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Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intraday Range
Chen, C.W.S. / Gerlach, R. / Hwang, B.B.K. / McAleer, M.J.
backtesting / bayesian statisticsResource overview

Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intraday Range

Paper2011

by Chen, C.W.S., Gerlach, R., Hwang, B.B.K., McAleer, M.J.

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backtestingbayesian statisticsmarket riskrisk managementstatistical inferencetime series

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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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Chen, C.W.S., Gerlach, R., Hwang, B.B.K., McAleer, M.J.

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