The relationship of cash conversion cycle are related to each other between business activity and performance of the firm organization, working capital, and liquidity risk. Based on the article that publish by Farooq et al (2016) which is about liquidity risk, performance and working capital relationship of cash conversion cycle (CCC), the article show how does cash conversion cycle and profitability works, measuring method that used to measure profitability in this article such as EBIT, ROE, ROI and ROA. The main purpose of the investigation is to define about the risk and return from the business activities, capital structure, investment or dividend of company valuation where need to deals with current liabilities and current asset.

So far, the study on the effect of liquidity on the cross-sectional asset pricing has been unconditional by nature. Although the first research paper finds that a bond’s comovement with market factors is priced, the significance of both market and liquidity risks in explaining bond spreads seems to be time-varying. Further investigation on time-varying liquidity beta and liquidity risk premium can be beneficial. Therefore, the second research paper, “Pricing of Government Bonds around the World and Time- varying Liquidity Risk”, further investigate the effects of liquidity risk and time-varying risk on bond pricing around the world (both developed and emerging countries). We allow both time-series and cross-sectional variations in liquidity betas. Using a regime switching model, we find that the transition from the low to the high liquidity-beta state can be predicted by a decline in U.S. equity market performance (as a proxy for the world economy) and also by a rise in the global bond market volatility. The results suggest that the liquidity risk or liquidity beta is time varying across two different regimes: it increases in times of high uncertainty and is always larger in emerging than in developed countries. This is consistent with the results for U.S. equity markets: Watanabe and Watanabe (2008) report that the high-beta state for U.S. equities is associated with high equity volatility and preceded by a period of declining expectations about future market liquidity. Nevertheless, in the cross-sectional analyses, the price of risk or premium required by investors for holding this time-varying risk is relatively stable.

2 risk which is an aggressive collection program and the implementation of credit limit for mass- market products. This has resulted TM to have a decreasing number of total bad debts. The Credit Management Assessment System has helped to monitor credit rating, allocate credit limit, monitors usage against credit limit, and monitor the customer payment behavior which then allows TM to do further analysis. TM is also exposed to market risk and to mitigate this risk TM established risk management policies, guidelines, and procedures. Therefore, hedging strategies are used to mitigate these exposures which are foreign exchange risk, price risk and interest rate risk. Tm is exposed to liquidity risk hence they actively monitor and control these exposures. TM ensures that they have adequate deposits with financial institutions and cash and bank balances that are always readily available and can liquidate easily to meet any payment obligation when it is due.