Hybrid Financial Models for Capturing Asset Correlation Structures

EasyChair Preprint 15051

Abstract

Accurately modeling asset correlation structures is critical for risk management, portfolio optimization, and pricing derivative products in financial markets. Traditional models, such as the Gaussian copula and correlation-based methods, often fail to capture complex, non-linear relationships between assets, especially during periods of market stress. In response, hybrid financial models have emerged, combining various statistical, econometric, and machine learning techniques to better represent the dynamic and multi-faceted nature of asset correlations. This abstract explores the development and application of hybrid financial models designed to capture asset correlation structures more effectively. These models integrate approaches from stochastic processes, copula theory, factor models, and deep learning to account for time-varying and non-linear dependencies among assets. By blending traditional econometric models with machine learning algorithms, hybrid models can dynamically adjust to changing market conditions, improving their ability to capture tail dependencies, extreme events, and contagion effects.

Keyphrases: Asset Allocation, Asset correlation, Asset pricing models, Copula models, Correlation Structures, Dynamic correlation, Financial Engineering, Financial Risk Metrics, Hybrid Financial Models, Machine Learning in Finance, Quantitative Finance, Simulation techniques, empirical validation, financial modeling, multivariate analysis, portfolio optimization, risk management, statistical methods, stochastic processes, time series analysis

@booklet{EasyChair:15051,
  author    = {Wayzman Kolawole},
  title     = {Hybrid Financial Models for Capturing Asset Correlation Structures},
  howpublished = {EasyChair Preprint 15051},
  year      = {EasyChair, 2024}}