Early warning of large volatilities based on recurrence interval analysis in Chinese stock markets

TL;DR

This paper introduces a method for predicting large stock market volatilities using recurrence interval analysis and $q$-exponential distribution, enabling more accurate risk forecasting and management.

Contribution

It develops an analytical hazard probability formula based on recurrence intervals and demonstrates its effectiveness in forecasting large volatilities in Chinese stock markets.

Findings

Recurrence intervals follow a $q$-exponential distribution across stocks.

The hazard probability formula aligns well with empirical data.

The method effectively predicts large volatility events with high accuracy.

Abstract

Being able to forcast extreme volatility is a central issue in financial risk management. We present a large volatility predicting method based on the distribution of recurrence intervals between volatilities exceeding a certain threshold $Q$ for a fixed expected recurrence time $\tau_Q$. We find that the recurrence intervals are well approximated by the $q$-exponential distribution for all stocks and all $\tau_Q$ values. Thus a analytical formula for determining the hazard probability $W(\Delta t |t)$ that a volatility above $Q$ will occur within a short interval $\Delta t$ if the last volatility exceeding $Q$ happened $t$ periods ago can be directly derived from the $q$-exponential distribution, which is found to be in good agreement with the empirical hazard probability from real stock data. Using these results, we adopt a decision-making algorithm for triggering the alarm of the occurrence of the next volatility above $Q$ based on the hazard probability. Using a "receiver operator characteristic" (ROC) analysis, we find that this predicting method efficiently forecasts the occurrance of large volatility events in real stock data. Our analysis may help us better understand reoccurring large volatilities and more accurately quantify financial risks in stock markets.