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

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

Strong seasonality is observed in the volatile hourly Alberta temperature and its low- and high-order statistical moments. We propose a time series model consisting of a linear combination of an annual sinusoidal model, a diurnal sinusoidal model and a fractional residual model, to study the characteristics of these spatial and time-dependent Alberta temperatures. Wavelet multi-resolution analysis is used to measure Hurst exponents of the temperature series. Our empirical results show that these Hurst exponents vary over various time scales, indicating the existence of multi-fractality in the temperatures. Such temperature models are of importance for the pricing and insurance of agricultural crops, of tourist resorts and of all forms of energy extraction and generation of importance to the resource-based economy of Alberta. Of particular interests are the observed extreme volatilities in the winters, caused by the unpredictable Chinook winds, which may be an important reason to introduce a Chinook insurance option. / 64 leaves : map ; 29 cm