Abstract
The purpose of this study is to find the influence of various macroeconomic factors on the volatility index, as macroeconomic factors affect stock market volatility, resulting in an impact on the VIX Index, representing the risk in the stock market. To estimate the significance and importance of the U.S. macroeconomic variables on stock market volatility and risk, classification problems from machine learning are constructed to predict the daily and weekly trends of the VIX Index. Data from May 2007 to December 2021 is considered for analysis. The selected models are trained with twenty-four daily features and twenty-four plus nine weekly features. The outcomes suggest that the decisions made by the Light GBM and XG Boost on ranking features can be significantly accepted over logistic regression. It is found from the results that economic policy uncertainty indices, gold price, the USD Index, and crude oil are signified as strong predictors. The Financial Stress Index, initial claims, M2, TED spread, Fed rate, and credit spread are also strong predictors, while various yields on fixed income securities make a little less impact on the VIX Index. The TED spread, Financial Stress Index, and Equity Market Volatility (Infectious Disease Tracker) are positively associated with the VIX.
Highlights
Academic Editors: Kevin Dow andThe CBOE volatility index (VIX) Index is a short-term measure of real-time risk in the stock market and is viewed as a fear index
Considering such an important revelation, the main motivation behind this study is to examine the role of macroeconomic variables on the risk contribution to the stock market, unlike most of the studies on returns where risk is measured by the VIX Index
As predicting the day-to-day and week-to-week movements of the VIX Index is interesting, and its association with the macroeconomic variables is highly important, machine learning algorithms, such as logistic regression, XG Boost, and Light GBM, are applied on the set of feature variables derived from the daily and weekly macroeconomic variables and a closing value of the VIX Index, and after performing hyperparameters tuning, the captured feature variables are ranked according to their importance in predicting the daily and weekly movements of the VIX Index
Summary
The CBOE VIX Index is a short-term measure of real-time risk in the stock market and is viewed as a fear index. The day-to-day movements in the VIX Index indicate how the market’s perceptions fluctuate over time, and it is an important tool for risk management in the capital market. The movements of the VIX Index from day to day are of interest, as a good check on the shifting market perceptions of risk, and for volatility trading, using options strategies, or VIX futures. Some researchers (Carr 2017; Onan et al 2014; Sarwar 2012) believe the VIX acts as a fear index or a market perception of risk, while others (Bantwa 2017; Chandra and Thenmozhi 2015) propose risk handling and portfolio diversification. Since 2003, the VIX Index is the implied volatility of the options written on the S&P 500
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