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We propose a model for optimizing structured portfolios with liquidity-adjusted Value-at-Risk (LVaR) constraints, whereby linear correlations between assets are replaced by the multivariate nonlinear dependence structure based on Dynamic Conditional Correlation t-copula modeling. Our portfolio optimization algorithm minimizes the LVaR function under adverse market circumstances and multiple operational and financial constraints. When we consider a diversified portfolio of international stock and commodity market indices under multiple realistic portfolio optimization scenarios, the obtained results consistently show the superiority of our approach relative to other competing portfolio strategies including the minimum-variance, risk-parity and equally weighted portfolio allocations.
This study adopts a copula wavelet approach to analyze dynamics of the gold price against bonds, stocks and exchange rates based on disaggregation of the underlying relationships across different frequencies. We also examine whether gold prices are directly affected by changes in uncertainty. Analyzing data for nine economies for a sample period starting in 1985, we find that the role of gold changes significantly after the collapse of Lehman Brothers in 2008. Gold is unable to serve as a hedge in the classical sense while the findings for the period prior to 2008 mostly suggest that gold is able to shield investors. Uncertainty measures display a surprising and time-varying relationship with the path of the gold price. While economic policy uncertainty is positively correlated with gold price developments, macroeconomic uncertainty and inflation uncertainty among forecasters are both negatively related to gold.
We transform financial return series into its frequency and time domain via wavelet decomposition to separate shortrun noise from long-run trends and assess the relevance of each frequency to value-at-risk (VaR) forecast. Furthermore, we analyze financial assets in calm and turmoil market times and show that daily 95% VaR forecasts are mainly driven by the volatility that is captured by the first scales comprising the short-run information, whereas more timescales are needed to adequately forecast 99% VaR. As a result, individual timescales linked via copulas outperform classical parametric VaR approaches that incorporate all information available.
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