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We compare nonnested parametric specifications of the stochastic discount factor (SDF) using the conditional Hansen–Jagannathan (HJ-) distance. This distance measures the discrepancy between a parametric model-implied SDF and the admissible SDF’s satisfying all the conditional (dynamic) no-arbitrage restrictions, instead of just few unconditional no-arbitrage restrictions for managed portfolios chosen through the instrument selection. We estimate the conditional HJ-distance by a generalized method of moments estimator and establish its large sample properties for model selection purposes. We compare empirically several SDF models including multifactor beta pricing specifications and some recently proposed SDF models that are conditionally linear in consumption growth.
An American option provides the right to perform a specified financial transaction (sell, buy, exchange) on or before the contract maturity. Many different contracts traded on centralized and OTC markets are of this kind. In particular, a plain vanilla American option is a contract between two parties concerning the possibility of selling or buying a reference asset (underlying) at a specified price (strike price). Setting the contract price and choosing the best moment for its exercise are two of the most studied problems in finance during the last 40 years. In financial markets, the behavior of the underlying is not predictable. Thus, a description of the probability law governing its stochastic evolution is necessary for the determination of the contract price and the optimal exercise decision.The majority of the existing literature focuses on mathematical and numerical procedures for computing the option price and determining the optimal exercise policy for a given law of motion of the underlying. For these purposes, only a model for the dynamics of the underlying under the risk-neutral distribution is required. When this approach is put into practice, typically a parametric model for such distribution is adopted and the parameters are calibrated on a cross-section of available option prices.On the contrary, in this PhD thesis, that summarizes the research conducted to obtain the degree of Doctor of Philosophy in Economics at the University of Lugano, an econometric framework for the empirical pricing of American options is developed. In this framework, a statistical model for the dynamics of the underlying is specified by the researcher and estimated on available data. Data include both time series of relevant state variables and cross-sections of observed option prices. The estimated model is then used to estimate the price of contracts that are not currently actively traded on the market.The econometric approach proposed in this thesis features three major characteristics. First, it is based on a coherent specification of both historical and risk-neutral dynamics. Second, the statistical model for the dynamics of the underlying is more general than most of the models previously considered in the literature. Third, the model parameters can be consistently estimated even when the amount of option data is limited.In the first three chapters of the thesis, the problem, the proposed solution and an empirical application of the novel method are presented. Chapter 1 introduces the price of an American option as the expected value of the contract at the most remunerative time for exercising it. Some different pricing techniques based on this representation and the way they are used to handle with real data are briefly reviewed. Chapter 2 presents the novel empirical methodology developed in the PhD research.Chapter 3 describes an application of this methodology for the analysis of IBM shares and plain vanilla American options written on them. In the last two chapters of the thesis, the regularity assumptions
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