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The Effects of Risk Modelling: Assessing Value-at-Risk Accuracy

Author

Listed:
  • Zatul Karamah Ahmad Baharul-Ulum

    (Faculty of Entrepreneurship and Business, Universiti Malaysia Kelantan, Malaysia)

  • Ismail Ahmad

    (Arshad Ayub Graduate Business School, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia)

  • Norhana Salamudin

    (Arshad Ayub Graduate Business School, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia)

  • Norzaidi Mohd Daud

    (Office of Community of Research, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia.)

Abstract

This study examines Value-at-Risk (VaR) models that are integrated with several volatility representations to estimate the market risk for seven nonfinancial sectors traded on the first board of the Malaysian stock exchange. In a sample that spanned 19 years from1993 until 2012 for construction, consumer product, industrial product, plantation, property, trade and services and mining sectors, the expected maximum losses are quantified at 95% confidence level. For accuracy determination, assessments using Kupiec test and Christoffersen test have provided evidence that almost every model are found to be accurate for all sets of occurrence. However, using the Lopez test which takes into consideration the magnitude of the impact of exceptions, the most accurate model is the VaR which is integrated with GARCHt. This study found that fat tails and asymmetries are important issues that need to be considered when estimating VaR in managing financial risks.

Suggested Citation

  • Zatul Karamah Ahmad Baharul-Ulum & Ismail Ahmad & Norhana Salamudin & Norzaidi Mohd Daud, 2015. "The Effects of Risk Modelling: Assessing Value-at-Risk Accuracy," Institutions and Economies (formerly known as International Journal of Institutions and Economies), Faculty of Economics and Administration, University of Malaya, vol. 7(2), pages 1-29, July.
    • Handle: RePEc:umk:journl:v:7:y:2015:i:2:p:1-29
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    More about this item

    Keywords

    Backtesting; Value-at-Risk; Volatility Modelling;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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