Beatriz Vaz de Melo Mendes and Cecília Aíube
This paper aims to statistically model the serial dependence in the first and second moments of a univariate time series using copulas, bridging the gap between theory and…
Abstract
Purpose
This paper aims to statistically model the serial dependence in the first and second moments of a univariate time series using copulas, bridging the gap between theory and applications, which are the focus of risk managers.
Design/methodology/approach
The appealing feature of the method is that it captures not just the linear form of dependence (a job usually accomplished by ARIMA linear models), but also the non‐linear ones, including tail dependence, the dependence occurring only among extreme values. In addition it investigates the changes in the mean modeling after whitening the data through the application of GARCH type filters. A total 62 US stocks are selected to illustrate the methodologies.
Findings
The copula based results corroborate empirical evidences on the existence of linear and non‐linear dependence at the mean and at the volatility levels, and contributes to practice by providing yet a simple but powerful method for capturing the dynamics in a time series. Applications may follow and include VaR calculation, simulations based derivatives pricing, and asset allocation decisions. The authors recall that the literature is still inconclusive as to the most appropriate value‐at‐risk computing approach, which seems to be a data dependent decision.
Originality/value
This paper uses a conditional copula approach for modeling the time dependence in the mean and variance of a univariate time series.
Details
Keywords
Beatriz Vaz de Melo Mendes and Ricardo Pereira Câmara Leal
Proposes a new covariance matrix robust estimator able to capture the correct orientation of the data and the large unconditional variance caused by occasional high volatility…
Abstract
Purpose
Proposes a new covariance matrix robust estimator able to capture the correct orientation of the data and the large unconditional variance caused by occasional high volatility periods.
Design/methodology/approach
Derives easy‐to‐compute estimates for the center and covariance matrix of a data set. The method finds the correct orientation of the data through a robust estimator and the variances through the classical sample covariance matrix.
Findings
Simulation experiments confirm the good performance of the proposed estimator under ε‐contaminated normal models and multivariate t‐distributions.
Practical implications
Provides illustrations of the usefulness of this practical tool for the finance industry, in particular when constructing efficient frontiers. Shows that robust portfolios yield higher cumulative returns and possess more stable weights compositions.
Originality/value
It provides an alternative estimator for the covariance matrix able to find a good fit for the bulk of the data as well as for the extreme observations, which could be plugged in widely used financial tools.
Details
Keywords
SERGIO M. FOCARDI and FRANK J. FABOZZI
Fat‐tailed distributions have been found in many financial and economic variables ranging from forecasting returns on financial assets to modeling recovery distributions in…
Abstract
Fat‐tailed distributions have been found in many financial and economic variables ranging from forecasting returns on financial assets to modeling recovery distributions in bankruptcies. They have also been found in numerous insurance applications such as catastrophic insurance claims and in value‐at‐risk measures employed by risk managers. Financial applications include: