996 resultados para Liquidity risk


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Systematic liquidity shocks should affect the optimal behavior of agents in financial markets. Indeed, fluctuations in various measures of liquidity are significantly correlated across common stocks. Accordingly, this paper empirically analyzes whether Spanish average returns vary cross-sectionally with betas estimated relative to two competing liquidity risk factors. The first one, proposed by Pastor and Stambaugh (2002), is associated with the strength of volume-related return reversals. Our marketwide liquidity factor is defined as the difference between returns highly sensitive to changes in the relative bid-ask spread and returns with low sensitivities to those changes. Our empirical results show that neither of these proxies for systematic liquidity risk seems to be priced in the Spanish stock market. Further international evidence is deserved.

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We examine the role of liquidity risk, both as a stock characteristic as well as systematic liquidity risk, in UK mutual fund performance for the first time. Using four alternative measures of stock liquidity we extract principal components across stocks in order to construct systematic or market liquidity factors. We find that on average UK mutual funds are tilted towards liquid stocks (except for small stock funds as might be expected) but that, counter-intuitively, liquidity as a stock characteristic is positively priced in the cross-section of fund performance. We find that systematic liquidity risk is positively priced in the cross-section of fund performance. Overall, our results reveal a strong role for stock liquidity level and systematic liquidity risk in fund performance evaluation models.

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This paper proposes a new non-parametric method for estimating model-free, time-varying liquidity betas which builds on realized covariance and volatility theory. Working under a liquidity-adjusted CAPM framework we provide evidence that liquidity risk is a factor priced in the Greek stock market, mainly arising from the covariation of individual liquidity with local market liquidity, however, the level of liquidity seems to be an irrelevant variable in asset pricing. Our findings provide support to the notion that liquidity shocks transmitted across securities can cause market-wide effects and can have important implications for portfolio diversification strategies. ©2012 Elsevier B.V. All rights reserved.

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Field lab: Business project

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The Chinese stock market is an order-driven market and hence its characteristics are structurally different from quote-driven markets. There are no studies that consider the role of the market liquidity risk factor in determining cross-sectional stock returns in a model including financial market anomalies for order-driven markets. Our aim is to test whether financial market anomalies such as firm size, the book-to-market ratio, the turnover rate, and momentum both with and without the inclusion of the market liquidity risk factor in the case of the Chinese stock market can explain cross-sectional stock returns. The empirical framework is based on the model proposed by Avramov and Chordia (AC, 2006). Our main finding is that the AC model can capture financial market anomalies except momentum when we include the market liquidity risk factor on the Chinese stock market.

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The crisis that unfolded in 2007/2008 turned the attention of the financial world toward liquidity, the lack of which caused substantial losses. As a result, the need arose for the traditional financial models to be extended with liquidity. Our goal is to discover how Hungarian market players relate to liquidity. Our results are obtained through a series of semi-structured interviews, and are hoped to be a starting point for extending the existing models in an appropriate way. Our main results show that different investor groups can be identified along their approaches to liquidity, and they rarely use sophisticated models to measure and manage liquidity. We conclude that although market players would have access to complex liquidity measurement and management tools, there is a limited need for these, because the currently available models are unable to use complex liquidity information effectively.

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The crisis that unfolded in 2007/2008 turned the attention of the financial world toward liquidity, the lack of which caused substantial losses. As a result, the need arose for the traditional financial models to be extended with liquidity. Our goal is to discover how Hungarian market players relate to liquidity. Our results are obtained through a series of semistructured interviews, and are hoped to be a starting point for extending the existing models in an appropriate way. Our main results show that different investor groups can be identified along their approaches to liquidity, and they rarely use sophisticated models to measure and manage liquidity. We conclude that although market players would have access to complex liquidity measurement and management tools, there is a limited need for these, because the currently available models are unable to use complex liquidity information effectively.

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A basic intuition is that arbitrage is easier when markets are most liquid. Surprisingly, we find that momentum profits are markedly larger in liquid market states. This finding is not explained by variation in liquidity risk, time-varying exposure to risk factors, or changes in macroeconomic condition, cross-sectional return dispersion, and investor sentiment. The predictive performance of aggregate market illiquidity for momentum profits uniformly exceed that of market return and market volatility states. While momentum strategies are unconditionally unprofitable in US, Japan, and Eurozone countries in the last decade, they are substantial following liquid market states.

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This thesis aims to investigate pricing of liquidity risks in London Stock Exchange. Liquidity Adjusted Capital Asset Pricing Model i.e. LCAPM developed by Acharya and Pedersen (2005) is being applied to test the influence of various liquidity risks on stock returns in London Stock Exchange. The Liquidity Adjusted Capital Asset Pricing model provides a unified framework for the testing of liquidity risks. All the common stocks listed and delisted for the period of 2000 to 2014 are included in the data sample. The study has incorporated three different measures of liquidity – Percent Quoted Spread, Amihud (2002) and Turnover. The reason behind the application of three different liquidity measures is the multi-dimensional nature of liquidity. Firm fixed effects panel regression is applied for the estimation of LCAPM. However, the results are robust according to Fama-Macbeth regressions. The results of the study indicates that liquidity risks in the form of (i) level of liquidity, (ii) commonality in liquidity (iii) flight to liquidity, (iv) depressed wealth effect and market return as well as aggregate liquidity risk are priced at London Stock Exchange. However, the results are sensitive to the choice of liquidity measures.

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This paper investigates the role of credit and liquidity factors in explaining corporate CDS price changes during normal and crisis periods. We find that liquidity risk is more important than firm-specific credit risk regardless of market conditions. Moreover, in the period prior to the recent “Great Recession” credit risk plays no role in explaining CDS price changes. The dominance of liquidity effects casts serious doubts on the relevance of CDS price changes as an indicator of default risk dynamics. Our results show how multiple liquidity factors including firm specific and aggregate liquidity proxies as well as an asymmetric information measure are critical determinants of CDS price variations. In particular, the impact of informed traders on the CDS price increases when markets are characterised by higher uncertainty, which supports concerns of insider trading during the crisis.

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The study examines the impact of liquidity risk on freight derivatives returns. The Amihud liquidity ratio and bid–ask spreads are utilized to assess the existence of liquidity risk in the freight derivatives market. Other macroeconomic variables are used to control for market risk. Results indicate that liquidity risk is priced and both liquidity measures have a significant role in determining freight derivatives returns. Consistent with expectations, both liquidity measures are found to have positive and significant effects on the returns of freight derivatives. The results have important implications for modeling freight derivatives, and consequently, for trading and risk management purposes.

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How do the liquidity functions of banks affect investment and growth at different stages of economic development? How do financial fragility and the costs of banking crises evolve with the level of wealth of countries? We analyze these issues using an overlapping generations growth model where agents, who experience idiosyncratic liquidity shocks, can invest in a liquid storage technology or in a partially illiquid Cobb Douglas technology. By pooling liquidity risk, banks play a growth enhancing role in reducing inefficient liquidation of long term projects, but they may face liquidity crises associated with severe output losses. We show that middle income economies may find optimal to be exposed to liquidity crises, while poor and rich economies have more incentives to develop a fully covered banking system. Therefore, middle income economies could experience banking crises in the process of their development and, as they get richer, they eventually converge to a financially safe long run steady state. Finally, the model replicates the empirical fact of higher costs of banking crises for middle income economies.

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A 2007-ben kezdődő pénzügyi eredetű világgazdasági válság nyilvánvalóvá tette a kapcsolatot a likviditás két fajtája, a finanszírozási és a piaci likviditás között. A cikk megismertet a piaci likviditással kapcsolatos olyan alapvető fogalmakkal, mint order flow, ajánlati könyv, piaci struktúrák, bemutatja a piaci likviditás dimenzióit, a likviditás néhány mutatószámát és a piaci likviditás stilizált tényeit. A banki likviditáskezelés rövid összefoglalásán túl bevezetést nyújt a portfóliók likviditáskockázat melletti értékelésébe, végezetül összefoglalja, hogy az alapvetően finanszírozási likviditási kockázatnak kitett nem pénzügyi vállalatok hogyan vehetik figyelembe döntéseik meghozatalakor a piaci likviditást. _____________________ The global financial crisis starting in 2007 has showed that the connection between market liquidity and funding liquidity is apparent. This paper introduces the basic notions regarding market liquidity such as order flow, order book, and market structures. The article also presents the various dimensions of market liquidity, several measures of liquidity and the stylized facts of market liquidity. Besides a short description of liquidity management of banks it briefly introduces portfolio valuation in the presence of liquidity risk. Finally, the paper gives insight to non-financial companies, fundamentally exposed to funding liquidity risk, on how to consider market liquidity in their decisions.

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Although risk management can be justified by financial distress, the theoretical models usually contain hedging instruments free of funding risk. In practice, management of the counterparty risk in derivative transactions is of enhanced importance, consequently not only is trading on exchanges subject to the presence of a margin account, but also in bilateral (OTC) agreements parties will require margins or collateral from their partners in order to hedge the mark-tomarket loss of the transaction. The aim of this paper is to present and compare two models where the financing need of the hedging instrument also appears, influencing the hedging strategy and the optimal hedging ratio. Both models contain the same source of risk and optimisation criterion, but the liquidity risk is modelled in different ways. In the first model, there is no additional financing resource that can be used to finance the margin account in case of a margin call, which entails the risk of liquidation of the hedging position. In the second model, the financing is available but a given credit spread is to be paid for this, so hedging can become costly.