Doctoral thesis

Derivative securities in risk management and asset pricing


81 p

Thèse de doctorat: Università della Svizzera italiana, 2018

English The high informational content and the ease of accessibility are among the most attractive features which make derivative securities particularly useful in financial applications. With a special focus on risk management and asset pricing, I present several methodologies which involve the use of option and futures data in the estimation process. This doctoral thesis consists of three chapters. The first one, “Forward-looking VaR and CVaR: an application to the Natural gas Market”, presents, backtests and compares point risk forecasts for the natural gas market using a novel methodology which introduces derivative securities into a classical calibration setting. The second chapter, “A Bayesian Estimate of the Pricing Kernel”, is a joint work with G. Barone-Adesi and A. Mira. The article sets the pricing kernel estimation into a Bayesian framework, which enables to combine the use of derivative and historical data in the physical density calibration. Thanks to their higher accuracy and flexibility, the resulting pricing kernel estimates display a monotonic decreasing shape over a large range of returns, consistently with the classical theory. The third chapter, “S&P 500 Index, an Option-Implied Risk Analysis”, is a joint work with G. Barone-Adesi and C. Sala. Tested on the US equity market, the article presents a detailed analysis on the performance of the option-implied risk metrics both in absolute terms and relative to the existing historical-based risk metrics.
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