# Time Series Copulas for Heteroskedastic Data

## TL;DR

This paper introduces parametric copulas tailored for heteroskedastic time series, enabling better modeling of serial dependence, volatility co-movement, and spillovers, with improved risk forecasting over traditional GARCH models.

## Contribution

The paper develops a novel copula framework for heteroskedastic time series, extending to multivariate cases and deriving new measures of volatility dependence.

## Key findings

- Copula models outperform GARCH in capturing marginal distributions.
- Enhanced accuracy in value at risk forecasts.
- Effective modeling of volatility spillovers and co-movement.

## Abstract

We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We develop our copula for first order Markov series, and extend it to higher orders and multivariate series. We derive the copula of a volatility proxy, based on which we propose new measures of volatility dependence, including co-movement and spillover in multivariate series. In general, these depend upon the marginal distributions of the series. Using exchange rate returns, we show that the resulting copula models can capture their marginal distributions more accurately than univariate and multivariate GARCH models, and produce more accurate value at risk forecasts.