You are here

Dissertations

Inferring dependencies between financial assets with applications to multi-name credit derivatives

Roy Mashal, 2004
Faculty Advisor: Assaf Zeevi
Print this abtract

Abstract

In this dissertation we: (1) develop a statistical framework for testing dependence assumptions in a given time series; (2) develop a statistical test for comparing dependence structures (aka copula functions ) derived from the Normal and Student-t distributions and use this to quantify the potential for extreme co-movements and; (3) analyze in detail credit derivative models and their sensitivity to different dependence assumptions. The main results of our studies may be summarized as follows. First, the t-copula assumption is a more plausible model of dependence for the tested time-series; the Normal copula provides a lesser fit than the t-copula but a superior fit compared with the three Archimedean copulas tested, namely, Frank, Gumbel, and Clayton. Second, exploiting the nesting of the Normal copula within the t-family we show that the former can be almost always rejected on the basis of a likelihood ratio test. Third, financial data exhibit a clear tendency for extreme co-movements which cannot be predicted on the basis of a Normal copula model. Fourth, as the dimensionality increases (i.e., the number of assets being tested for dependence increases) the distinction between the Normal and t-copulas become “sharper”. Fifth, the dependence structure of asset returns is strikingly similar to the one underlying equity returns. Finally, the tendency for extreme co-movements does not seem to be affected by the sampling frequency, in contrast to the phenomenon observed in univariate returns that tend to be “heavy-tailed” in higher frequencies, and more “Gaussian-like” in lower frequencies. Our results bear important financial implications which we illustrate throughout this thesis with examples that include: MSCI national equity indices data; risk measure for portfolios of equity options; pricing n th to default baskets; pricing and risk measures of synthetic CDO tranches, and; analysis of portfolio tail dependence indices.

Doctoral Program News

Wong wins Deloitte Fellowship

We are proud to announce that Yu Ting (Forester) Wong is one of the recipients of the 2014 Deloitte Foundation Doctoral Fellowship in Accounting.

Read More About Yu Ting >

The PhD Program Congratulates John Yao

PhD student John Yao was a finalist in the 2013 M&SOM (Manufacturing & Service Operations Management) student paper competition.

Read More About John >

Apply Now
Sept 2014

Doctoral
Deadline: 01/05/14

MS Marketing
Deadline: 02/02/14

MS Financial Economics
Deadline: 02/02/14

MS Leadership
Rolling admission

Check Application Status

Once you've submitted your application, you can login and track your status by using the link below.

Check Status