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A Benchmark Approach to Quantitative Finance (Finance) by Eckhard Platen PDF

By Eckhard Platen

ISBN-10: 0195300572

ISBN-13: 9780195300574

The benchmark strategy offers a normal framework for monetary marketplace modeling, which extends past the normal risk-neutral pricing thought. It allows a unified therapy of portfolio optimization, spinoff pricing, built-in threat administration and assurance threat modeling. The life of an an identical risk-neutral pricing degree isn't required. as an alternative, it results in pricing formulae with appreciate to the real-world chance degree. This yields very important modeling freedom which seems to be precious for the derivation of sensible, parsimonious industry versions. the 1st a part of the booklet describes the required instruments from chance concept, facts, stochastic calculus and the speculation of stochastic differential equations with jumps. the second one half is dedicated to monetary modeling by the benchmark process. numerous quantitative tools for the real-world pricing and hedging of derivatives are defined. the final framework is used to supply an figuring out of the character of stochastic volatility. The booklet is meant for a large viewers that comes with quantitative analysts, postgraduate scholars and practitioners in finance, economics and coverage. It goals to be a self-contained, obtainable yet mathematically rigorous creation to quantitative finance for readers that experience an affordable mathematical or quantitative history. ultimately, the publication may still stimulate curiosity within the benchmark process by way of describing a few of its energy and large applicability.

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Extra resources for A Benchmark Approach to Quantitative Finance (Finance)

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If one searches in probability or statistics textbooks and encyclopedias, then the characteristic function of the Student t distribution is notably absent or erroneous. 85) φX (θ) = exp{ı µ ∆ θ} 1 Γ ( 12 n) 2 2 n−1 for all degrees of freedom n > 0 and θ ∈ . 29). 87) K1 (¯ α) α ¯ 2 + δ 2 ∆ θ2 for θ ∈ . 88) for θ ∈ . A convenient proof for the above results can be obtained by interpreting the above distributions as normal mixture distributions. This means that the random variable is assumed to be conditionally Gaussian distributed with independent random variance.

12) We could add further examples but it should now be clear how to obtain the mean of a random variable with a given density. 13) is finite. Consequently, the variance, also called the second central moment, is always nonnegative. The square root of 2 , is called the standard deviation of X. Note that if the variance, σX = σX Var(X) = 0, then P (X = E(X)) = 1. 14) For a two-point distributed random variable X, taking values x1 with probability p1 and x2 with probability p2 = 1 − p1 , its variance is given by 2 σX = p1 (1 − p1 ) (x2 − x1 )2 .

9. Hyperbolic density under log scale The hyperbolic density is a three parameter density. The parameter α ¯ is the shape parameter with smaller α ¯ implying larger tail heaviness. Furthermore, when α ¯ → ∞ the hyperbolic density asymptotically approaches the Gaussian density. In Fig. 9 we graph the hyperbolic density in a logarithmic scale. Variance Gamma Density (*) Madan & Seneta (1990) proposed that log-returns are distributed with a normal-variance gamma mixture distribution. This case is obtained when√the shape parameters are such that λ > 0 and α ¯ = 0, that is, δ = 0 and α = c2 λ .

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A Benchmark Approach to Quantitative Finance (Finance) by Eckhard Platen


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