Perspective Chapter: Detecting Volatility Pattern of Assets Returns Using Wavelet Analysis

Abstract

This chapter advocates for the use of wavelet analysis as a potent tool in understanding the dynamic nature of asset price volatility in financial markets. While traditional methods like GARCH models have been valuable, wavelet analysis offers a distinctive approach by decomposing time series data into various scales and frequencies. This enables a comprehensive perspective, capturing both short-term fluctuations and long-term trends. In an era of interconnected and information-rich financial markets, the ability to discern subtle volatility patterns is crucial. The chapter provides a guide to wavelet analysis, explaining its foundations, principles, and methodology for application to financial time series. Real data from NASDAQ Composite, DOW Incorporated, S&P500, and Omnicell Inc. is used for illustration. The efficacy of wavelet analysis is emphasized, offering finance professionals, academia, and researchers a simple yet robust approach to navigate the complexities of modern financial markets, make informed decisions, and adapt to evolving conditions. The chapter aims to enhance understanding of financial market behavior, inspiring further research and innovation in financial analysis and risk management.