Th. doct.--Econ.--Florence--European University institute, 1998. / Notes bibliogr.

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This paper uses a multi-factor Arbitrage Pricing model to measure the systematic risks of U.S. Foreign Direct Investments (FDI) in the largest emerging markets of Latin America: Argentina, Brazil, Chile, and Mexico. The Arbitrage Pricing Theory (APT) states that returns on investments are exposed to and affected by a number of economy-wide factors or risks. Moreover, risk is defined as the potential losses due to the unanticipated or unexpected changes in the systematic risk factors . Because the unexpected changes in those factors account for the discrepancies between expected and actual returns, we can measure systematic risk by using traditional econometrics and multivariable analysis. Essentially, APT postulates expected returns are a linear function of unexpected changes in various regressors. The magnitude and sign of the coefficients generated provide a way to obtain a dollar denominated time explicit measure of risk. This model is estimated with a variety of estimators and it identifies four risk factors: the annual growth rates of Gross Domestic Product (GDP), money supply (M1), total exports, and total external debt, as determinants of returns. The Ordinary Least Square (OLS) results are somewhat robust--three out of four factors have the expected sign, thus supporting the hypothesis. GLS procedures reveal similar results. / Master of Arts

Tang Kwong-leung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 83-84). / Abstracts in English and Chinese. / Abstract --- p.ii / 摘要 --- p.iii / Acknowledgment --- p.iv / Table of Contents --- p.v / Chapter Chapter 1 --- Introduction / Chapter 1.1 --- Modern View of Arbitrage --- p.1 / Chapter 1.2 --- Arbitrage Opportunity in China Stock Market --- p.4 / Chapter Chapter 2 --- The China Stock Market / Chapter 2.1 --- Stock Trading --- p.7 / Chapter 2.2 --- Segmentation of the Market --- p.8 / Chapter 2.3 --- Listed Companies --- p.10 / Chapter 2.4 --- Literature Review --- p.11 / Chapter 2.5 --- Liberalization of B Shares --- p.14 / Chapter 2.6 --- Operational Difference between A-Share and B-Share Trading --- p.16 / Chapter Chapter 3 --- The Data / Chapter 3.1 --- A Shares versus B Shares --- p.18 / Chapter 3.2 --- Definition of Discount --- p.19 / Chapter 3.3 --- A Shares versus H Shares --- p.23 / Chapter Chapter 4 --- Test for The Existence of Structural Break / Chapter 4.1 --- The Methodology --- p.26 / Chapter 4.2 --- The Empirical Findings --- p.28 / Chapter Chapter 5 --- Explanations for Limited Arbitrage / Chapter 5.1 --- Absence of Free Convertibility of Renminbi --- p.29 / Chapter 5.1.1 --- China's Foreign Exchange Control and Regulation --- p.30 / Chapter 5.1.2 --- Current Account versus Capital Account --- p.31 / Chapter 5.1.3 --- Impact of Foreign Exchange Control on Price Discount --- p.32 / Chapter 5.1.4 --- Free Convertibility of Foreign Exchange in Hong Kong --- p.33 / Chapter 5.1.4.1 --- The Data --- p.34 / Chapter 5.1.4.2 --- Definition of Discount --- p.34 / Chapter 5.1.4.3 --- Basis Idea --- p.35 / Chapter 5.1.4.4 --- The Empirical Findings --- p.37 / Chapter 5.2 --- Investment Returns of A Shares and B Shares --- p.37 / Chapter 5.2.1 --- The Data --- p.38 / Chapter 5.2.2 --- Foreign Exchange Factor --- p.42 / Chapter 5.2.3 --- Basic Idea --- p.43 / Chapter 5.2.4 --- Different Sets of Returns --- p.44 / Chapter 5.2.5 --- The Empirical Findings --- p.47 / Chapter 5.2.6 --- Irrelevant Difference in Liquidity between A Shares and B Shares --- p.51 / Chapter Chapter 6 --- Conclusion --- p.52 / Tables / Table 1 Share Structure of PRC Issuers --- p.54 / Table 2 Descriptive Statistics of Different Types of PRC Issuers --- p.55 / Table 3 Trading Summary of PRC Issuers --- p.56 / Table 4 Lists of Companies

Informed traders trade options on underlying securities to lower transaction costs and increase financial leverage for price trend and variance strategies. Options markets play a significant role in price discovery by incorporating private information about future prices for an underlying security into option prices. I generate a new model-free volatility measure to calculate the "distance from arbitrage bounds" from minute-by-minute option series for the S&P 500 index and 30 individual underlying stocks. These iron butterfly arbitrage bounds (IBBs) use intraday call and put option prices from the Bloomberg database. Narrow and wide IBBs are expected to reveal the options market valuation of volatility by market participants. Data series is gathered by using successive one-minute intervals from the Bloomberg database. The data comprise the most recent bid and ask option prices and volumes. I collect S&P 500 index values and index options and use 30 underlying stock prices and option prices for the contracts that have the largest option trading volume during the sampling interval. These bid and ask prices reflect the information generated by intraday price pressures implied by S&P 500 index options or stock options. Consistent with the option micro-structure literature, I find that the IBB measure for actively traded stock options attains its highest level immediately after the open of the market, declines steadily throughout the first trading hour and remains relatively stable until market close. However, index IBBs behave differently. S&P 500 index option IBB attains its lowest level during the first hour of the trading day, then increases and remains relatively stable until market close. I present new evidence regarding the dynamic relation between stock returns and innovations in expected volatility by using the minute-by-minute change in implied volatility (IV) as a proxy. Unlike the relationship between individual stock returns and their respective changes in implied idiosyncratic volatility, I find that all the coefficients on the market volatility index (VIX) term are negative and significant. Therefore, the evidence supports the explanation that the negative relationship between stock returns and expected volatility innovations is primarily related to the systematic component of the expected volatility. I also test whether narrow and wide IBB values capture incremental information to explain the return-volatility relationship. Results indicate that neither narrow IBB nor wide IBB values provide additional information beyond that provided by VIX and IV. The results are robust to five-minute and ten-minute sampling frequencies.

A thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy (by production of original research) in Mathematics School of Mathematics University of the Witwatersrand Johannesburg December 2012 / Generating functions and asymptotic analysis have been used in four di er- ent situations to establish new results for extremely well studied structures. Later in this thesis a more detailed individual abstract for each of these studies is provided. The four situations are: A. Durfee square areas in integer partitions. B. A study of the relationship between integer compositions and their constituent partitions by specifying the asymptotic expectation of the number of such partitions in arbitrary composition. C. Similar to B above but focusing more on the generating functions rather than on the expectations derived therefrom. D. In the area of plane partitions with additional structure imposed upon them.

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).

Abstract As Convertible Bonds (CB) market increasingly prospers in recent years, not only has it become one of the most important financing tools for public firms, but also the popular investment target for investors. However, given the complexity of Convertible Bonds Issuance Terms, coexistence of new and old regulations, and difficulties in obtaining relevant information, investors know little about CB in terms of its investment restrictions as well as arbitrage opportunities and models. This study attempts to explore arbitrage strategies for CB¡¦s trading and investing, and to design a CB ¡§Information System¡¨ which severs as a CB database querying system, and helps enhance the efficiency of CB investment and arbitrage. In this study, three CB related information systems were developed after analyzing CB issuance information and market price: 1. ¡§Database Querying System,¡¨ which is used to search for arbitrage restrictions and related information before engaging in CB investment and arbitrage. 2. ¡§Put Provision Instant Quoting System,¡¨ which is able to receive market quotation before exchange deadline, and to instantly calculate the rate of return in put provision. 3. ¡§Arbitrage Instant Quoting System,¡¨ like ¡§Put Provision Instant Quoting System,¡¨ can promptly calculate the rate of return in CB and considerably increase operating efficiency and the rate of return in arbitrage. Based on the case study and empirical research, this study argues that these three information systems can practically help control the fluctuation of market price, enhance operating efficiency, and serve as an effective financial operating tool of CB arbitrage strategies. Accordingly, several conclusions of this study are presented as follows: 1. The current stock market is inefficient, so the arbitrage opportunities are still available. 2. When market is thriving and stock price exceeds CB price, there are more arbitrage opportunities for and higher rate of return in CB. 3. While investing put provision for CB often accompanies high return opportunities, investors are suggested to gingerly evaluate the finance risks of public firms beforehand, such as making use of ¡§Instant Quoting System¡¨ to operate, which is helpful to controlling market price effectively. 4. To avoid operating risks, Investors should recognize all kinds of issuance restrictions in advance. 5. Conducting ¡§Instant Quoting System¡¨ can obtain market information quickly and promote operation efficiency and the rate of return.

Texte remanié de: Th. doct.--Droit--Aix-Marseille 3, 2003. / Bibliogr. p. 491-565. Index.

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).