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This study analyzes affine styled-facts price dynamics of Henry Hub natural gas price by incorporating the price features of jump risk, and seasonality within stochastic volatility framework. Affine styled-facts dynamics has the advantage of being able to incorporate mean reversion (MR), stochastic volatility (SV), seasonality trends (S), and jump diffusion (J) in a standardized inclusive framework. Our main finding is that models that incorporate jumps significantly improve overall out-of-sample option pricing performance. The combined MRSVJS model provides the best fit of both daily gas price returns and the related cross section of option prices. Incorporating seasonal effects tend to provide more stable pricing ability, especially for the long-term option contracts. [ABSTRACT FROM AUTHOR]

We test and implement portfolio strategies for three major asset pricing models, under uniform diagnostic measures using the PACAP data set containing all current listing and de-listing of firms for the local stock exchange in several Pacific Basin countries. Compared to the often used MSCI database that include only a subset of the (large) firms in the local markets, the more complete coverage of our database allows for more robust testing of current multifactor asset pricing models since the possible effects of additional factors such as size and book to market may not show up correctly using less comprehensive data sets. Our data set also provides a natural packet of nonUS data for addressing the issue of whether the results of recent asset pricing research are sample specific. Our overall results provide multi-country (sample nonspecific) support for the additional asset pricing risk factors of the Fama-French three-factor model but not for the momentum factor of the Carhart model. We additionally find that the size risk factor is more prominent than value risk factor in the Pacific Basin markets. Finally, we find strong evidence that portfolio strategies implemented to capture value and size effects are profitable in the Pacific Basin stock markets. [ABSTRACT FROM AUTHOR]

Statistical time-series approaches to hedging are difficult to beat, especially out-of-sample, and are capable of out-performing many theory-based derivative pricing model approaches to hedging commodity price risks using futures contracts. However, the vast majority of time-series approaches to hedging discussed in the literature are essentially linear statistical projections, whether univariate or multivariate. Little is known about the potential hedging capabilities of nonlinear methods. This study describes how least-squares orthogonal polynomial approximation methods based on the spanning polynomial projection (SPP) can be used to enhance standard (linear) optimal hedging methods and improve hedging performance for a hedger with a mean-variance objective. Empirical analyses show that the SPP can be used effectively for hedging and gives better out-of-sample hedging performance than the benchmark VEC-GARCH hedging model. Results are robust to the inclusion of transaction costs and risk-aversion assumptions. [ABSTRACT FROM AUTHOR]