Contributions published at Quantitative Finance (Marc Chesney)

We conduct a laboratory experiment to study whether people intuitively use real-option strategies in a dynamic investment setting. The participants were asked to play as an oil manager and make production decisions in response to a simulated mean-reverting oil price. Using cluster analysis, participants can be classified into four groups, which we label ‘mean-reverting’, ‘Brownian motion real-option’, ‘Brownian motion myopic real-option’, and ‘ambiguous’. We find two behavioral biases in the strategies of our participants: ignoring the mean-reverting process, and myopic behavior. Both lead to too frequent switches when compared with the theoretical benchmark. We also find that the last group behaved as if they have learned to incorporate the true underlying process into their decisions, and improved their decisions during the later stage.

This paper determines the value of asset tradeability in an option pricing framework. In our model, tradeability is valuable since it allows investors to exploit temporary mispricings of stocks. The model delivers several novel insights on the value of tradeability: The value of tradeability is the larger, the higher the pricing efficiency of the market is. Uncertainty increases the value of tradeability, no matter whether the uncertainty results from noise trading or from new information about the fundamental value of the stock. The value of tradeability is the larger, the longer the illiquid stock cannot be traded and the more trading dates the liquid stock offers.

This appendix extends the empirical results in Chesney, Crameri, and Mancini (2011). Informed trading activities on put and call options are analyzed for 19 companies in the banking and insurance sectors from January 1996 to September 2009. Our empirical findings suggest that certain events such as the takeovers of AIG and Fannie Mae/Freddie Mac, the collapse of Bear Stearns Corporation and public announcements of large losses/writedowns are preceded by informed trading activities in put and call options. The realized gains amount to several hundreds of millions of dollars. Several cases are discussed in detail.

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The pricing of energy commodity derivatives will be the topic of this thesis. We present existing models and results. Furthermore, we analyse the futures price behaviour with respect to maturity and time-to-maturity and find seasonal volatility patterns. We tackle these empirical findings with a modified Heston model with seasonal mean volatility function which depends on the maturity and the time-to-maturity. We discuss the implementation with finite elements to price American futures options and use the Greeks for a least-squares fitting to market data. The calibration results show a very good fit to market data, with relative root mean squared errors in the area between 0.5% to 2.5% for natural gas and heating oil calls and puts as well as for gasoline call options.

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In this paper, we give a financial justification, based on no-arbitrage conditions, of the (H)-hypothesis in default time modeling. We also show how the (H)-hypothesis is affected by an equivalent change of probability measure. The main technique used here is the theory of progressive enlargements of filtrations.

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