Doctoral thesis

Options trading strategies and equity risk premia


93 p

Thèse de doctorat: Università della Svizzera italiana, 2018

English This doctoral thesis examines, from both a theoretical and an empirical perspective, different aspects of the equity derivative markets, such as the appropriate evaluation of equity risk premia and the development of trading strategies based on options. In the first chapter, entitled “Approximate arbitrage with limit orders”, I introduce an almost riskless trading scheme involving two options and two asynchronous operations: a limit order for one of the assets and a market order for the other one, once the limit order is executed. A model integrating option pricing and order arrivals explains the proximity of this strategy to a pure arbitrage. In particular, satisfying the requisites of the approximate arbitrage opportunities, I therefore refer to it as a limit order approximate arbitrage. An empirical study on a novel option data set confirms that market participants actively invest in these trades. The analysis also reveals the presence of short-living pure arbitrage opportunities in the market, promptly taken by the arbitrageurs. In the second chapter, entitled “Trading central moments” (a joint work with Paul Schneider), we propose a definition of realized central moments that is tradeable. The prices of these realized central moments are the implied central moments proposed by Bakshi et al. (2003). The motivation for utilizing our measure rather than sample moments is threefold: first, unlike sample moments, our measures are tradeable and the trading profits therefore admit an interpretation as risk premia. Second, even if sample central moments were tradeable, asymptotically, they could be different from implied central moments in absence of risk premia. Finally, estimates of sample central moments are based on the past trajectory of the financial asset, while our measures, as well as implied moments, are not. The last chapter, “Evaluating models jointly with economic and statistical criteria” (a second joint work with Paul Schneider), introduces a new criterion for the estimation of models used in finance, which explicitly incorporates the models' ability to provide signals for trading strategies. An out-of-sample analysis reveals that an investor using this estimator may enjoy significant excess returns over a competitor who employs purely statistical criteria such as Generalized Method of Moments or Maximum Likelihood.
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