Skip to Main Content
Introduction to Bayesian Estimation and Copula Models of Dependence
book

Introduction to Bayesian Estimation and Copula Models of Dependence

by Arkady Shemyakin, Alexander Kniazev
March 2017
Beginner to intermediate content levelBeginner to intermediate
352 pages
9h 18m
English
Wiley
Content preview from Introduction to Bayesian Estimation and Copula Models of Dependence

CHAPTER 8 International Markets

8.1 Introduction

One of the important fields of research in modern financial mathematics and risk management deals with stock indexes. Global, regional, and national stock indexes and index futures contracts serve as instruments for hedging and diversification in the international markets. Statistical modeling of the joint behavior of stock indexes has been of special interest recently because such models can be used directly to hedge complex multinational investment portfolios, see Sharma and Seth [34].

Portfolio diversification can be attained through taking positions in futures, which are indexed through geographically or economically remote markets. In the classical Markowitz model this remoteness is modeled using an insignificant or even negative correlation between the national indexes. However, considering modern financial data, correlation analysis often proves to be insufficient because linear correlation, which fits perfectly dependence in multivariate normal models, poorly describes nonnormal joint distributions, since they allow for such deviations as asymmetry, heavy tails, and nonlinear dependence of distribution components, see [9], [10], or [30]. That is why we are especially interested in the analysis of tails of joint distributions, see Fortin and Kuzmics [12], as it helps us to assess the probabilities of several stock indexes plummeting simultaneously, which could cause global markets to collapse and inflict great losses on ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Deep Learning through Sparse and Low-Rank Modeling

Deep Learning through Sparse and Low-Rank Modeling

Zhangyang Wang, Yun Fu, Thomas S. Huang
Learning Bayesian Models with R

Learning Bayesian Models with R

Hari Manassery Koduvely

Publisher Resources

ISBN: 9781118959015Purchase book