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Activity Number: 520 - Contributed Poster Presentations: Business and Economic Statistics Section
Type: Contributed
Date/Time: Wednesday, August 2, 2017 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract #323476
Title: An Investigation of Conditional Heteroscedasticity Structural Change in S&P 500 Returns
Author(s): Jinyu Du* and V A Samaranayake
Companies: and Missouri University of Science and Technology
Keywords: Conditional Heteroscedasticity ; GARCH Models ; Volatility Structure ; News Impact Curves ; Regression Analysis

It is well-known that financial returns such as those obtained from S&P 500 index data exhibits conditional heteroscedasticity. In this study, we investigate whether the conditional volatility structures of S&P500 differ between economic recession and non-recession periods. This investigation was performed on S&P500 returns from1989 - 2015 as well as S&P 500 sector returns from 2007 - 2017. In initial investigations into an appropriate volatility model for this data, the EGARCH (1,1) was found to be the optimal model, indicating the underlying asymmetric conditional volatility structure caused by positive and negative shocks of news/innovation. Regression analysis on the logarithm of squared return data was performed to determine whether model parameters changed across different time segments. News Impact Curves (NIC) were plotted to visualize the underlying differences among models for the selected time periods. In general, negative news/shocks induced higher conditional volatility change than positive news/shocks. Results indicated that volatility structures during the non-recession periods are significantly different from those of the recession periods, with the latter inducing more volatility. S&P 500 sector returns also showed similar patterns.

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

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