Abstract
Volatility is a fundamental notion in financial markets, influencing investment decisions, risk management techniques, and market dynamics. This paper provides a thorough overview of the historical evolution and practical implications of volatility, focusing on important works and key advancements in the field. The overview begins with early conceptions of volatility and the necessity for measurement prompted by market collapses, then progresses to advanced quantitative models and computer tools. The study includes key innovations such as the Black-Scholes model, which revolutionized options pricing and pioneered the concept of implied volatility. The Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models introduced frameworks for modeling time-varying volatility, paving the way for greater forecasting accuracy. Advancements in computing techniques have made it easier to analyze high-frequency data and estimate realized volatility, providing timely insights into market trends. The review also investigates contemporary trends, such as the use of machine learning algorithms and the issues provided by cryptocurrency marketplaces. Furthermore, the article examines the various characteristics and metrics of volatility, emphasizing its multidimensional nature and diverse uses in risk management, portfolio optimization, derivative pricing, and market analysis. Practical examples show how investors, traders, and financial professionals may use volatility to navigate complex market settings and make sound judgments. Finally, the study highlights the enduring significance of volatility in financial markets and highlights the need for continuing research and analysis to improve our understanding of market behavior. Acknowledging the complexities of volatility prepares market participants with valuable understandings to manage risks effectively and capitalize on market opportunities, thus contributing to financial stability and optimal portfolio performance.
Publisher
Lattice Science Publication (LSP)
Reference35 articles.
1. Al-Hamdooni, E. K. F. (2023). The relationship between the random walk of the returns of financial market indices and market efficiency: an analytical study of the indicators of a sample of Arab financial markets. International Journal of Professional Business Review, 8(3), 1-28. https://doi.org/10.26668/businessreview/2023.v8i3.663
2. Andersen, T. G., & Bollerslev, T. (1998). Answering the skeptics: yes, standard volatility models do provide accurate forecasts. International Economic Review, 39(4), 885-905. https://doi.org/10.2307/2527347.
3. Bachelier, L. Théorie de la spéculation. Annales scientifiques de l'École Normale Supérieure, Serie 3, 17 (1900), (pp. 21-86). https://doi.org/10.24033/asens.476
4. Louis Bachelier's theory of speculation: the origins of modern finance;Bachelier;Princeton University Press,2011
5. Barro, R. J., Fama, E. F., Fischel, D. R., Meltzer, A. H., Roll, R., & Telser, L. G. (1989). Black Monday and the future of financial markets, edited by R.W. Kamphuis, Jr., R.C. Kormendi and J. W. H. Watson (MidAmerican Institute for Public Policy Research, Inc. and Dow Jones-Irwin, Inc.).