We assess the ability of minimum-variance portfolio allocation strategies accounting for time-varying correlation between assets to provide performance benefits relative to an equally-weighted portfolio. Prior to transaction costs correlation-based strategies emphatically outperform the...

Motivated by previous studies documenting significant return and volatility effects of economic policy uncertainty (EPU) on the stock market, this study examines whether EPU has an effect on the dynamic conditional correlations between stock and commodity returns. Our findings point to a...

We employ averaging over statistical ensemble of assets to derive an index characterizing the level of correlations in a financial market – the eCORR index. This index does not require lengthy historical data and reacts immediately to any changes in correlations. Study of statistical...

We propose information theoretic tests for serial independence and linearity in time series against nonlinear dependence on lagged variables, based on the conditional mutual information. The conditional mutual information, which is a general measure for dependence, is estimated using the...

The equal-risk-contribution, inverse-volatility weighted, maximum-diversification and minimum-variance portfolio weights are all direct functions of the estimated covariance matrix. We perform a Monte Carlo study to assess the impact of covariance matrix misspecification to these risk-based...

In this paper we consider the deterministic trend model where the error process is allowed to be weakly or strongly correlated and subject to nonstationary volatility. Extant estimators of the trend coefficient are analyzed. We find that under heteroskedasticity the Cochrane-Orcutt-type...

In this paper we study systemic risks in the Korean banking sector by using two famous systemic risk measures – the MES (marginal expected shortfall) and CoVaR. To compute both measures we employ Engle's dynamic conditional correlation model. Our empirical analysis shows, first, that although...

We showed that Singular Spectrum Analysis (SSA) applied to time series yields better correlations for risk simulations. This involved comparing SSA-based correlations with standard correlations and to noise, a zero correlation Wishart random matrix (WRM). We complete this testing here. We also...

This is the second paper presenting noise-reduced, stable correlations for long-term risk measurement. We smooth time series using Singular Spectrum Analysis (SSA) and then form the correlations from these smoothed time series. These correlations have superior time stability and are cleaned of...

This is the third paper in a series devoted to obtaining noise-reduced, stable correlations by smoothing time series using Singular Spectrum Analysis, or SSA. Here we show that the SSA-based correlations are superior in terms of noise reduction, employing a number of simple tests using Random...