Bulgarian Stock Market and Market Risk Forecasting under Long Memory in Returns Cover Image

Bulgarian Stock Market and Market Risk Forecasting under Long Memory in Returns
Bulgarian Stock Market and Market Risk Forecasting under Long Memory in Returns

Author(s): Boyan Lomev, Nikolay Netov
Subject(s): Economy, National Economy, Supranational / Global Economy, Socio-Economic Research
Published by: Софийски университет »Св. Климент Охридски«
Keywords: value-at-risk (VaR); long memory; Monte Carlo simulation; Balkans stock markets

Summary/Abstract: The Basel Committee on banking supervision at the Bank for International Settlements requires financial institutions to meet capital requirements on base VaR estimates, which has made the VaR methodology a fundamental market risk management tool employed by the financial institutions. Although it is widely used, the practicability of VaR was questioned and the traditional approaches to VAR computations – the variance-covariance method, historical simulation, Monte Carlo simulation, and stress-testing – were claimed to provide a non-satisfactory evaluation of possible losses for stock markets with long memory in returns. The main research question of this paper is: is there any underestimation of the maximum probable loss earned on the next trading day assessed by the Monte Carlo simulation approach for risk estimation using VaR measure when applied for capital markets showing long memory in returns? The study also brings a localized flavor by exploring if the following statement is correct: the Monte Carlo simulation approach for risk estimation using VaR measure does not produce adequate results when applied to the Bulgarian capital market and modifications of the classical approach that give a more precise measure could be suggested. The test of this hypothesis has indicated that there is an underestimation of the maximum probable loss earned on the next trading day when the forecast is done with the Monte Carlo simulation approach (which could be attributed to the presence of long memory in returns and could mean that another analytical approach should be applied). JEL classification: C22, C53; G17

  • Issue Year: 13/2016
  • Issue No: 1
  • Page Range: 185-200
  • Page Count: 16
  • Language: English