Nos derniers articles

"#expectile is a #risk measure that, similar to #var (quantile) and CVaR (superquantile), can be employed in #riskmanagement."

"#ifrs17 introduces the concept of a #riskadjustment that compensates #insurers for the #uncertainty about the amount and timing of the cash flows that arise from #nonfinancial#risks. The method for its calculation is not prescribed and several options are emerging, including #var and cost of #capital."

"This paper investigates the relationship between #geopoliticalrisk and #financialstress using a bivariate #var model. The study uses the #gpr Index to measure geopolitical risk and the OFSR FSI index to measure financial stress over a period of 06 January 2000 - 03 March 2023 on daily data. The results show a significant relationship between financial stress and geopolitical risk"

This paper explores the optimal #reinsurance design for an #insurer with multiple lines of business, where the dependence structure between #risks is unknown. The study considers Value-at-Risk (#var) and Range-Value-at-Risk (#rvar) as #riskmeasures and applies general premium principles. The optimal reinsurance strategies are obtained under budget constraint and expected profit constraint.

This paper proposes a novel mixed-frequency quantile vector autoregression (MF-QVAR) model that uses a #bayesian framework and multivariate asymmetric Laplace distribution to estimate missing low-frequency variables at higher frequencies. The proposed method allows for timely policy interventions by analyzing conditional quantiles for multiple variables of interest and deriving quantile-related #riskmeasures at high frequency. The model is applied to the US economy to #nowcast conditional quantiles of #gdp, providing insight into #var, Expected Shortfall, and distance among percentiles of real GDP nowcasts.

"... we propose a reverse stress testing framework for dynamic models. Specifically, we consider a compound Poisson process over a finite time horizon and stresses composed of expected values of functions applied to the process at the terminal time. We then define the stressed model as the probability measure under which the process satisfies the constraints and which minimizes the KullbackLeibler divergence to the reference compound Poisson model."

"When developing large-sample statistical inference for quantiles, also known as Values-at-Risk in finance and insurance, the usual approach is to convert the task into sums of random variables. The conversion procedure requires that the underlying cumulative distribution function (cdf) would have a probability density function (pdf), plus some minor additional assumptions on the pdf. In view of this, and in conjunction with the classical continuous-mapping theorem, researchers also tend to impose the same pdf-based assumptions when investigating (functionals of) integrals of the quantiles, which are natural ingredients of many risk measures in finance and insurance. Interestingly, the pdf-based assumptions are not needed when working with integrals of quantiles, and in this paper we explain and illustrate this remarkable phenomenon."

"The methodologies examined include filtered historical simulation, extreme value theory, Monte Carlo simulation and historical simulation. Autoregressive-moving-average and generalized-autoregressive-conditional-heteroscedasticity models are used to estimate VaR."

"The empirical evidence suggests that a distribution based on a single copula is not flexible enough, and thus we model the dependence structure by means of vine copulas. We show that the approach based on regular vines improves the fit. Moreover, even though losses corresponding to different event types are found to be dependent, the assumption of perfect positive dependence is not supported by our analysis. "

"Our model handles typical stop-loss reinsurance contracts. We show that a three-point distribution achieves the worst-case VaR of the total retained loss of the insurer, from which the closed-form solutions of the worst-case distribution and optimal deductible are obtained. Moreover, we show that the worst-case Conditional Value-at-Risk of the total retained loss of the insurer is equal to the worst-case VaR, and thus the optimal deductible is the same in both cases."