Backtesting VaR under the COVID-19 sudden changes in volatility

We analyze the impact of the COVID-19 pandemic on the conditional variance of stock returns. We look at this effect from a global perspective, so we employ series of major stock market and sector indices. We use the Hansen's Skewed-t distribution with EGARCH extended to control for sudden changes in volatility. We oversee the COVID-19 effect on measures of downside risk such as the Value-at-Risk. Our results show that there is a significant sudden shift up in the return distribution variance post the announcement of the pandemic, which must be explained properly to obtain reliable measures for financial risk management. [Abstract copyright: Crown Copyright © 2021 Published by Elsevier Inc. All rights reserved.

Estimating the risk-adjusted capital is an affair in the tails

(Re)insurance companies need to model their liabilities' portfolio to compute the risk-adjusted capital (RAC) needed to support their business. The RAC depends on both the distribution and the dependence functions that are applied among the risks in a portfolio. We investigate the impact of those assumptions on an important concept for (re)insurance industries: the diversification gain. Several copulas are considered in order to focus on the role of dependencies. To be consistent with the frameworks of both Solvency II and the Swiss Solvency Test, we deal with two risk measures: the Value-at-Risk and the expected shortfall. We highlight the behavior of different capital allocation principles according to the dependence assumptions and the choice of the risk measure.Capital Allocation, Copula, Dependence, Diversification Gain, Model Uncertainty, Monte Carlo Methods, Risk-Adjusted Capital, Risk Measure

Backtesting VaR under the COVID-19 sudden changes in volatility

We analyze the impact of the COVID-19 pandemic on the conditional variance of stock returns. We look at this effect from a global perspective, so we employ series of major stock market and sector indices. We use the Hansen’s Skewed-t distribution with EGARCH extended to control for sudden changes in volatility. We oversee the COVID-19 effect on measures of downside risk such as the Value-at-Risk. Our results show that there is a significant sudden shift up in the return distribution variance post the announcement of the pandemic, which must be explained properly to obtain reliable measures for financial risk management.Financial support from the Spanish Ministry of Economy and Competitiveness through grant ECO2017-87069-P is gratefully acknowledged by the second author

Risk-based Probabilistic Quantification of Power Distribution System Operational Resilience

It is of growing concern to ensure the resilience in electricity infrastructure systems to extreme weather events with the help of appropriate hardening measures and new operational procedures. An effective mitigation strategy requires a quantitative metric for resilience that can not only model the impacts of the unseen catastrophic events for complex electric power distribution networks but also evaluate the potential improvements offered by different planning measures. In this paper, we propose probabilistic metrics to quantify the operational resilience of the electric power distribution systems to high-impact low-probability (HILP) events. Specifically, we define two risk-based measures: Value-at-Risk ($VaR_\alpha$) and Conditional Value-at-Risk ($CVaR_\alpha$) that measure resilience as the maximum loss of energy and conditional expectation of a loss of energy, respectively for the events beyond a prespecified risk threshold, $\alpha$. Next, we present a simulation-based framework to evaluate the proposed resilience metrics for different weather scenarios with the help of modified IEEE 37-bus and IEEE 123-bus system. The simulation approach is also extended to evaluate the impacts of different planning measures on the proposed resilience metrics.Comment: 12 pages, 11 figures, journa

A Risk Assessment Model on Pine Wood Nematode in the EU

Pine wood nematode, B. xylophilus poses a serious threat for the European forest industry. This study applies a quantitative risk assessment to analyze the risk of pine wood nematode in the EU, by estimating the reduction expected within forestry stock available for wood supply and its downstream roundwood market. Spatial analysis is used to join information on climate suitability, host distribution, pest spread and value of assets. Economic impacts are presented spatially on a NUTS-2 scale based on partial budgeting technique and for the EU as a whole based on partial equilibrium modeling. Results highlight the Southern regions of Europe as high risk areas with a total impact on available forestry stock of 19,000 M € after 20 years of an outbreak and no regulatory control measures. Welfare analysis of the roundwood market, in which its production represents 2,5% of forestry stock, demonstrates the ability of the producers to pass most of the negative impact to the consumers by charging higher prices. Reduction in social welfare estimated at 2,043 M €, where consumer surplus decreased by 2,622 M € and net producer surplus, affected and non-affected producers, increased by 579 M €.Risk assessment, pine wood nematode, economic analysis, EU, Crop Production/Industries, Risk and Uncertainty,

Assessing Financial Loss due to Pluvial Flooding and the Efficacy of Risk-Reduction Measures in the Residential Property Sector

The final publication is available at Springer via http://dx.doi.org/10.1007/s11269-014-0833-6A novel quantitative risk assessment for residential properties at risk of pluvial flooding in Eindhoven, The Netherlands, is presented. A hydraulic model belonging to Eindhoven was forced with low return period rainfall events (2, 5 and 10-year design rainfalls). Three scenarios were analysed for each event: a baseline and two risk-reduction scenarios. GIS analysis identified areas where risk-reduction measures had the greatest impact. Financial loss calculations were carried out using fixed-threshold and probabilistic approaches. Under fixed-threshold assessment, per-event Expected Annual Damage (EAD) reached €38.2 m, with reductions of up to €454,000 resulting from risk-reduction measures. Present costs of flooding reach €1.43bn when calculated over a 50-year period. All net-present value figures for the risk-reduction measures are negative. Probabilistic assessment yielded EAD values up to more than double those of the fixed-threshold analysis which suggested positive net-present value. To the best of our knowledge, the probabilistic method based on the distribution of doorstep heights has never before been introduced for pluvial flood risk assessment. Although this work suggests poor net-present value of risk-reduction measures, indirect impacts of flooding, damage to infrastructure and the potential impacts of climate change were omitted. This work represents a useful first step in helping Eindhoven prepare for future pluvial flooding. The analysis is based on software and tools already available at the municipality, eliminating the need for software upgrading or training. The approach is generally applicable to similar cities.European Commission Seventh Framework Program (EC FP7

Jump liquidity risk and its impact on CVaR

Purpose – The aim is to study jump liquidity risk and its impact on risk measures: value at risk (VaR) and conditional VaR (CVaR). Design/methodology/approach – The liquidity discount factor is modelled with mean revision jump diffusion processes and the liquidity risk is integrated in the framework of VaR and CVaR. Findings – The standard VaR, CVaR, and the liquidity adjusted VaR can seriously underestimate the potential loss over a short holding period for rare jump liquidity events. A better risk measure is the liquidity adjusted CVaR which gives a more realistic loss estimation in the presence of the liquidity risk. An efficient Monte Carlo method is also suggested to find approximate VaR and CVaR of all percentiles with one set of samples from the loss distribution, which applies to portfolios of securities as well as single securities. Originality/value – The paper offers plausible stochastic processes to model liquidity risk

New Risk Measures: Magnitude and Propensity Approach

openRisk measurement, an interdisciplinary field that incorporates probabilistic modeling, data analysis, algorithmic efficiency, and financial markets, is a critical aspect of modern risk management. Traditional risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) offer a single deterministic value representing the potential losses in a given distribution. However, these one-dimensional risk measures may not adequately capture the complexity of real-world risks. This study investigates the integration of magnitude and propensity in risk analysis to enhance risk assessment and decision-making processes. The objective is to develop a comprehensive framework that combines these two key dimensions to provide a more nuanced perspective on risk management. By leveraging historical data and statistical techniques, the research quantifies the frequency and severity of risks, leading to a deeper understanding of their impact. Real-world data and case studies are analyzed to contribute to the advancement of risk measurement and evaluation practices. By offering a more nuanced and robust characterization of risk, the proposed three-dimensional magnitude-propensity approach has the potential to enhance risk management practices across various domains, ultimately contributing to more informed decision-making and improved financial stability

Extreme Risk, Value-At-Risk And Expected Shortfall In The Gold Market

Extreme value theory (EVT) has been widely applied in fields such as hydrology and insurance. It is a tool used to reflect on probabilities associated with extreme, and thus rare, events. EVT is useful in modeling the impact of crashes or situations of extreme stress on investor portfolios. It describes the behavior of maxima or minima in a time series, i.e., tails of a distribution. In this paper, we propose the use of generalised Pareto distribution (GPD) to model extreme returns in the gold market. This method provides effective means of estimating tail risk measures such as Value-at-Risk (VaR) and Expected Shortfall (ES). This is confirmed by various backtesting procedures. In particular, we utilize the Kupiec unconditional coverage test and the Christoffersen conditional coverage test for VaR backtesting, while the Bootstrap test is used for ES backtesting. The results indicate that GPD is superior to the traditional Gaussian and Student’s t models for VaR and ES estimations

Exchange Rate Risk and International Equity Portfolio Diversification: A South African Investor’s Perspective

This paper examines the impact of foreign exchange rate risk on the expected return of a South African investor’s portfolio. A GJR-GARCH based Value at Risk (VaR) model was used to compute the upside and downside risk measures. Data sample of ten emerging stock markets were utilized: from 1 January 2000 to 6 March 2019. The tails of negative and positive asset returns were modelled with the help of the generalized Pareto distribution (GPD) method in order to separate left tail risk from right tail risk. Our findings reveal that international diversification substantially enhances the South African investor’s portfolio return, with a noticeable yield increase in China, Brazil, Argentina, Mexico, and Russia. Furthermore, the Singaporean dollar and Chinese Yuan are found to have a negative impact on the portfolio return, while the rest of the currencies have a positive impact on the portfolio return. Also, we found that exchange rate risk is underestimated when using the variance-covariance method as it fails to capture the swing movement of currency in the minimum- value at risk optimization