Semiparametric Dynamic Copula Models for Portfolio Optimization

ArXiv ID: 2504.12266 “View on arXiv”

Authors: Unknown

Abstract

The mean-variance portfolio model, based on the risk-return trade-off for optimal asset allocation, remains foundational in portfolio optimization. However, its reliance on restrictive assumptions about asset return distributions limits its applicability to real-world data. Parametric copula structures provide a novel way to overcome these limitations by accounting for asymmetry, heavy tails, and time-varying dependencies. Existing methods have been shown to rely on fixed or static dependence structures, thus overlooking the dynamic nature of the financial market. In this study, a semiparametric model is proposed that combines non-parametrically estimated copulas with parametrically estimated marginals to allow all parameters to dynamically evolve over time. A novel framework was developed that integrates time-varying dependence modeling with flexible empirical beta copula structures. Marginal distributions were modeled using the Skewed Generalized T family. This effectively captures asymmetry and heavy tails and makes the model suitable for predictive inferences in real world scenarios. Furthermore, the model was applied to rolling windows of financial returns from the USA, India and Hong Kong economies to understand the influence of dynamic market conditions. The approach addresses the limitations of models that rely on parametric assumptions. By accounting for asymmetry, heavy tails, and cross-correlated asset prices, the proposed method offers a robust solution for optimizing diverse portfolios in an interconnected financial market. Through adaptive modeling, it allows for better management of risk and return across varying economic conditions, leading to more efficient asset allocation and improved portfolio performance.

Keywords: Copula Models, Mean-Variance Optimization, Time-Varying Dependence, Skewed Generalized T, Multivariate Analysis, Equities (Multi-Country)

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced semiparametric methods, non-parametric copulas (empirical beta copula), and detailed marginal distribution modeling (Skewed Generalized T) requiring substantial mathematical derivations. It demonstrates high empirical rigor by applying the model to real-world rolling window data from multiple countries (USA, India, Hong Kong), detailing performance metrics (Sharpe ratio, net worth), and comparing results against benchmarks.

  flowchart TD
    A["Research Goal: Develop Robust Portfolio<br>Optimization Model"] --> B["Data & Inputs<br>Multicountry Equity Returns"]
    B --> C["Key Methodology<br>Semiparametric Dynamic Copula"]
    C --> D["Computational Process<br>1. Model Marginals: Skewed Generalized T<br>2. Model Dependence: Empirical Beta Copula<br>3. Time-varying Parameter Estimation"]
    D --> E["Key Outcomes<br>1. Captures Asymmetry & Heavy Tails<br>2. Accommodates Dynamic Market Conditions<br>3. Robust Cross-Country Portfolio Optimization"]