by Mok Man-fai, Mansfield. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves 49-50. / ABSTRACT --- p.i / ACKNOWLEDGEMENT --- p.ii / TABLE OF CONTENTS --- p.iii / Chapter / Chapter I. --- INTRODUCTION --- p.1 / Chapter II. --- EURODOLLAR FUTURES AND OPTIONS --- p.3 / Eurodollar Futures --- p.3 / Hedging With Eurodollar Futures --- p.4 / Options On Eurodollar Futures --- p.5 / Contract Type --- p.5 / Contract Style --- p.6 / Contract Lifespan --- p.6 / Contract Value --- p.6 / Hedging With Eurodollar Options --- p.7 / Naked Positions --- p.7 / Chapter III. --- ASSET/LIABILITY MANAGEMENT --- p.9 / Gap Concept --- p.10 / Gap Analysis --- p.11 / Types of Gaps --- p.12 / Positive And Negative Gaps --- p.13 / Voluntary And Involuntary Gaps --- p.13 / Chapter IV. --- HEDGING THE GAP --- p.14 / Macro Hedge --- p.14 / Micro Hedge --- p.17 / Macro Hedge vs Micro Hedge --- p.17 / Chapter V. --- HEDGING METHODOLOGY --- p.19 / Cross Hedge Basis Risk --- p.20 / Hedge Ratio --- p.20 / Time Basis Risk . . --- p.21 / Basic Hedge With No Time Basis Risk --- p.23 / Example 1: Single 90-Day Gap --- p.24 / Example 2: Single 30-Day Gap --- p.24 / Example 3: Single 180-Day Gap --- p.25 / Example 4: Series of 90-day gaps --- p.25 / Example 5: Series of 30-Day Gaps --- p.26 / Basic Hedge With Time Basis Risk --- p.27 / Hedging Of A Series Of Liability Issues --- p.32 / Strip hedge --- p.32 / Stack hedge --- p.33 / Chapter VI. --- OPTIONS AND FUTURES --- p.35 / Similarities and Differences --- p.35 / Merits And Demerits --- p.37 / Chapter VII. --- REASONS FOR HEDGING --- p.39 / Merits --- p.39 / Demerits --- p.40 / Chapter VIII. --- THE SITUATION IN HONG KONG --- p.42 / Chapter IX. --- CONCLUSION --- p.45 / APPENDIX --- p.47 / BIBLIOGRAPHY --- p.49

by Wong Man Kit, Andy, Yu Miu Ki. / Thesis (M.B.A.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 53-54). / ABSTRACT --- p.ii / ACKNOWLEDGEMENT --- p.iii / TABLE OF CONTENTS --- p.iv / Chapter / Chapter I. --- INTRODUCTION --- p.1 / Credit Risk --- p.2 / Operational risk --- p.3 / Liquidity risk --- p.3 / Legal risk --- p.3 / Market Risk --- p.3 / Model risk --- p.4 / Chapter II. --- LITERATURE REVIEW --- p.5 / Value at Risk (VaR) --- p.5 / Minimum Variance --- p.7 / Dollar equivalence --- p.8 / Statistical Hedging --- p.8 / Risk and Return in an Imperfect Hedge --- p.8 / Expected return and standard deviation in a hedged position --- p.9 / Risk and Return in an actual hedge --- p.11 / Optimal Hedge Ratio --- p.13 / Deriving Optimal Hedge Ratio h* --- p.15 / Computing the minimum risk hedge ratio by regression --- p.16 / Basis Risk --- p.18 / Sources of Basis Risk --- p.19 / Variation in the equilibrium price relationship between cash and futures --- p.19 / "Random ""noise"" in the price process" --- p.19 / Mismatch between cash position and the underlying for the future --- p.20 / Hedging Effectiveness --- p.21 / Chapter III. --- DATA AND METHODOLOGY --- p.25 / Data --- p.25 / Data Collection --- p.25 / Data Selection --- p.25 / Data Manipulation --- p.26 / Methodology --- p.27 / Part I: The Selection of the Portfolios --- p.27 / Part II: The Determination of the Hedge Ratio --- p.28 / Part III: Hedged vs. Unhedged --- p.29 / Part IV: Data Analysis & Comparison --- p.31 / Chapter IV. --- FINDINGS --- p.35 / High volatility of Hong Kong market --- p.35 / Manipulation of institutional investors --- p.36 / Hong Kong financial market are less mature --- p.36 / Less efficient information flow --- p.37 / Less Sophisticated Investors --- p.38 / Results and Discussion --- p.39 / Empirical Results --- p.40 / Explanation for the differences --- p.42 / Limitations --- p.47 / Learning Period --- p.47 / Cross Hedging --- p.47 / Mismatch between the futures and the underlying index --- p.48 / Missing Stock Data in the S&P500 --- p.49 / Chapter V. --- CONCLUSION --- p.50 / Tradeoff between risk and return --- p.50 / Hedge Effectiveness --- p.51 / BIBLIOGRAPHY --- p.53

No description available.

No description available.

Thesis (M.M. (Finance & Investment))--University of the Witwatersrand, Faculty of Commerce, Law and Management, Graduate School of Business Administration, 2013. / Recent unexpected variability in the earnings of agribusinesses in South Africa has led stakeholders to ask as to why projected financial performance tended to be so different from the actual results achieved. This paper aims to make an empirical contribution to the discussion on the effects of soft commodity price volatility on the returns of entities whose major business involves derivatives trading in agricultural commodity products. Firstly, mathematical models for commodity price volatility are determined for the major agricultural commodities on the South African Futures Exchange (SAFEX) using the autoregressive conditional heteroskedasticity (ARCH) and the generalised autoregressive conditional heteroskedasticity (GARCH) type of approaches. Secondly, the study then seeks to ascertain whether there are causality links between the commodity price volatility and the returns or earnings realised by selected agribusinesses over time. The paper then discusses some trading strategies that are applicable given that commodity price volatility can be forecasted using the statistical models identified under the study.

Doctor of Philosophy / This dissertation presents the results of three empirical studies on price formation and liquidity in financial futures markets. The research entails three related areas: the effect of taxes on the prices of Australian stock index futures; the efficiency of the information transmission mechanism between the cash and futures markets; and the price and liquidity impact of large trades in interest rate and equity index futures markets. An overview of previous research identifies some important gaps in the existing literature that this dissertation aims to resolve for the benefit of arbitrageurs, investment managers, brokers and regulators.

Thesis (M. Phil.)--University of Hong Kong, 1999. / Also available in print.

<p>Studien ämnar ge en beskrivande bild av hur hedgefonder använder futures och CFDs och till vilket syfte. Författarna avser även att genomföra en jämförelse mellan instrumenten för att ge en ökad förståelse för hur CFDs kan användas som instrument och vilka strategier som är mest lämpade beroende på mål och riskpreferenser.</p><p>Studien är byggd på det deduktiva synsättet. För insamling av det empiriska materialet har en kvalitativ metod används varvid fyra telefonintervjuer genomfördes. Teorin har hämtats från flertalet källor där både artiklar, böcker och Internet har använts.</p><p>De strategier som användes av hedgefonderna i studien var Förvaltade terminer, Global makro samt Marknadsneutral. Ingen av hedgefonderna använde sig av CFDs, detta berodde enligt hedgefonderna på lågt utbud av underliggande varor samt bristande likviditet. Anledningen till varför de använde futures var de låga transaktionskostnaderna, stora utbudet av underliggande varor samt den höga likviditeten.</p>

Studien ämnar ge en beskrivande bild av hur hedgefonder använder futures och CFDs och till vilket syfte. Författarna avser även att genomföra en jämförelse mellan instrumenten för att ge en ökad förståelse för hur CFDs kan användas som instrument och vilka strategier som är mest lämpade beroende på mål och riskpreferenser. Studien är byggd på det deduktiva synsättet. För insamling av det empiriska materialet har en kvalitativ metod används varvid fyra telefonintervjuer genomfördes. Teorin har hämtats från flertalet källor där både artiklar, böcker och Internet har använts. De strategier som användes av hedgefonderna i studien var Förvaltade terminer, Global makro samt Marknadsneutral. Ingen av hedgefonderna använde sig av CFDs, detta berodde enligt hedgefonderna på lågt utbud av underliggande varor samt bristande likviditet. Anledningen till varför de använde futures var de låga transaktionskostnaderna, stora utbudet av underliggande varor samt den höga likviditeten.

Using seventeen years of data from 1991 to 2008, I derive a pricing model for farmland values. This valuation model is the first using agricultural commodity futures as a proxy for “ex ante” income projections for the crops grown or livestock grazed on United States farmland. While not all inclusive, the model is tested regionally including the Corn Belt, Delta States, Lake States, Mountain, Pacific Northwest, Pacific West and Southeast Regions. Additionally, I test whether interest rate futures contracts have a relationship with farmland values as interest rates have been proven to be a reliable predictor in past research. Farmland capitalization rates and anticipated inflation have hypothesized relationships, but are mainly used as control variables in the study. In general, agricultural commodity futures contracts are a poor predictor of changes in farmland market values. When examining relationships with quarterly percentage change regression models of the included variables, I find the Mountain region provides the most reliable pricing model where both live cattle and Minnesota wheat futures contracts has a positive statistically significant relationships with farmland market values. Also, wheat futures prices have a significant relationship with farmland values in the Corn Belt region. Interest rate futures contracts, farmland capitalization rates and anticipated inflation are not statistically significant in the majority of the regions. As a robustness check, I model the price levels of the variables using Johansen’s cointegration procedure. This time-series econometric methodology provides results in regards to long-run equilibrium relationships between the variables. The results are only slightly better. Corn, orange juice and sugar futures contracts have positive statistically significant relationships with farmland market values in multiple regions. Again, wheat has a statistically significant positive relationship with farmland values in the Corn Belt region. The Mountain region and interest rate futures contracts are not applicable for the cointegration tests as they are not integrated to the order of one.