Time-Varying Dependence

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. ...

Multivariate Simulation-based Forecasting for Intraday Power Markets: Modelling Cross-Product Price Effects ArXiv ID: 2306.13419 “View on arXiv” Authors: Unknown Abstract Intraday electricity markets play an increasingly important role in balancing the intermittent generation of renewable energy resources, which creates a need for accurate probabilistic price forecasts. However, research to date has focused on univariate approaches, while in many European intraday electricity markets all delivery periods are traded in parallel. Thus, the dependency structure between different traded products and the corresponding cross-product effects cannot be ignored. We aim to fill this gap in the literature by using copulas to model the high-dimensional intraday price return vector. We model the marginal distribution as a zero-inflated Johnson’s $S_U$ distribution with location, scale and shape parameters that depend on market and fundamental data. The dependence structure is modelled using latent beta regression to account for the particular market structure of the intraday electricity market, such as overlapping but independent trading sessions for different delivery days. We allow the dependence parameter to be time-varying. We validate our approach in a simulation study for the German intraday electricity market and find that modelling the dependence structure improves the forecasting performance. Additionally, we shed light on the impact of the single intraday coupling (SIDC) on the trading activity and price distribution and interpret our results in light of the market efficiency hypothesis. The approach is directly applicable to other European electricity markets. ...