A0789
Title: A nonparametric approach to understand the dynamics of multivariate financial data
Authors: Kunal Rai - Indian Institute of Management Bangalore (India) [presenting]
Archi Roy - Indian Institute of Science Education and Research Pune (India)
Itai Dattner - University of Haifa (Israel)
Soudeep Deb - Indian Institute of Management Bangalore (India)
Abstract: In recent years, the increasing availability of multivariate financial data has underscored the limitations of traditional parametric models, particularly in terms of flexibility and scalability. These challenges are addressed by leveraging recent advancements in nonparametric econometrics to explore the asymptotic inference of drift, volatility, and risk metrics and quantiles within a unified framework for a multi-dimensional stochastic regression model. The consistency of the drift, volatility, and quantile estimators, as well as the asymptotic normality of the drift estimator, is established, providing a statistically robust foundation for analyzing complex financial markets. By capturing nonlinear dependencies and the temporal evolution of multivariate relationships that utilize functional or physical dependence, the approach offers a more comprehensive understanding of market dynamics. The theoretical results are further validated through detailed simulations and a real-world application to energy market returns, demonstrating the practical utility of the proposed method. The analytical tools available for financial forecasting , risk management, and portfolio optimization are not only enhanced, but also contribute to a deeper understanding of financial market behavior.
