. Based on these active risk factors, an adjustment for intertemporal dependency is made. The authors extend TEDAS methodology … to three gestalts differing in allocation weights’ determination: a Cornish-Fisher Value-at-Risk minimization, Markowitz …

various frequencies, we define a \textit{quantile spectral} beta representation that characterizes asset's risk generally … market risk that captures dependence between extremely low market and asset returns. Second, extreme market volatility risk … as daily data. These results suggest that both frequency-specific tail market risk and extreme volatility risk are priced …

Handling risk factors in the context of a multi-asset risk parity portfolio allocation has created increased interest … in recent literature. When allocating along risk factors through principal components, one major problem that persists is … positions if investors accept equal risk contributions for the first few risk factors, which explain most of the portfolio …

This study investigates the factors of Bitcoin's tail risk, quantified by Value at Risk (VaR). Extending the …

In this paper, we propose a general data-driven framework that unifies the valuation and risk measurement of financial … risk measures. Then we risk-neutralize the non-parametric density and distribution functions to model-independently valuate … pricing and risk management of multiple financial contracts based solely on observable time series data …

In this paper, we propose a general data-driven framework that unifies the valuation and risk measurement of financial … risk measures. Then we risk-neutralize the non-parametric density and distribution functions to model-independently valuate … pricing and risk management of multiple financial contracts based solely on observable time series data …

. Two approaches based on the extreme value theory were compared: Block Maxima and the Peaks Over Threshold. Forecasts were …

, which is called G-bounds. Constructed G-bounds evaluate risk in the financial markets more carefully than models based on …, the closer the risk of losses on the stock market to the corresponding risk of loss for a normal distribution, the higher …