Journal of Risk

Farid AitSahlia    
Warrington College of Business, University of Florida

Risk measures in the context of ambiguity, the link between correlation and coskewness, systemic and contagion risk within the Chinese financial markets, and anticipatory high-frequency trading are the four topics covered in the papers that make up this issue of The Journal of Risk.

In the issue’s first paper, “A robust distorted Orlicz premium: modeling, computational scheme and applications”, Qiong Wu and Huifu Xu address model ambiguity relating to the subjective probability distribution of the underlying uncertainty or a multiplicity of Young functions by using Orlicz distorted risk measures and their robust versions, namely the coherent Haezendonck–Goovaerts risk measures. Wu and Xu characterize the properties of the premiums for these measures and provide a portfolio optimization implementation based on linear programming and convex programs.

In “Modeling coskewness with zero correlation and correlation with zero coskewness”, the second paper in the issue, Carole Bernard, Jinghui Chen and Steven Vanduffel demonstrate that a correlation of zero can coexist with any degree of coskewness, and, conversely, that zero coskewness can occur alongside any level of correlation. The authors further extend these findings to the rank-based analogues: Spearman’s rho and rank coskewness. In effect, their findings are a cautionary tale regarding the interpretation of potential links between coskewness and correlation.

Our third paper, “The connectedness, structure and performance of different financial networks”, is by Ye Wuyi, Wang Xuhui, Li Mingge and Guo Ranran, who conduct an extensive empirical comparison of four quantitative network models in the context of the Chinese financial system: vector autoregression-forecast error variance decomposition (VAR-FEVD), quantile vector autoregression-forecast error variance decomposition (QVAR-FEVD), linear conditional value-at-risk (CoVaR) and tailevent driven network (TENET). The study evaluates how each model captures systemic risk and financial contagion, assesses their performance relative to “physical” networks (based on geographic and ownership proximities) and examines their explanatory power using the dynamic network quantile regression (DNQR) framework. The paper also tests each model’s ability to identify systemically important financial institutions (SIFIs), benchmarking these against regulatory classifications in China. The authors find that the TENET model performs best, particularly in capturing nonlinear tail dependencies and identifying key systemic institutions.

Closing out the issue in its fourth paper, “The implications of extraordinary speed in contemporary financial markets trading”, Viktor Manahov presents evidence of systematic anticipatory trading, where the cancellation of trading orders within 20 milliseconds of posting leads to arbitrage opportunities. Based on data for the E-mini S&P 500 and the SPDR S&P 500 ETF Trust, Manahov shows that anticipatory high-frequency trades generate substantial profits and risk-adjusted returns, while market participants lacking latency and sophisticated trading algorithms will likely encounter higher execution costs. As a practical policy measure, he proposes the implementation of batch auctions, which will mitigate queuing risk for high-frequency traders and yield beneficial consequences for market quality.