Journal of Risk

Farid AitSahlia    
Warrington College of Business, University of Florida

This issue of The Journal of Risk covers systemic risk assessment accounting for capital allocation; aggregation of individual positions for risk management; efficient Bermudan swaption pricing; and extreme tail risk forecasting in the Chinese banking sector.

In the issue’s first paper, “Asymptotic behavior of systemic risk based on the higher-moment capital allocation”, Maojie Ye, Jiangyan Peng and Chenghao Xu investigate the asymptotic properties of systemic risk under multivariate regular variation for dependence, as well as quasi-asymptotic independence. Using this framework, Ye et al examine the tail behavior of aggregated random variables and derive asymptotic expressions for systemic expected shortfall and marginal expected shortfall. This approach accommodates heavy-tailed losses and complex dependence structures, offering a more realistic approach to modeling financial systems, especially in view of the 2007–9 global financial crisis.

The second paper in this issue, “Berms without calibration” by Konstantin E. Feldman, proposes a new semi-analytical pricing model for Bermudan swaptions. Traditional term structure models, such as the linear Gauss–Markov-style model, involve calibrating volatility and mean-reversion curves, which can be difficult due to a lack of liquidly traded products for forward volatility in the interest rate market. In contrast, the proposed model eliminates the need for preliminary product-specific calibration, resulting in a pricing method that is more simple and accurate, as illustrated on market data.

In our third paper, “A primer on generalized weighted risk functionals”, Nawaf Mohammed, Edward Furman and Jianxi Su develop a risk management approach to aggregate financial positions. Instead of the commonly used standard weighted sum of random variables, Mohammed et al propose generalized weighted risk functionals, for which they identify conditions to rank risk measures and isolate the separate impacts of aggregation and randomness.

The issue’s fourth and final paper, “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” by Xiaobin Du and Yan Sun, suggests an approach integrating short-term volatility and long-term trends – respectively captured by GARCH and MIDAS – to effectively address the limitations of traditional GARCH models in nonstationary data. The authors further account for macroeconomic variables (such as the Macroeconomic Sentiment Index, broad monetary supply (M2) and the Economic Policy Uncertainty Index) in the generalized Pareto distribution scale parameter to enhance the dynamic prediction of tail risks. Using Chinese banking data, Du and Sun demonstrate the superior performance of their approach relative to traditional GARCH, GARCH-EVT and other benchmark models, especially at high confidence levels (99% and 99.5%).