Systemic risk indicator based on implied and realized volatility

TL;DR

This paper introduces IVRVSRI, a new systemic risk indicator based on implied and realized volatility, demonstrating its effectiveness in predicting stock market risks across multiple regions from 2000 to 2023.

Contribution

The paper presents a novel, simplified systemic risk measure that reduces model risk and computational complexity while providing robust and accurate risk predictions.

Findings

IVRVSRI effectively captures systemic risk variations across regions.

It outperforms other SRIs in forecasting S&P 500 weekly returns.

The measure is adaptable to different assets and high-frequency data.

Abstract

We propose a new measure of systemic risk to analyze the impact of the major financial market turmoils in the stock markets from 2000 to 2023 in the USA, Europe, Brazil, and Japan. Our Implied Volatility Realized Volatility Systemic Risk Indicator (IVRVSRI) shows that the reaction of stock markets varies across different geographical locations and the persistence of the shocks depends on the historical volatility and long-term average volatility level in a given market. The methodology applied is based on the logic that the simpler is always better than the more complex if it leads to the same results. Such an approach significantly limits model risk and substantially decreases computational burden. Robustness checks show that IVRVSRI is a precise and valid measure of the current systemic risk in the stock markets. Moreover, it can be used for other types of assets and high-frequency data. The forecasting ability of various SRIs (including CATFIN, CISS, IVRVSRI, SRISK, and Cleveland FED) with regard to weekly returns of S&P 500 index is evaluated based on the simple linear, quasi-quantile, and quantile regressions. We show that IVRVSRI has the strongest predicting power among them.