This study aims to test the sufficiency of the solvency capital requirement approach for calculating operational risk using the standard formula as defined in Solvency II.
In this study different value-at-risk (VaR) models are analyzed under different estimation approaches (filtered historical simulation, extreme value theory and Monte Carlo simulation) and backtested with different techniques.
This paper not only provides a theoretical model for the value-at-risk of active and passive trading strategies but also discusses the substantial implications relevant to risk management.
Conservative capital buffers may not be enough to protect against tail events
This paper develops a method for estimating value-at-risk and conditional value-at-risk when the underlying risk factors follow a beta distribution in a univariate and a matrix-variate setting.
Measurement of operational risk regulatory capital in the banking sector: developed countries versus emerging markets
This paper addresses operational risk as a fundamental risk type faced by banks in emerging and developed economies.
The author evaluates the usefulness of bias-correction methods in enhancing the Vasicek model for market risk and counterparty risk management practices.
An optimal hedging strategy for options in discrete time using a reinforcement learning technique
The authors investigate the performance of various value-at-risk (VaR) models in the context of the highly volatile Nordic power futures market, examining whether simple averages of models provide better results than the individual models themselves.
The analysis in this paper reveals that additional fundamental risk gets transferred along supply chains, and that suppliers are exposed to additional fundamental risk that is not captured by their market beta. Suppliers are therefore exposed to…
Old-fashioned parametric models are still the best: a comparison of value-at-risk approaches in several volatility states
The authors present backtesting results for 1% and 2.5% VaR of six indexes from emerging and developed countries using several of the best-known VaR models, including generalized autoregressive conditional heteroscedasticity (GARCH), extreme value theory…
This research develops a framework adopting conditional covariance modeling combined with various de-noising methods to estimate the portfolio VaR and proves the importance of DCC over the sample rolling method widely used in the industry.
Value-at-risk in the European energy market: a comparison of parametric, historical simulation and quantile regression value-at-risk
This paper examines a set of value-at-risk (VaR) models and their ability to appropriately describe and capture price-change risk in the European energy market.
The authors propose a model for conduct risk losses, in which conduct risk losses are characterized by having a small number of extremely large losses (perhaps only one) with more numerous smaller losses.
Measuring expected shortfall under semi-parametric expected shortfall approaches: a case study of selected Southern European/Mediterranean countries
In this paper, the authors investigate the applicability of semi-parametric approaches for estimating expected shortfall.
In this paper, the authors introduce a new ES backtesting framework based on the duality between coherent risk measures and scale-invariant performance measures.
Pascal Traccucci et al present an extended reverse stress test triptych approach with three variables
This paper investigates the effects of window-size selection on various models for value-at-risk (VaR) forecasting using high-performance computing.
This paper studies the volatility of the Euler rule for capital allocation in static and dynamic empirical applications with a simulated history.
Alvin Stroyny and Tim Wilding build a dynamic risk framework for multi-asset global portfolios
Acerbi and Szekely present a backtest for expected shortfall
This paper proposes an efficient method to obtain the distribution of the CVA at a given risk horizon, from which risk measures such as the CVA VaR can be computed.
In this paper, the authors propose a modification of expected shortfall that does not treat all losses equally. We do this in order to represent the worries surrounding big drops that are typical of multiperiod investors.