Journal of Risk


Farid AitSahlia

Warrington College of Business, University of Florida

The use of margins to manage risk in central clearing counterparties (CCPs) is well established but not well understood. This issue of The Journal of Risk contains a paper that offers a remedy to address their procyclicality – a problem that is further exacerbated during financial crises. The other papers in this issue address volatility estimation, interest rate risk management, and the impact of the supply chain on stock return volatility.

In our first paper, “Procyclicality mitigation for initial margin models with asymmetric volatility”, Elena Goldman and Xiangjin Shen propose an approach for setting margins for CCPs so that they dynamically adjust to changing market conditions in a way that reduces the risk of spiraling liquidity, which ultimately affects the stability of the financial system. The setting of these margins depends on estimates of value-at-risk. The authors show that an asymmetric generalized autoregressive conditional heteroscedasticity (GARCH) model capturing high-frequency return volatility and low-frequency macroeconomic volatility in an asymmetric fashion performs better than the ad hoc approaches commonly in use.

In “Range-based volatility forecasting: a multiplicative component conditional autoregressive range model”, the issue’s second paper, Haibin Xie makes use of the high–low price range for the estimation of volatility in a manner that also accounts for the long-memory effect. Xie proposes an extended conditional autoregressive range model, and demonstrates empirically, via S&P 500 index data, that it is superior to its alternatives.

The third paper in the issue, “Integrating macroeconomic variables into behavioural models for interest rate risk measurement in the banking book” by Zhongfang He, addresses the challenge of accounting for the correlation between critical macroeconomic variables and interest rates. The author’s approach is nonparametric and relies on a decomposition of macroeconomic variables into their conditional expectations and a residual term, thus reducing the problem to the optimization of a mean squared loss function, where projection properties can be exploited. This approach is easy to implement and is illustrated with empirical bank data.

In our fourth and final paper, “Volatility spillover along the supply chains: a network analysis on economic links”, Theo Berger and Ramazan Gençay investigate the effect of a customer’s fundamental risk on a supplier’s volatility. They find that suppliers are significantly exposed to additional fundamental risk that is not captured by their market betas if their customers experience large losses beyond their individual value-at-risk limits.

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