scholarly journals Modelling Daily Value-At-Risk Using Realized Volatility and Arch Type Models

2003 ◽  
Author(s):  
Pierre Giot ◽  
Sebastien Laurent
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Realized Volatility

Volatility Forecast by Volatility Index and Its Use as a Risk Management Tool Under a Value-at-Risk Approach

2018 ◽  
Vol 21 (02) ◽  
pp. 1850010 ◽  
Author(s):  
Yam Wing Siu
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Risk Premium ◽  
Value At Risk ◽  
Realized Volatility ◽  
Management Tool ◽  
Spot Price ◽  
Pricing Options ◽  
Significance Levels ◽  
Risk Approach

This paper examines the predicting power of the volatility indexes of VIX and VHSI on the future volatilities (or called realized volatility, [Formula: see text] of their respective underlying indexes of S&P500 Index, SPX and Hang Seng Index, HSI. It is found that volatilities indexes of VIX and VHSI, on average, are numerically greater than the realized volatilities of SPX and HSI, respectively. Further analysis indicates that realized volatility, if used for pricing options, would, on some occasions, result in greatest losses of 2.21% and 1.91% of the spot price of SPX and HSI, respectively while the greatest profits are 2.56% and 2.93% of the spot price of SPX and HSI, respectively, making it not an ideal benchmark for validating volatility forecasting techniques in relation to option pricing. Hence, a new benchmark (fair volatility, [Formula: see text] that considers the premium of option and the cost of dynamic hedging the position is proposed accordingly. It reveals that, on average, options priced by volatility indexes contain a risk premium demanded by the option sellers. However, the options could, on some occasions, result in greatest losses of 4.85% and 3.60% of the spot price of SPX and HSI, respectively while the greatest profits are 4.60% and 5.49% of the spot price of SPX and HSI, respectively. Nevertheless, it can still be a valuable tool for risk management. [Formula: see text]-values of various significance levels for value-at-risk and conditional value-at-value have been statistically determined for US, Hong Kong, Australia, India, Japan and Korea markets.

scholarly journals Forecasting Realized Volatility of Russian stocks using Google Trends and Implied Volatility

2019 ◽  
Vol 12 (1) ◽  
pp. 79-88
Author(s):  
T. Bazhenov ◽  
D. Fantazzini
Keyword(s):  
At Risk ◽  
Empirical Evidence ◽  
Value At Risk ◽  
Implied Volatility ◽  
Realized Volatility ◽  
Google Trends ◽  
Probability Level ◽  
Sample Analysis ◽  
Search Activity ◽  
Additional Information

This work proposes to forecast the Realized Volatility (RV) and the Value-at-Risk (VaR) of the most liquid Russian stocks using GARCH, ARFIMA and HAR models, including both the implied volatility computed from options prices and Google Trends data. The in-sample analysis showed that only the implied volatility had a significant effect on the realized volatility across most stocks and estimated models, whereas Google Trends did not have any significant effect. The outof-sample analysis highlighted that models including the implied volatility improved their forecasting performances, whereas models including internet search activity worsened their performances in several cases. Moreover, simple HAR and ARFIMA models without additional regressors often reported the best forecasts for the daily realized volatility and for the daily Value-at-Risk at the 1 % probability level, thus showing that efficiency gains more than compensate any possible model misspecifications and parameters biases. Our empirical evidence shows that, in the case of Russian stocks, Google Trends does not capture any additional information already included in the implied volatility.

Value-at-Risk Forecasting using Realized Volatility Models and GARCH-type Models

2013 ◽  
Vol 21 (2) ◽  
pp. 135-167
Author(s):  
Chan-Soo Jeon
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Realized Volatility ◽  
Distribution Model ◽  
Distribution Models ◽  
Volatility Models ◽  
Risk Forecasting ◽  
T Distribution ◽  
Daily Returns ◽  
Skewed T Distribution

The aim of this paper is to compare the performance of VaR (value-at-risk) using Realized Volatility Models (which use intraday returns) with VaR the performance of GARCH-type Models (which use daily returns) with three different distribution innovations (normal distribution, t-distribution, skewed t-distribution). In this paper, we empirically examine VaR forecast of korean stock market using KOSPI and KOSDAQ. Empirical results indicate that the Realized Volatility models is superior to the GARCH-type models in forecasting VaR. We also find Var forecast by skewed t-distribution model are more accurate than those using the normal and t-distribution models. Thus, VaR using Realized Volatility models and skewed t-distribution enhances the performance of risk management in Korean financial markets.

Realized volatility models and alternative Value-at-Risk prediction strategies

2014 ◽  
Vol 40 ◽  
pp. 101-116 ◽  
Author(s):  
Dimitrios P. Louzis ◽  
Spyros Xanthopoulos-Sisinis ◽  
Apostolos P. Refenes
Keyword(s):  
At Risk ◽  
Risk Prediction ◽  
Value At Risk ◽  
Realized Volatility ◽  
Volatility Models
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