Estimating Systemic Risk within Financial Networks: A Two-Step Nonparametric Method

ArXiv ID: 2310.18658 “View on arXiv”

Authors: Unknown

Abstract

CoVaR (conditional value-at-risk) is a crucial measure for assessing financial systemic risk, which is defined as a conditional quantile of a random variable, conditioned on other random variables reaching specific quantiles. It enables the measurement of risk associated with a particular node in financial networks, taking into account the simultaneous influence of risks from multiple correlated nodes. However, estimating CoVaR presents challenges due to the unobservability of the multivariate-quantiles condition. To address the challenges, we propose a two-step nonparametric estimation approach based on Monte-Carlo simulation data. In the first step, we estimate the unobservable multivariate-quantiles using order statistics. In the second step, we employ a kernel method to estimate the conditional quantile conditional on the order statistics. We establish the consistency and asymptotic normality of the two-step estimator, along with a bandwidth selection method. The results demonstrate that, under a mild restriction on the bandwidth, the estimation error arising from the first step can be ignored. Consequently, the asymptotic results depend solely on the estimation error of the second step, as if the multivariate-quantiles in the condition were observable. Numerical experiments demonstrate the favorable performance of the two-step estimator.

Keywords: CoVaR, conditional quantile, Monte-Carlo simulation, nonparametric estimation, multivariate-quantiles, Systemic Risk / Financial Networks

Complexity vs Empirical Score

• Math Complexity: 9.0/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats
• Why: The paper presents advanced statistical theory including consistency proofs, asymptotic normality derivations, and complex kernel-based nonparametric methods, indicating high mathematical complexity. While it uses Monte Carlo simulations for validation, the lack of backtesting against real market data, absence of implementation code, and reliance on synthetic data limit its empirical readiness for trading.

  flowchart TD
    Start["Research Goal<br>Estimate CoVaR for<br>Systemic Risk in Financial Networks"] --> Problem["Problem<br>Unobservable Multivariate-Quantiles<br>Condition for CoVaR"]
    Problem --> Method["Proposed Method<br>Two-Step Nonparametric Estimation"]
    Method --> Data["Input<br>Monte-Carlo Simulation Data"]
    Data --> Step1["Step 1: First-Step Estimation<br>Estimate Multivariate-Quantiles<br>Using Order Statistics"]
    Step1 --> Step2["Step 2: Kernel Estimation<br>Estimate Conditional Quantile<br>Conditioned on Order Statistics"]
    Step2 --> Results["Outcomes<br>Consistent & Asymptotically Normal Estimator<br>Favorable Performance in Numerical Experiments"]
    
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    style Problem fill:#fff3e0,stroke:#e65100,stroke-width:2px
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    style Data fill:#f3e5f5,stroke:#4a148c,stroke-width:2px
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