Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk

2017 ◽  
Author(s):  
Fahed Mostafa ◽  
Tharam Dillon ◽  
Elizabeth Chang
Keyword(s):  
At Risk ◽  
Option Pricing ◽  
Computational Intelligence ◽  
Value At Risk ◽  
Volatility Forecasting

scholarly journals Volatility forecasting and value-at-risk estimation in emerging markets: the case of the stock market index portfolio in South Africa

2011 ◽  
Vol 12 (4) ◽  
pp. 401-411 ◽  
Author(s):  
Lumengo Bonga-Bonga ◽  
George Mutema
Keyword(s):  
At Risk ◽  
Stock Market ◽  
Emerging Markets ◽  
Value At Risk ◽  
Risk Estimation ◽  
Volatility Forecasting ◽  
Stock Market Index ◽  
Market Index ◽  
Auto Regressive ◽  
Asymmetric Behaviour

Accurate modelling of volatility is important as it relates to the forecasting of Value-at-Risk (VaR). The RiskMetrics model to forecast volatility is the benchmark in the financial sector. In an important regulatory innovation, the Basel Committee has proposed the use of an internal method for modelling VaR instead of the strict use of the benchmark model. The aim of this paper is to evaluate the performance of RiskMetrics in comparison to other models of volatility forecasting, such as some family classes of the Generalised Auto Regressive Conditional Heteroscedasticity models, in forecasting the VaR in emerging markets. This paper makes use of the stock market index portfolio, the All-Share Index, as a case study to evaluate the market risk in emerging markets. The paper underlines the importance of asymmetric behaviour for VaR forecasting in emerging markets’ economies.

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