Technical paper/Value-at-risk (VAR)
Forecasting extreme tail risk in China’s banking sector: an approach based on a component generalized autoregressive conditional heteroscedasticity and mixed data sampling model and extreme value theory
The authors put forward a means to forecast extreme tail risk in the Chinese banking sector - the component GARCH-MIDAS-EVT-X model.
Risk measures associated with insurance losses in Ghana
The authors investigate VaR and TVaR comprehensive motor insurance claims paid by an insurance company in Ghana and compare the estimates obtained by these risk measures.
Overcoming issues with time-scaling value-at-risk
The authors investigate the impact of different time-scaling techniques on the accuracy of value-at- risk models, emphasising the importance of carefully scaling methods and considering alternative risk modeling approaches.
Correlation breakdowns, spread positions and central counterparty margin models
The authors investigate correlation behavior during adverse market conditions and the potential impact on CCP margins, finding that such breakdowns appear to be more common than expected.
Volatility-sensitive Bayesian estimation of portfolio value-at-risk and conditional value-at-risk
The authors put forward a new means to integrate volatility information in the estimation of value-at-risk and conditional value-at-risk which is shown to be effective in risk estimation during volatile market conditions.
A study of China’s financial market risks in the context of Covid-19, based on a rolling generalized autoregressive score model using the asymmetric Laplace distribution
The authors construct a risk measurement model for the financial market during the Covid-19 pandemic, using data from the Shanghai Stock Exchange for empirical analysis.
Semi-nonparametric estimation of operational risk capital with extreme loss events
The authors put forward a means to estimate value-at-risk capital during extreme loss events which combines SNP estimation with EVT-POT theory.
Conditional and unconditional intraday value-at-risk models: an application to high-frequency tick-by-tick exchange-traded fund data
The authors consider conditional and unconditional intraday value-at-risk models for high-frequency exchange-traded funds, providing results useful to practitioners of high-frequency trading.
Credible value-at-risk
This paper proposes a means to determine whether a a calculated VaR is "too large" and give a definition of this term within the context.
Realized quantity extended conditional autoregressive value-at-risk models
The author presents models for improved Value-at-Risk forecasts and joint forecasts of Value at Risk and Expected Shortfall and demonstrates that high-frequency-data-based realized quantities lead to better forecasts.
Using a skewed exponential power mixture for value-at-risk and conditional value-at-risk forecasts to comply with market risk regulation
The authors investigate a method that combines two skewed exponential power distributions and models the conditional forecasting of VaR and CVaR and is in compliance with the recent Basel framework for market risk.
Value-at-risk models: a systematic review of the literature
The authors conduct a systematic literature review of value-at-risk models to determine which models are most often used and whether any change in model popularity occurred after the 2007-9 financial crisis.
Value-at-risk and the global financial crisis
The authors investigate the forecasting ability of bank VaR estimates around the 2007-9 financial crisis using daily data from seven international banks, finding systemic overstating of VaR either side of the financial crisis and mixed performance during…
Measuring tail operational risk in univariate and multivariate models with extreme losses
The authors consider operational risk models and derive limit behaviors for the value-at-risk and conditional tail expectation of aggregate operational risks in such models.
Semiparametric GARCH models with long memory applied to value-at-risk and expected shortfall
The authors introduce and apply new semiparametric GARCH models with long memory to obtain rolling one-step ahead forecasts for the value-at-risk and expected shortfall (ES) for market risk assets.
Modeling very large losses. II
This paper presents a means to estimate very large losses by supposing the event is the result of a succession of factors and estimating the probability of each factor.
Deep learning for efficient frontier calculation in finance
The author puts forward a means to calculate the efficient frontier in the Mean-Variance and Mean-CVaR portfolio optimization problems using deep neural network algorithms.
Oil value-at-risk forecasts: a filtered semiparametric approach
This paper proposes the GARCH model combined with the Cornish–Fisher expansion for the oil VaR forecast.
The importance of window size: a study on the required window size for optimal-quality market risk models
In this paper the authors study different moving-window lengths for value-at-risk evaluation, and also address subjectivity in choosing the window size by testing change point detection algorithms.
Estimating value-at-risk using quantile regression and implied volatilities
In this paper the authors propose a semi-parametric, parsimonious value-at-risk forecasting model based on quantile regression and readily available market prices of option contracts from the over-the-counter foreign exchange interbank market.
Evaluation of backtesting on risk models based on data envelopment analysis
In this study, different value-at-risk models, which are used to measure market risk, are analyzed under different estimation approaches and backtested with an alternative strategy.