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Paper / 2024SMQ / 31723958826147
Tail risk forecasting with semi-parametric regression models by incorporating overnight information
Cathy W. S. Chen / Takaaki Koike / Wei-Hsuan Shau
regression / risk managementResource overview

Tail risk forecasting with semi-parametric regression models by incorporating overnight information

Paper2024

by Cathy W. S. Chen, Takaaki Koike, Wei-Hsuan Shau

Tags
regressionrisk managementstatistics

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Suggested foundations for financial time series and risk measurement and stress. Specialist prerequisites vary by paper.

Before you read

  1. Stationarity and Unit Roots

    Check whether a relationship can be estimated consistently over time.

  2. Linear Regression

    Read the fitted relationship and its residuals.

  3. Random Variables and Distributions

    Represent losses as a distribution rather than one forecast.

  4. Expected Shortfall and Other Tail Risk Measures

    Distinguish a loss quantile from the average loss in the tail.

Go further

  1. Cointegration and Error Correction

    Study long-run relationships without confusing them with correlation.

  2. Regression Assumptions and Diagnostics

    Check the assumptions behind reported estimates.

  3. VaR Estimation in Practice: Variance-Covariance, Historical Simulation, Monte Carlo

    Compare risk estimators and their assumptions.

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