By Svetlozar T. Rachev, Stoyan V. Stoyanov, Visit Amazon's Frank J. Fabozzi Page, search results, Learn about Author Central, Frank J. Fabozzi,

ISBN-10: 047005316X

ISBN-13: 9780470053164

ISBN-10: 0470253606

ISBN-13: 9780470253601

This groundbreaking ebook extends conventional methods of chance size and portfolio optimization by way of combining distributional types with possibility or functionality measures into one framework. all through those pages, the professional authors clarify the basics of chance metrics, define new ways to portfolio optimization, and speak about a number of crucial threat measures. utilizing various examples, they illustrate a variety of purposes to optimum portfolio selection and threat concept, in addition to purposes to the realm of computational finance that could be helpful to monetary engineers.

**Read Online or Download Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization: The Ideal Risk, Uncertainty, and Performance Measures PDF**

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**Extra info for Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization: The Ideal Risk, Uncertainty, and Performance Measures**

**Example text**

8 provides an illustration is the two-dimensional case. 6. All points that have an elevation above 1 have a local dependence implying that the events Y 1 ∈ (y1 , y1 + ) and Y 2 ∈ (y2 , y2 + ) for a small > 0 are likely to occur jointly. This means that in a large sample of observations, we observe the two events happening together more often than implied by the independence assumption. In contrast, all points with an elevation below 1 have a local dependence implying that the events Y 1 ∈ (y1 , y1 + ) and Y 2 ∈ (y2 , y2 + ) for a small > 0 are likely to occur disjointly.

N is larger than what it would if the corresponding events were independent. Therefore, copula density values larger than 1 mean that the corresponding events are more likely to happen simultaneously. This analysis indicates that the copula density function provides information about the local dependence structure of a multidimensional random 30 ADVANCED STOCHASTIC MODELS variable Y relative to the case of stochastic independence. 8 provides an illustration is the two-dimensional case. 6. All points that have an elevation above 1 have a local dependence implying that the events Y 1 ∈ (y1 , y1 + ) and Y 2 ∈ (y2 , y2 + ) for a small > 0 are likely to occur jointly.

S. Kotz and A. W. Kemp (1993). , New York: John Wiley & Sons. Larsen, R. , and M. L. Marx (1986). An introduction to mathematical statistics and its applications, Englewed Clifs, NJ: Prentice Hall. Mikosch, T. (2006). ‘‘Copulas—tales and facts,’’ Extremes 9: 3–20. Patton, A. J. (2002). Application of copular theory in financial econometrics, Doctoral Dissertation, Economics, University of California, San Diego. Working paper, London School of Economics. ¨ Ruschendorf, L. (2004). ‘‘Comparison of multivariate risks and positive dependence,’’ Journal of Applied Probability 41(2): 391–406.

### Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization: The Ideal Risk, Uncertainty, and Performance Measures by Svetlozar T. Rachev, Stoyan V. Stoyanov, Visit Amazon's Frank J. Fabozzi Page, search results, Learn about Author Central, Frank J. Fabozzi,

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