Thesis (M.B.A.)--University of Hong Kong, 1994. / Includes bibliographical references (leaves 107-109).

Wong Hau Man, Ben. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 85-89). / Abstract / Acknowledgment / Chapter Chapter 1. --- Introduction --- p.1 / Chapter Chapter 2. --- Introduction to the Hang Seng Index Futures Market --- p.9 / Chapter Chapter 3. --- Dynamic Efficiency --- p.17 / Chapter 3.1 --- The Potential Lead/Lag Relationship between the Stock Index Futures price and the Stock Index --- p.18 / Chapter 3.2 --- Empirical Evidence of the Lead/Lag Relationship -the US experience --- p.20 / Chapter 3.3 --- Granger and Engle's Error-Correction Model --- p.21 / Chapter 3.4 --- Error-Correction Model for the Hang Seng Index Futures Price and Hang Seng Index --- p.25 / Chapter 3.5 --- Simultaneous Error-Correction Model --- p.30 / Chapter Chapter 4. --- Price Volatility --- p.38 / Chapter 4.1 --- Why Volatility Matters --- p.38 / Chapter 4.2a --- Theoretical Foundation of the relationship between Futures Trading and Cash Market Volatility --- p.39 / Chapter 4.2b --- Empirical Evidence of Futures Trading and Cash Market Volatility - the US experience --- p.40 / Chapter 4.3 --- The Schwert Estimation Method --- p.42 / Chapter 4.4 --- Hang Seng Index Volatility and Cash Market Trading Volume --- p.47 / Chapter 4.5 --- Hang Seng Index Volatility and Futures Trading Activities --- p.48 / Chapter 4.6 --- Hang Seng Index Volatility and Contract Life Cycle --- p.50 / Chapter Chapter 5. --- Margin Policy --- p.56 / Chapter 5.1 --- The Economic Role of Futures Margin --- p.57 / Chapter 5.2a --- Theoretical Foundation of the relationship between Margin Requirement and Futures Volatility --- p.58 / Chapter 5.2b --- Empirical Evidence of Margin Requirement and Price Volatility --- p.59 / Chapter 5.3 --- HSI Futures Margin Requirement and Probability of Exhaustion --- p.61 / Chapter 5.4 --- HSI Futures Margin Requirement and HSI Futures Volatility --- p.64 / Chapter 5.4a --- Event-Study Approach --- p.64 / Chapter 5.4b --- Alternative Method --- p.66 / Chapter 5.5 --- HSI Futures Leverage and Price Volatility --- p.70 / Chapter Chapter 6. --- Conclusions --- p.81 / REFERENCES --- p.85 / APPENDIX 1. - Data Description --- p.90 / APPENDIX 2. - FIGURES --- p.92 / Chapter - --- Figure 1. Trend of HSI from May 86 to Dec93 --- p.93 / Chapter - --- Figure 2. Trend of HSI Futures Price from May 86 to Dec93 --- p.94 / Chapter - --- Figure 3. Volatility of HSI from May 86 to Dec93 --- p.95 / Chapter - --- Figure 4. HSI Futures Margin and Futures Volatility (Futures volatility is measured in daily change in contracts value) --- p.96

by Lam Chi-keung, Wallace, Ng Kim-hung. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 41-44). / ABSTRACT --- p.iii / ACKNOWLEDGEMENTS --- p.iv / TABLE OF CONTENTS --- p.v / LIST OF TABLES --- p.vi / LIST OF FIGURES --- p.vii / LIST OF APPENDICES --- p.viii / CHAPTER / INTRODUCTION --- p.1 / DEVELOPMENT OF METHODOLOGY --- p.7 / cost-of-carry model --- p.7 / Stock Index Futures --- p.9 / Borrowing and Lending Rates --- p.12 / Transaction Costs --- p.13 / Calendar Spread in Stock Index Futures --- p.15 / Discrete Dividend --- p.15 / Futures Spread --- p.16 / SCOPE OF STUDY --- p.18 / Spread and Discrepancy --- p.18 / Trading Rule --- p.18 / Predicting Market Price by Equilibrium Futures Price --- p.21 / DATA --- p.22 / RESULTS --- p.26 / Descriptive Statistics --- p.26 / Stimulated Trading Rule --- p.27 / Regression Analysis --- p.28 / CONCLUSION AND DISCUSSION --- p.29 / APPENDIX --- p.31 / BIBLIOGRAPHY --- p.38

Study project (MBA)--University of Stellenbosch, 2003. / ENGLISH ABSTRACT: In a rational efficiently functioning market, the price of the share index and share index futures contracts should be perfectly contemporaneously correlated. According to the cost of carry model, the futures price should equal its fair value at maturity. The basis should be equal to the cost of carry throughout the duration of the futures contract. However, in practice the cost of carry model is obscured and the basis varies and is normally not equal to the cost of carry. Reasons for this variability in basis include the mark-to-market requirement of the futures contract, the differential tax treatment of spot and futures contracts, as well as the transaction cost of entering into a contract. Transaction costs are lower for futures contracts than for spot contracts. This study uses the Chen, Cuny and Haugen (1995) model to examine the relationship between the basis and volatility of the underlying index and between the open interest of the futures contract and the volatility of the underlying index. Chen et al. (1995) predicted that the basis is negatively related to the volatility of the underlying index and that the open interest is positively related to the volatility of the underlying index. The study will also test the statement by Helmer and Longstaff (1991) that the basis has a negative concave relationship with the level of interest rate. The tests were performed on data from ALSI, FINI and INDI futures contracts. The sample period was from January 1998 to December 2001. The results correspond to those obtained by Chen et al. (1995) in that the basis is negatively related to the volatility of the underlying index. This is true for all the three indices. The other main prediction of the Chen, Cuny and Haugen (CCH) model (1995), which is also supported by the study, is that open interest is significantly related to the volatility of the underlying index. The study also supports the statement by Helmer and Longstaff (1991) that the there is a highly significant negative concave relationship between the basis and interest rate. / AFRIKAANSE OPSOMMING: In "n mark wat rasioneel funksioneer, behoort die prys van die aandele-indeks en aandele-indekstermynkontrakte perfek gekorreleer te wees in tyd. Volgens die drakostemodel behoort die termynkontrakprys op die vervaldatum gelyk te wees aan die billike waarde daarvan. Die basis behoort vir die looptyd van die termynkontrak gelyk te wees aan die drakoste. In die praktyk word die drakostemodel egter vertroebel en wissel die basis en is dit gewoonlik nie gelyk aan die drakoste nie. Redes vir hierdie veranderlikheid van die basis sluit in die waardasie teenoor markprys van die termynkontrak, die belasting van toepassing op loko- en termynkontrakte, asook die transaksiekoste by die aangaan van "n kontrak. transaksiekoste vir termynkontrakte is laer as vir lokokontrakte. Hierdie studie gebruik die model van Chen, Cuny en Haugen (1995) om die verwantskap tussen die basis en die volatiliteit van die onderliggende indeks en tussen die oop kontrakte van die termynkontrak en die volatiliteit van die onderliggende indeks te ondersoek. Chen et al. (1995) voer aan dat daar 'n negatiewe verwantskap is tussen die basis en die volatiliteit van die onderliggende indeks en dat daar "n positiewe verwantskap is tussen die oop rente en die volatiliteit van die onderliggende indeks. Die studie toets ook Helmer en Longstaff (1991) se hipotese dat daar 'n negatiewe, konkawe verhouding tussen die basis en die rentekoersvlak bestaan. Die toetse is uitgevoer op data van ALSI-, FINI- EN INDItermynkontrakte. Die steekproef was van Januarie 1998 tot Desember 2001. Die resultate stem ooreen met dié van Chen, Cuny en Haugen (1995) se model (CCH-model) in dié opsig dat daar "n negatiewe verband is tussen die basis en die volatiliteit van die onderliggende indeks. Dit geld vir al drie die indekse. Die ander hoofresultate van Chen et al. (1995), wat ook deur die studie ondersteun word, is dat daar "n beduidende verband tussen die oop kontrakte en die volatiliteit van die onderliggende indeks bestaan. Die studie ondersteun ook Helmer en Longstaff(1991) se siening dat daar 'n beduidende, negatiewe, konkawe verhouding tussen die basis en die rentekoers bestaan.

published_or_final_version / Business Administration / Master / Master of Business Administration

published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

Investing in commodities may have important benefits for investors but only in the last few decades have they started to think more about this possibility. Furthermore, large investors are more inclined to change their own personal view. Therefore, understanding the benefits that commodities could give to an investment portfolio might alleviate investors’ concerns. Several previous studies, as Belousova and Dorfleitner (2012) suggest, that the commodities with higher benefits are precious metals and gold, in particular. The purpose of our work is to understand which possible benefits are for equity investors and if they are common for certain commodities with different physical characteristics. The first part of our empirical work focuses on the main descriptive statistics of the return distribution (mean, variance, volatility, skewness, kurtosis and correlation) for 8 stock indices and 7 commodity futures. The main goal of this is to understand the differences among the commodities and between the commodities and the stock indices. In the second part of the empirical work, we test the safe-haven and the hedge properties of these commodities on a weekly basis for all of them with stock indices, and we do the same on a daily and monthly basis for only commodities which are negatively correlated on average with the stock indices. In the last part of our work, we combine these 7 commodities, following the principles of Bloomberg Commodity Index (BCOM), in order to create a well-balanced and well-diversified commodity index. Additionally, we create some mixed portfolios using this index and a different stock index every time. After that we look at the volatilities and the returns of these mixed portfolios with different weight combinations. Our main goals in this section are to understand the characteristics of the commodity index in comparison with stock indices and then, finding which weight combinations give the mixed portfolios the optimal risk-return trade off. Understanding which are efficient weights, can lead to conclusions about the weight that commodities should have in a portfolio according to the risk tolerance of the investors.  The research is done considering three time frequencies: daily, weekly and monthly; in line with the ones used by Baur and McDermott (2010). The sample size differs among these three different time basis. In fact, daily data started in January 2007 and the other two time frequencies data began with January 1997. All the time samples ended in March 2016. The results of the first part show that gold is the only commodity with a volatility similar to the stock indices (it also has a higher average return) and that on the daily, weekly and monthly basis. Whereas, the other commodities are much riskier than stock indices since they have higher volatility for all the three time-frequencies analyzed.  The results of the second part suggest that only gold is both a safe-haven and hedging commodity in line with the methodology used by Baur and McDermott (2010), but only for DAX 30 on a weekly basis. Furthermore, our results also show that natural gas is strong hedge in some cases such as natural gas for STI (Singapore) on a monthly basis or gold for Nikkei 225 on daily, weekly and monthly basis. Other commodities are neither safe-haven nor hedge in any case, except for silver which is a safe-haven commodity for DAX 30 and Sensex which at its worst, 1% and 5%, declines in the market respectively. The results of the last part of our work show that all the minimum variance mixed portfolios (the ones with the weights give the lowest risk) - made on a weekly basis - reduce the portfolio volatility and make the portfolio returns higher than the stock indices returns in 5 cases out of 8. Additionally, the results show how investors, who add a well-balanced and well-diversified commodity index to their portfolios, are able to observe several weight combinations and choose the one which suits their risk tolerance. Moreover, our results show that the optimal-weight combinations for commodity weights are lower than 0,5 only for FTSE 100 and S&P 500 (both values are 0,49) and higher than 0,62 but lower than 0,7 for DAX 30, Nikkei 225, Hang Seng, Sensex, SSEC. Furthermore, the optimal weight for STI is 0,54.

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