Portfolio Risk Evaluation: An Approach Based on Dynamic Conditional Correlations Models and Wavelet Multi-Resolution Analysis
type: Dataset
description: We analyzed the volatility dynamics of three developed markets (U.K., U.S. and Japan), during the period 2003-2011, by comparing the performance of several multivariate volatility models, namely Constant Conditional Correlation (CCC), Dynamic Conditional Correlation (DCC) and consistent DCC (cDCC) models. To evaluate the performance of models we used four statistical loss functions on the daily Value-at-Risk (VaR) estimates of a diversified portfolio in three stock indices: FTSE 100, S&P 500 and Nikkei 225. We based on one-day ahead conditional variance forecasts. To assess the performance of the abovementioned models and to measure risks over different time-scales, we proposed a wavelet-based approach which decomposes a given time series on different time horizons. Wavelet multiresolution analysis and multivariate conditional volatility models are combined for volatility forecasting to measure the comovement between stock market returns and to estimate daily VaR in the time-frequency space. Empirical results shows that the asymmetric cDCC model of Aielli (2008) is the most preferable according to statistical loss functions under raw data. The results also suggest that wavelet-based models increase predictive performance of financial forecasting in low scales according to number of violations and failure probabilities for VaR models.
landing page: https://shs.hal.science/halshs-00793068
keyword: dynamic-conditional-correlations
keyword: infoeu-reposemanticspreprint
keyword: preprints-working-papers-
keyword: shsecohumanities-and-social-scienceseconomics-and-finance
keyword: stock-prices
keyword: value-at-risk
keyword: wavelet-decomposition
issued: 2026-05-14T06:06:20.593967
modified: 2026-05-14T06:06:20.593973
language: en
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

Portfolio Risk Evaluation: An Approach Based on Dynamic Conditional Correlations Models and Wavelet Multi-Resolution Analysis
type: Distribution
format: HTML
access URL: https://shs.hal.science/halshs-00793068
issued: 2026-05-14T06:06:20.612401
modified: 2026-05-14T06:06:20.575416