Forecasting Equity Volatility Dynamics with Markov-Switching EGARCH Models
Name
dennissharma-tyson-mfin-sloan-2024-thesis.pdf
Description
Thesis PDF
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1.17 MB
Format
Adobe PDF
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2e985970b44514e305d08277718a23d3
Author(s)
Dennis-Sharma, Tyson
Advisor(s)
Kogan, Leonid
Date Issued
February 2024
Publisher
Massachusetts Institute of Technology
Abstract
Understanding and anticipating stock market volatility enables better portfolio management. We forecast US equity volatility with a Markov-Switching EGARCH model with one high and one low volatility regime. We show that this model contains similar information about future volatility as the VIX Index. It also outperforms single-regime GARCH and EGARCH models. Moreover, the model’s 1-day ahead regime predictions are economically significant: market volatility and kurtosis, equity risk premia, and stock-bond relations shift when the model forecasts a regime change.
MIT Department
Sloan School of Management
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