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4220. Volatility of Stock Market Aggregate Volatility (VoV) Forecast Based on Cross-Sectional Intrinsic Entropy’s Volatility Estimates

Invited abstract in session WD-63: Complexity and Financial Patterns, stream OR in Banking, Finance and Insurance: New Tools for Risk Management.

Wednesday, 14:30-16:00
Room: S14 (building: 101)

Authors (first author is the speaker)

1. Claudiu Vinte
Economic Informatics and Cybernetics Department, Bucharest University of Economic Studies
2. Marcel Ausloos
School of Business
3. Bogdan Iftimie
Department of Applied Mathematics, Bucharest University of Economic Studies

Abstract

In the context of index volatility versus the volatility of the entire market, the following question becomes pertinent: Does the market volatility track the index volatility, or is it the other way around? To estimate the market's overall volatility, we employ the cross-sectional intrinsic entropy (CSIE) model as the underlying methodology for tracking the aggregate volatility of all the symbols listed and traded on a given stock market. We investigate the appropriate GARCH models to forecast the variance of market cross-sectional aggregate volatility estimates and the variance of market indices volatility estimates (VoV) forecasting. The CSIE model provides estimates of the daily aggregate volatility for the entire market. Alternatively, index volatility estimates are determined on n-day rolling windows. Therefore, CSIE market volatility estimates are integrated into the research methodology as comparable n-day moving averages. Our study uses historical end-of-day (EOD) data consisting of traded volume, opening, high, low, and closing prices (OHLCV) for all companies listed on the NYSE and NASDAQ and the corresponding market indices, S&P500 and NASDAQ Composite respectively. We conclude that the S&P 500 is not fully representative of the market, and particularly in the periods preceding recessions, it does not capture overall market immanent deep trends, specifically those revealed through the CSIE market volatility estimator.

Keywords

Status: accepted

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