A Basic Overview of Various Stochastic Approaches to Financial Modeling With Examples

TL;DR

This paper reviews various stochastic models used in financial market analysis, comparing their capabilities and limitations, and demonstrates the effectiveness of Levy processes in stock return prediction through empirical analysis.

Contribution

It provides a comprehensive overview of stochastic models in finance, including new insights into Levy processes for parameter estimation and model calibration.

Findings

Levy processes improve stock return predictions.

Different models capture market features with varying accuracy.

Hybrid approaches are suggested for future research.

Abstract

This paper explores stochastic modeling approaches to elucidate the intricate dynamics of stock prices and volatility in financial markets. Beginning with an overview of Brownian motion and its historical significance in finance, we delve into various stochastic models, including the classic Black-Scholes framework, the Heston model, fractional Brownian motion, GARCH models, and Levy processes. Through a thorough investigation, we analyze the strengths and limitations of each model in capturing key features of financial time series data. Our empirical analysis focuses on parameter estimation and model calibration using Levy processes, demonstrating their effectiveness in predicting stock returns. However, we acknowledge the need for further refinement and exploration, suggesting potential avenues for future research, such as hybrid modeling approaches. Overall, this study underscores the importance of stochastic modeling in understanding market dynamics and informs decision-making in the financial industry.