Scenario-based principal component value-at- risk when the underlying risk factors are skewed and heavy-tailed: an application to Italian banks' interest rate risk exposure
Purpose
The purpose of this paper is to study connections between interest rate risk and credit risk and investigate the inter-risk diversification benefit due to the joint consideration of these risks in the banking book containing sovereign debt.
Design/methodology/approach
The paper develops the historical derivative-based value at risk (VaR) for assessing the downside risk of a sovereign debt portfolio through the integrated treatment of interest rate and credit risks. The credit default swaps spreads and the fixed-leg rates of interest rate swap are used as proxies for credit risk and interest rate risk, respectively.
Findings
The proposed methodology is applied to the decade-long history of emerging markets sovereign debt. The empirical analysis demonstrates that the diversified VaR benefits from imperfect correlation between the risk factors. Sovereign risks of non-core emu states and oil producing countries are discussed through the prism of VaR metrics.
Practical implications
The proposed approach offers a clue for improving risk management in regards to banking books containing government bonds. It could be applied to access the riskiness of investment portfolios containing the wider spectrum of assets beyond the sovereign debt. The approach represents a useful tool for investigating interest rate and credit risk interrelation.
Originality/value
The proposed enhancement of the traditional historical VaR is twofold: usage of derivative instruments’ quotes and simultaneous consideration of the interest rate and credit risk factors to construct the hypothetical liquidity-free bond yield, which allows to distil liquidity premium.
PurposeThis paper aims to propose a scenario-based approach for measuring interest rate risks. Many regulatory capital standards in banking and insurance make use of similar approaches. The authors provide a theoretical justification and extensive backtesting of our approach.Design/methodology/approachThe authors theoretically derive a scenario-based value-at-risk for interest rate risks based on a principal component analysis. The authors calibrate their approach based on the Nelson–Siegel model, which is modified to account for lower bounds for interest rates. The authors backtest the model outcomes against historical yield curve changes for a large number of generated asset–liability portfolios. In addition, the authors backtest the scenario-based value-at-risk against the stochastic model.FindingsThe backtesting results of the adjusted Nelson–Siegel model (accounting for a lower bound) are similar to those of the traditional Nelson–Siegel model. The suitability of the scenario-based value-at-risk can be substantially improved by allowing for correlation parameters in the aggregation of the scenario outcomes. Implementing those parameters is straightforward with the replacement of Pearson correlations by value-at-risk-implied tail correlations in situations where risk factors are not elliptically distributed.Research limitations/implicationsThe paper assumes deterministic cash flow patterns. The authors discuss the applicability of their approach, e.g. for insurance companies.Practical implicationsThe authors’ approach can be used to better communicate interest rate risks using scenarios. Discussing risk measurement results with decision makers can help to backtest stochastic-term structure models.Originality/valueThe authors’ adjustment of the Nelson–Siegel model to account for lower bounds makes the model more useful in the current low-yield environment when unjustifiably high negative interest rates need to be avoided. The proposed scenario-based value-at-risk allows for a pragmatic measurement of interest rate risks, which nevertheless closely approximates the value-at-risk according to the stochastic model.
Interest rate, liquidity, and sovereign risk: derivative-based VaR