Modeling Financial Volatility in the Presence of Abrupt Changes

TL;DR

This paper introduces a nonparametric method to detect abrupt changes in financial volatility, improving modeling accuracy by identifying structural breaks without assuming Gaussian distributions.

Contribution

It presents a novel nonparametric approach for detecting volatility jumps and modeling regimes, addressing computational and distributional limitations of existing methods.

Findings

Improved fit of volatility models for major stock indexes

Effective detection of structural break points in financial data

Enhanced modeling of volatility regimes

Abstract

The volatility of financial instruments is rarely constant, and usually varies over time. This creates a phenomenon called volatility clustering, where large price movements on one day are followed by similarly large movements on successive days, creating temporal clusters. The GARCH model, which treats volatility as a drift process, is commonly used to capture this behavior. However research suggests that volatility is often better described by a structural break model, where the volatility undergoes abrupt jumps in addition to drift. Most efforts to integrate these jumps into the GARCH methodology have resulted in models which are either very computationally demanding, or which make problematic assumptions about the distribution of the instruments, often assuming that they are Gaussian. We present a new approach which uses ideas from nonparametric statistics to identify structural break points without making such distributional assumptions, and then models drift separately within each identified regime. Using our method, we investigate the volatility of several major stock indexes, and find that our approach can potentially give an improved fit compared to more commonly used techniques.