Copula Models

Economic uncertainty and exchange rates linkage revisited: modelling tail dependence with high frequency data ArXiv ID: 2511.05315 “View on arXiv” Authors: Nourhaine Nefzi, Abir Abid Abstract The aim of this paper is to dig deeper into understanding the exchange rates and uncertainty dependence. Using the novel Baker et al. (2020)’s daily Twitter Uncertainty Index and BRICS exchange rates, we investigate their extreme tail dependence within an original time-varying copula framework. Our analysis makes several noteworthy results. Evidence for Indian, Russian and South African currencies indicates an elliptical copulas’ dominance implying neither asymmetric features nor extreme movements in their dependence structure with the global economic uncertainty. Importantly, Brazilian and Chinese currencies tail dependence is upward trending suggesting a safe-haven role in times of high global economic uncertainty including the recent COVID-19 pandemic. In such circumstances, these markets offer opportunities to significant gains through portfolio diversification. ...

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. ...

Basket Options with Volatility Skew: Calibrating a Local Volatility Model by Sample Rearrangement ArXiv ID: 2407.02901 “View on arXiv” Authors: Unknown Abstract The pricing of derivatives tied to baskets of assets demands a sophisticated framework that aligns with the available market information to capture the intricate non-linear dependency structure among the assets. We describe the dynamics of the multivariate process of constituents with a copula model and propose an efficient method to extract the dependency structure from the market. The proposed method generates coherent sets of samples of the constituents process through systematic sampling rearrangement. These samples are then utilized to calibrate a local volatility model (LVM) of the basket process, which is used to price basket derivatives. We show that the method is capable of efficiently pricing basket options based on a large number of basket constituents, accomplishing the calibration process within a matter of seconds, and achieving near-perfect calibration to the index options of the market. ...