Risk diversification is the basis of insurance and investment. It is thus crucial to study the effects that could limit … it. One of them is the existence of systemic risk that affects all of the policies at the same time. We introduce here a … probabilistic approach to examine the consequences of its presence on the risk loading of the premium of a portfolio of insurance …

such policies. A typical industry practice consists of using fund mapping regressions to represent basis risk stemming from …

Portfolio credit risk is often concerned with the tail distribution of the total loss, defined to be the sum of default … also discuss estimates for Value-at-Risk, and observe that our results may be extended to cases where the number of factors …

-horizon goal makes it more appealing among practitioners than myopic approaches, like Markowitz's mean-variance or risk parity. The … GOP literature typically considers risk-neutral investors with an infinite investment horizon. In this paper, we compute … the optimal bet sizes in the more realistic setting of risk-averse investors with finite investment horizons. We find that …

In this paper, the generalized Pareto distribution (GPD) copula approach is utilized to solve the conditional value-at-risk …-parametric kernel-smoothed interior and the parametric Pareto upper tail and (iii) Value-at-Risk (VaR) to quantify risk measure. The … minimizing CVaR measure and simulated copula returns combined outperforms the risk/return of domestic portfolios, such as the US …

An accurate assessment of the risk of extreme environmental events is of great importance for populations, authorities … and the banking/insurance/reinsurance industry. Koch (2017) introduced a notion of spatial risk measure and a … corresponding set of axioms which are well suited to analyze the risk due to events having a spatial extent, precisely such as …

Hedging downside risk before substantial price corrections is vital for risk management and long-only active equity … manager performance. This study proposes a novel methodology for crafting timing signals to hedge sectors' downside risk …

findings suggest that machine learning methods provide more accurate models of stock returns based on risk factors than … standard regression-based methods of estimation. They also indicate that certain risk factors and combinations of risk factors …