Wong, Chun Yin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 45-46). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Weather Risks and Weather Derivatives --- p.4 / Chapter 2.1 --- Weather Risk --- p.4 / Chapter 2.2 --- Weather Derivatives --- p.6 / Chapter 2.3 --- Importance of Long Term Forecasting --- p.7 / Chapter 3 --- Modeling the Temperature --- p.9 / Chapter 3.1 --- Stationary Long-Memory Time Series Model --- p.13 / Chapter 3.2 --- Use of Temporal Aggregation Model --- p.19 / Chapter 4 --- Weather Derivative Valuation Models --- p.26 / Chapter 4.1 --- List of Assumptions --- p.27 / Chapter 4.2 --- Valuation Formula --- p.30 / Chapter 4.3 --- Forecasting power of daily temperature model --- p.32 / Chapter 4.4 --- Empirical Result --- p.37 / Chapter 5 --- Summary and Conclusion --- p.43 / Bibliography --- p.45

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Vita. Includes bibliographical references. Available also from UMI Company.

Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains xi, 90 p.; also includes graphics (some col.). Includes bibliographical references (p. 88-90). Available online via OhioLINK's ETD Center

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Whilst common intuition and the rapid growth of weather derivative practices effectively support the notion that equity returns are sensitive to weather randomness, empirical support is fragile. This thesis is the first study that investigates weather exposure and weather risk-return trade-off consistent with the arbitrage pricing theory (APT). It explores weather risk and its premium in the U.S. market during January 1980 to December 2009, based on three of the most weather-influenced industries. The research starts with the construction of ten seasonally-adjusted weather measures as the proxies of unexpected temperature, gauged in Fahrenheit degree and percentage terms. The weather exposures of individual firms are estimated based on each of the ten measures and the market return. Although average weather exposure coefficients are small, the number of firms with significant estimates is more than attributable to chance and results are more profound in utilities. The weather coefficients are mainly stable over the sample period, indicating that the introduction of weather derivatives does not significantly impact a firm’s weather exposure. Further investigation into summer and winter time reveals that most of the significant weather betas are found in winter. However, only a minority of firms have statistically different weather betas between the two seasons. Results are robust with respect to the ten measures. The finding that unpredictable weather broadly affects groups of stocks has a direct implication in asset prices, as weather risk may be one of the priced factors. In this study, the weather risk premium is estimated using the standard two-pass Fama and MacBeth (1973) methodology, enhanced with Shanken’s adjustments for the errors in variables problem. The tests are based on firm-level and portfolio-level regressions, assessed by different model specifications and repeated for the ten weather measures. In the unconditional setting, there is little support that the market price of weather risk is not zero. Although the estimates are insignificant, the magnitudes of weather premiums are relatively high compared with those of other macroeconomic factors in previous literature. Most of the estimated weather pricings are negative; thus, stocks exposed to weather should be hedged against an unanticipated increase in temperature. The main pricing results are robust to alternative sample sets, portfolio formations, base assets and weather measures. Nonetheless, the significance of weather premium is slightly affected by model specifications. In few cases, the pricings of weather risk are significant when the positive values of weather betas are used in cross-sectional regressions.

Weather has a significant impact on business activities of many kinds. The list of economic activities subjected to the risk of the weather include: the energy producers and consumers, the industry of leisure, the insurance industry, the food industry and the agricultural industries but the primary industry, namely the energy industry, has given rise to the demand for weather derivatives and has caused the weather risk management industry to evolve actively. A derivative is a contract or security, whose payoffs depend upon the price of an underlying asset price, and is used to control the risks of naturally-arising exposures to such an asset price. Therefore weather derivatives are financial contracts with payouts that depend on weather in some form. It is a contract that provides a payoff in response to an index level based on weather phenomena (West, 2002).The underlying variable can be for example humidity, rain, snowfall, temperature, or even sunshine. The main players who take part in the weather derivatives markets industry can be grouped in to five main categories, namely: 1) End users who are also referred to as hedgers 2) Speculators 3) Market makers 4) Brokers 5) Insurance and re-insurance companies. Since the late 90’s when the first weather derivatives transactions were recorded, the underlying market has witnessed the development of a new derivative market in the United States, which is gradually expanding across Europe. However, the newly developed market for weather derivatives is not liquid in Africa and specifically South Africa mainly due to the following factors: 1) Many companies and business organisations have not yet established a hedging policy or even figured out how their businesses or industries are exposed to weather risks. 2 2) “Since many companies and industries depend on insurance companies to cover their risks, it is possible that the solutions suggested by these companies or industries looking for protection from weather risks differ according to the cover provided by these insurance organisations “(Micali, 2008). The main aim of this study is to review available statistical models for pricing derivatives, with temperature as the underlying which could enable industries, businesses and other organisations in South Africa to protect themselves against losses due to fluctuations in the weather and therefore hedge their risks.

Yao Li. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 66-68). / Abstracts in English and Chinese. / Chapter Chapter 1 --- Weather Derivatives: A Review --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- Types of weather risk --- p.1 / Chapter 1.3 --- Key weather derivative elements --- p.3 / Chapter 1.4 --- Methods for pricing weather derivatives --- p.5 / Chapter 1.5 --- Current Situation in Hong Kong: the Recreation Industry --- p.8 / Tables and Figures --- p.10 / Chapter Chapter 2 --- Markov Models with Application to Hong Kong's Rainfall --- p.13 / Chapter 2.1 --- The Model --- p.14 / Chapter 2.2 --- Maximum Likelihood Estimation --- p.17 / Chapter 2.2.1 --- Estimates for Occurrence Model --- p.18 / Chapter 2.2.2 --- Estimates for Intensity Model --- p.23 / Chapter 2.3 --- Model for Amount --- p.28 / Tables and Figures --- p.29 / Chapter Chapter 3 --- Contract Specifications and Option Evaluation --- p.42 / Chapter 3.1 --- The Contract --- p.42 / Chapter 3.2 --- The Monte-Carlo Simulation --- p.44 / Chapter 3.2.1 --- The Rainfall Event --- p.45 / Chapter 3.2.2 --- The Aggregate Payoff --- p.47 / Chapter 3.2.3 --- Some Simulation Results --- p.48 / Chapter 3.3 --- Further Applications --- p.49 / Tables and Figures --- p.55 / Chapter Chapter 4 --- Concluding Remarks and Discussions --- p.64 / References --- p.66

Yeung Yun Sing Samson. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 64-67). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background --- p.5 / Chapter 2.1 --- Applicability of Weather Derivative in Hong Kong: The Recre- ation Industry --- p.7 / Chapter 2.2 --- Types of Weather Risk --- p.9 / Chapter 3 --- Literature Review --- p.12 / Chapter 4 --- Basic Model --- p.17 / Chapter 4.1 --- Notations --- p.18 / Chapter 4.2 --- Assumptions --- p.21 / Chapter 4.3 --- The Profit Model --- p.22 / Chapter 5 --- Fundamental Analysis --- p.25 / Chapter 5.1 --- Sales Profit Analysis --- p.25 / Chapter 5.2 --- Option Analysis --- p.27 / Chapter 5.3 --- Profit Function Reformulation --- p.30 / Chapter 6 --- Objectivel: Lexicographic Optimization --- p.35 / Chapter 6.1 --- Equivalence between Lexicographic Optimization and Expected Utility Maximization --- p.38 / Chapter 6.2 --- Minimizing the Conditional Profit Variance given Q* --- p.39 / Chapter 6.3 --- Numerical Examples --- p.42 / Chapter 6.3.1 --- Convexity of conditional profit variance --- p.42 / Chapter 6.3.2 --- Correlation between Q* & N* --- p.47 / Chapter 7 --- Objective2: Mean-Variance Optimization --- p.52 / Chapter 7.1 --- Numerical Examples --- p.59 / Chapter 8 --- Conclusion and Future Work --- p.61 / Bibliography --- p.64 / Chapter A --- Weather Option Pricing --- p.68 / Chapter B --- Infeasibility of Perfect Hedge --- p.70

published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

Numerous authors note failure of private insurance markets to provide affordable and comprehensive crop insurance. Economic logic suggests that index contracts potentially may have some advantages when compared with traditional (farm based) crop insurance. It is also a matter of common knowledge that weather is an important production factor and at the same time one of the greatest sources of risk in agriculture. Hence introduction of crop insurance contracts, based on weather indexes, might be a reasonable approach to mitigate problems, associated with traditional crop insurance products, and possibly lower the cost of insurance for end users. In spite of the fact that before the financial crisis of 2008-09 market for weather derivatives was the fastest growing derivatives market in the USA, agricultural producers didn’t express much interest in application of weather derivatives to management of their systematic risk. There are several reasons for that, but the most important one is the presence of high basis risk, which is represented by its two major components: technological (i.e. goodness of fit between yield and weather index) and geographical basis. Majority of the researchers is focusing either on pricing of weather derivatives or on mitigation of geographical basis risk. At the same time the number of papers researching possible ways to decrease technological basis is quite limited, and always assumes linear dependency between yields and weather variables, while estimating the risk reducing efficiency of weather contracts, which is obviously large deviation from reality. The objective of this study is to estimate the risk reducing efficiency of crop insurance contracts, based on weather derivatives (indexes) in the state of Texas. The distributions of representative farmer’s profits with the proposed contracts are compared to the distributions of profits without a contract. This is done to demonstrate the risk mitigating effect of the proposed contracts. Moreover the study will try to account for a more complex dependency structures between yields and weather variables through usage of copulas, while constructing joint distribution of yields and weather data. Selection of the optimal copula will be implemented in the out-of-sample efficient framework. An effort will be done to identify the most relevant periods of year, when weather has the most significant influence on crop yields, which should be included in the model, and to discover the most effective copula to model joint weather/yield risk. Results suggest that effective insurance of crop yields in the state of Texas by the means of proposed weather derivatives is possible. Besides, usage of data-mining techniques allows for more accurate selection of the time periods to be included in the model than ad hoc procedure previously used in the literature. Finally selection of optimal copula for modeling of joint weather/yield distribution should be crop and county specific, while in general Clayton and Frank copula of Archimedean copula family provide the best out-of-sample metric results.