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Activity Number: 70 - Nonlinearites and Information
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #328727
Title: Estimation of Dynamic Conditional Correlation Matrices by a Nonlinear Common Factor Model
Author(s): Craig Rolling* and Yongli Zhang and Yuhong Yang
Companies: Saint Louis University and Independent Researcher and University of Minnesota
Keywords: dynamic correlation; energy pricing; factor analysis; finance; high-dimensional data; portfolio optimization

In economic and business data, the covariance or correlation matrix of a random vector often fluctuates with time and exhibits seasonality. The most widely-used approaches for estimating and forecasting the correlation matrix (e.g., multivariate GARCH) often are hindered by estimation and inference difficulties, especially in high dimensions, and require strong assumptions. In this talk, we propose a new method for modeling and forecasting correlation matrices that allows the correlation to be driven nonlinearly by common factors. The nonlinear common factor approach simplifies estimation in high-dimensional settings and provides more flexibility than previous factor-based methods. This talk will introduce our method and illustrate its use on natural gas and power prices in Boston, which are related to the common factors of daily temperature and humidity.

Authors who are presenting talks have a * after their name.

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