New PDF release: Bayesian Methods in Finance

By Svetlozar T. Rachev, John S. J. Hsu, Biliana S. Bagasheva, Frank J. Fabozzi

ISBN-10: 0470249242

ISBN-13: 9780470249246

ISBN-10: 0471920835

ISBN-13: 9780471920830

Bayesian tools in Finance offers an in depth review of the idea of Bayesian tools and explains their real-world functions to monetary modeling. whereas the rules and ideas defined during the booklet can be utilized in monetary modeling and choice making ordinarily, the authors specialise in portfolio administration and marketplace hazard management—since those are the components in finance the place Bayesian equipment have had the best penetration so far.

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Extra resources for Bayesian Methods in Finance

Example text

THE BAYES’ THEOREM Bayes’ theorem is the cornerstone of the Bayesian framework. 3 The likelihood function for the parameters of the normal distribution: contour plot 12 BAYESIAN METHODS IN FINANCE distribution of a random variable with its conditional distribution. 6 Bayes’ theorem is a rule that can be used to update the beliefs that one holds in light of new information (for example, observed data). We first consider the discrete version of Bayes’ theorem. Denote the evidence prior to observing the data by E and suppose that a researcher’s belief in it can be expressed as the probability P(E).

12) beta distribution is the conjugate distribution for the parameter, θ , of the binomial distribution. See Chapter 3 for more details on conjugate prior distributions. 5 Density curves of the two prior distributions for the binomial parameter, θ Note: The density curve on the left-hand side is the uniform density, while the one on the right-hand side is the beta density. where α > 0 and β > 0 are the parameters of the beta distribution and B(α, β) is the so-called beta function. 4, respectively, and we postpone the discussion of prior specification until the next chapter.

EK . The events are such 13 The Bayesian Paradigm probability, P(E), after observing the data is given by the ratio P(D | E)/P(D). The conditional probability, P(D | E), when considered as a function of E is in fact the likelihood function, as will become clear further below. As an illustration, consider a manager in an event-driven hedge fund. The manager is testing a strategy that involves identifying potential acquisition targets and examines the effectiveness of various company screens, in particular the ratio of stock price to free cash flow per share (PFCF).

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Bayesian Methods in Finance by Svetlozar T. Rachev, John S. J. Hsu, Biliana S. Bagasheva, Frank J. Fabozzi


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