A Probability Metrics Approach to Financial Risk Measures by Svetlozar T. Rachev, Stoyan V. Stoyanov, Frank J. Fabozzi PDF

By Svetlozar T. Rachev, Stoyan V. Stoyanov, Frank J. Fabozzi

ISBN-10: 1405183691

ISBN-13: 9781405183697

A likelihood Metrics method of monetary hazard Measures relates the sector of chance metrics and hazard measures to each other and applies them to finance for the 1st time.

  • Helps to reply to the query: which probability degree is better for a given problem?
  • Finds new kinfolk among latest periods of probability measures
  • Describes purposes in finance and extends them the place possible
  • Presents the idea of chance metrics in a extra available shape which might be acceptable for non-specialists within the field
  • Applications contain optimum portfolio selection, danger conception, and numerical tools in finance
  • Topics requiring extra mathematical rigor and aspect are incorporated in technical appendices to chapters

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Extra info for A Probability Metrics Approach to Financial Risk Measures

Sample text

We can consider random elements which could be of very general nature, such as multivariate variables and stochastic processes, without changing much the general framework. From a practical viewpoint, by extracting the general principles, we are able to treat equally easy one-dimensional random variables describing, for example, stochastic returns on investments, multivariate random variables describing, for instance, the multi-dimensional behavior of positions participating in two different portfolios, or much more complex objects such as yield curves.

In this section we will prove the following facts. 2) holds for every separable metric space U. 2) holds for every non-atomic probability space ( , A, Pr) if and only if U is universally measurable. We need a few preliminaries. 7. (see Loeve (1963), p. 99, and Dudley (1989), p. 82). If ( , A, Pr) is a probability space, we say that A ∈ A is an atom if Pr(A) > 0 and Pr(B) = 0 or Pr(A) for each measurable B ⊆ A. A probability space is non-atomic if it has no atoms. 1. (Berkes and Phillip (1979)).

Define a law n on U by n(A) = m(f (A)) where f : U → V is a Borel-isomorphism. m. 6 TECHNICAL APPENDIX there is a standard set ⊆ U with n(S) = 1. Then f (S) is a standard subset of V with m(f (S)) = 1. m. The following result, which is in essence due to Blackwell (1956), will be used in an important way later on (cf. 1). 6. m. separable metric space and suppose that Pr is a probability measure on U. If A is a countably generated sub- -algebra of B(U), then there is a real-valued function P(B|x), B ∈ B(U), x ∈ U such that (1) (2) (3) (4) for each fixed B ∈ B(U), the mapping x → P(B|x) is an Ameasurable function on U; for each fixed x ∈ U, the set function B → P(B|x) is a law on U; for each A ∈ A and B ∈ B(U), we have A P(B|x) Pr(dx) = Pr(A ∩ B); there is a set N ∈ A with Pr(N) = 0 such that P(B|x) = 1 whenever x ∈ U − N.

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A Probability Metrics Approach to Financial Risk Measures by Svetlozar T. Rachev, Stoyan V. Stoyanov, Frank J. Fabozzi


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