TITLE:
Value at Risk (VaR) Historical Approach: Could It Be More Historical and Representative of the Real Financial Risk Environment?
AUTHORS:
Evangelos Vasileiou
KEYWORDS:
Value at Risk, Risk Analysis, Volatility Regimes, US Stock Market, Eurozone Stock Market, Risk Measures Accuracy
JOURNAL NAME:
Theoretical Economics Letters,
Vol.7 No.4,
June
19,
2017
ABSTRACT: The purpose of this paper is to suggest a new
approach that improves the conventional Historical Value-at-Risk (HVaR)
estimations’ accuracy and can be easily applied by anyone. The main assumption
for the newly suggested method is “the more representative to the financial
conditions the data inputs are, the better the VaR estimations”. Volatility is
assumed to be the criterion for the “representative to the financial conditions”
definition. In practice, the newly suggested approach does not use the previous x days observations as data inputs in
the estimation process (as the HVaR does), but the last x filtered volatility (fv) observations of a “representative to the
current financial conditions dataset”. Depending on the volatility value, each
observation is classified in several regimes, from which the VaR is estimated
depending on the examined day’s volatility. This way the HVaR approach is more historical.
The empirical findings using data from the US and the Eurozone stock market show
that the newly suggested filtered volatility approach not only significantly
improves the VaR estimations, but also makes these estimations much more
representative of the real financial conditions. The results using the filtered
volatility approach are comparable to some previously documented VaR
estimations that adopt advanced econometric models. In this point, we should
note advanced econometric models have the drawbacks that are not usually
applied in financial markets industry because of their complexity. The newly suggested
approach: (i) popularizes some of the most advanced econometric techniques,
(ii) improves the VaR estimations accuracy, and (iii) enables financial risk
analysts and portfolio managers to estimate the risk-return under several
volatility regimes in order to help them to apply their desired investment
strategy. Finally, this paper not only examines accuracy using the
traditional/conventional tests [1], but also suggests some new measures for the
comparison amongst different VaR models and their ability to accurately
estimate the real financial risk.