<p>Fisher Black and Myron Scholes (Black and Scholes, 1973) presented in 1973 a valuation model for options that was intuitive and user friendly. This revolutionized the option market and made pricing an option easy.</p><p> </p><p>To get a sound understanding of liquidity risk we have to specify and describe liquidity (Matz and Neu, 2007, p.5). Market liquidity and funding liquidity are two kinds of liquidity. Market liquidity can be described as good when a security is easy to trade. Easy to trade is defined as small bid ask spread, small price impact and high resilience. If a bank or investor have good funding liquidity they have good availability of funds by their own capital or from loans.</p><p> </p><p>The meaning of liquidity risk can be divided into two major risks; market liquidity risk and funding liquidity risk (Pedersen, 2008). Market liquidity risk is the risk that the market liquidity gets worse when a trade needs to be made and this is the risk focused on in this paper.</p><p> </p><p>Do liquidity costs in option pricing theory exist and does it depend on a real supply curve?</p><p> </p><p>The main objective in this paper is to show if liquidity risk has a significant impact on option price and depends on a real supply curve. The study is based on theory and conclusions from earlier research. Built from Jarrow and Protters work in liquidity risk in option price (Jarrow and Protter, 2007) a model for the supply curve is derived.</p><p> </p><p>The scientific ideal in this paper has a clear positivistic approach. The quantitative method is my choice not only because of the link between positivism, deduction and quantitative methods but gives a certain advantage when considering this problem formulation and the type of data accessible. A model is derived with a base from a model derived by Jarrow and Protter (2007) and used to show the impact of liquidity risk in the option pricing theory.</p><p> </p><p>The result presented in this paper has shown that liquidity costs exist in theory and in practice, and this cost are binding. This model can be used to get exact costs in a specific case with a specific option and can help brokers to know the real liquidity risk they are exposed to.</p>

Fisher Black and Myron Scholes (Black and Scholes, 1973) presented in 1973 a valuation model for options that was intuitive and user friendly. This revolutionized the option market and made pricing an option easy.   To get a sound understanding of liquidity risk we have to specify and describe liquidity (Matz and Neu, 2007, p.5). Market liquidity and funding liquidity are two kinds of liquidity. Market liquidity can be described as good when a security is easy to trade. Easy to trade is defined as small bid ask spread, small price impact and high resilience. If a bank or investor have good funding liquidity they have good availability of funds by their own capital or from loans.   The meaning of liquidity risk can be divided into two major risks; market liquidity risk and funding liquidity risk (Pedersen, 2008). Market liquidity risk is the risk that the market liquidity gets worse when a trade needs to be made and this is the risk focused on in this paper.   Do liquidity costs in option pricing theory exist and does it depend on a real supply curve?   The main objective in this paper is to show if liquidity risk has a significant impact on option price and depends on a real supply curve. The study is based on theory and conclusions from earlier research. Built from Jarrow and Protters work in liquidity risk in option price (Jarrow and Protter, 2007) a model for the supply curve is derived.   The scientific ideal in this paper has a clear positivistic approach. The quantitative method is my choice not only because of the link between positivism, deduction and quantitative methods but gives a certain advantage when considering this problem formulation and the type of data accessible. A model is derived with a base from a model derived by Jarrow and Protter (2007) and used to show the impact of liquidity risk in the option pricing theory.   The result presented in this paper has shown that liquidity costs exist in theory and in practice, and this cost are binding. This model can be used to get exact costs in a specific case with a specific option and can help brokers to know the real liquidity risk they are exposed to.

Arbitrageurs play an important role in keeping market prices close to their fundamental values by providing market liquidity. Most arbitrageurs however use leverage. When funding conditions worsen they are forced to reduce their positions. The resulting selling pressure depresses market prices, and in certain situations, pushes arbitrage spreads to levels exceeding many standard deviations. This phenomenon drove many century old financial institutions into bankruptcy during the 2007−2009 financial crisis. In this thesis, we provide empirical evidence and demonstrate analytically the effects of funding liquidity on arbitrage. We further discuss the implications for risk management. To conduct our empirical studies, we construct a novel Funding Liquidity Stress Index (FLSI) using principal components analysis. Its constituents are measures representing various funding channels. We study the relationship between the FLSI index and three di↵erent arbitrage strategies that we reproduce with real and daily transactional data. We show that the FLSI index has a strong explanatory power for changes in arbitrage spreads, and is an important source of contagion between various arbitrage strategies. In addition, we perform “event studies” surrounding events of changing margin requirements on futures contracts. The “event studies” provide empirical evidence supporting important assumptions and predictions of various theoretical work on market micro-structure. Next, we explain the mechanism through which funding liquidity affects arbitrage spreads. To do so, we study the liquidity risk premium in a market micro-structure framework where market prices are determined by the supply and demand of securities. We extend the model developed by Brunnermeier and Pedersen [BP09] to multiple periods and generalize their work by considering all market participants to be risk-averse. We further decompose the liquidity risk premium into two components: 1) a fundamental risk premium and 2) a systemic risk premium. The fundamental risk premium compensates market participants for providing liquidity in a security whose fundamental value is volatile, while the systemic risk premium compensates them for taking positions in a market that is vulnerable to funding liquidity. The first component is therefore related to the nature of the security while the second component is related to the fragility of the market micro-structure (such as leverage of market participants and margin setting mechanisms).

¡@¡@Treasury stock system allows listed companies to buy back their own shares in the open market. In general, when the company announced stock repurchase event, investors are optimistic about the prospects of the company. Therefore, the company's stock price usually rises. But will investors buy more stocks, leading to increased liquidity of stocks? This study combines with stock repurchase and liquidity to investigate the impact of stock repurchase on liquidity. Further events will be studied by different factors stratified, including firm size, stock price, industry, the purpose of stock repurchase, the proportion of execution, the holding ratio of insiders and institutional investors. By using three types of liquidity measures, the study is to observe the changes of liquidity of stocks in the different situations. ¡@¡@After conducting mean difference in pair-sample test, the empirical results indicate that the sample stocks in the stock repurchase announcement, the outcome supports liquidity increase hypothesis. In terms of the stratification factors, the smaller of the company size or lower stock price will help increase the liquidity of the stock in the market. Non-electronics sector, aims to buy back equity write-off shares can improve market liquidity. Higher or lower percentage of insider ownership shares in¡@companies will lead to the increase of stock liquidity. Higher holding shares proportion of institutional investor in companies will increase liquidity. The amount of execution ratio is of no factors, but the liquidity of the stock repurchase still supports the liquidity increase hypothesis.

Thesis (Ph. D.)--University of Washington, 2001. / Vita. Includes bibliographical references (leaves 94-100).

This thesis is an empirical analysis which is focussed on the potential relationship between liquidity and asset pricing; where its key objective is to provide an assessment about the role for liquidity in asset pricing models. The data sample covers the United Kingdom from 1987 to 2009 and the methodological approaches include; Fama and MacBeth (1973) cross section regressions; time series regression analysis; factor analysis; and, non-nested testing. Several liquidity measures are compared, including the Amivest, the Hui-Heubel and the Amihud measures of liquidity. The role of unexpected liquidity and monetary policy is also considered. Building on earlier findings in the thesis, a deeper examination of the role of liquidity in explicit asset pricing frameworks, such as the capital asset pricing model and the Fama-French three factor model, then takes place through incorporation of the Hui- Heubel and Amihud measures of liquidity. Overall, the results suggest that conditions of declining liquidity (rising illiquidity) appear to be associated with increasing risk premia. This observation appears also to apply when portfolios are sorted by size. Finally, the conclusion is reached that modelling liquidity within an asset pricing framework is likely to be very useful, particularly given the changes to the financial market horizon where liquidity as a concept has come increasingly to the fore because of current government policies associated with quantitative easing.

Using permanent income life-cycle theory, I analyze the effects of liquidity constraints on the household’s ability to smooth life time consumption due to a change in housing and stock market wealth. Using data from the Canadian national accounts and chartered bank balance sheets I test if improved access to housing wealth due to fundamental shifts to the banking industry in the 1980s has lowered liquidity constraints and improved the household’s ability to smooth consumption.

Arbitrageurs play an important role in keeping market prices close to their fundamental values by providing market liquidity. Most arbitrageurs however use leverage. When funding conditions worsen they are forced to reduce their positions. The resulting selling pressure depresses market prices, and in certain situations, pushes arbitrage spreads to levels exceeding many standard deviations. This phenomenon drove many century old financial institutions into bankruptcy during the 2007−2009 financial crisis. In this thesis, we provide empirical evidence and demonstrate analytically the effects of funding liquidity on arbitrage. We further discuss the implications for risk management. To conduct our empirical studies, we construct a novel Funding Liquidity Stress Index (FLSI) using principal components analysis. Its constituents are measures representing various funding channels. We study the relationship between the FLSI index and three di↵erent arbitrage strategies that we reproduce with real and daily transactional data. We show that the FLSI index has a strong explanatory power for changes in arbitrage spreads, and is an important source of contagion between various arbitrage strategies. In addition, we perform “event studies” surrounding events of changing margin requirements on futures contracts. The “event studies” provide empirical evidence supporting important assumptions and predictions of various theoretical work on market micro-structure. Next, we explain the mechanism through which funding liquidity affects arbitrage spreads. To do so, we study the liquidity risk premium in a market micro-structure framework where market prices are determined by the supply and demand of securities. We extend the model developed by Brunnermeier and Pedersen [BP09] to multiple periods and generalize their work by considering all market participants to be risk-averse. We further decompose the liquidity risk premium into two components: 1) a fundamental risk premium and 2) a systemic risk premium. The fundamental risk premium compensates market participants for providing liquidity in a security whose fundamental value is volatile, while the systemic risk premium compensates them for taking positions in a market that is vulnerable to funding liquidity. The first component is therefore related to the nature of the security while the second component is related to the fragility of the market micro-structure (such as leverage of market participants and margin setting mechanisms).

This paper investigates the price impact cost for MSCI constituents on the Taiwan Stock Exchange (TSE) from Jan. 2001 to Dec. 2004. While the behavior of price impact cost in U.S. security markets has been extensively analyzed, there are few studies about it in the pure limit-order markets. Unlike Breen, Hodrick, and Korajczyk (2002), a panel data model is applied to fit our cross-sectional and time series data. We find that the price impact cost is well predicted by predetermined firm characteristics and exhibits a Ushaped pattern over the trading day. Furthermore, the evidence suggests that the reformations of trading regulations and the improvements of information disclosures would have a significant effect on the price impact cost for overall stocks.

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.