Daily VAR Forecasts with Realized Volatility and GARCH Models
Daily VAR Forecasts with Realized Volatility and GARCH Models
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DOI：
10.15611/aoe.2015.1.06
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发表时间：
2015-05
影响因子：
0.5
通讯作者：
Barbara Będowska-Sójka
中科院分区：
文献类型：
--
作者：
Barbara Będowska-Sójka
在本文中，我们评估的风险价值（VaR）模型下的波动率预测方法。我们计算了2007年至2011年期间华沙证券交易所WIG 20指数每日风险价值的一步预测。我们的分析扩展了现有的研究，扩大了类的模型，包括基于每日数据的Gestival类模型和基于日内收益率的已实现波动率模型（HAR-RV，HAR-RV-J和ARFIMA）。我们发现，从每日收益率和已实现波动率模型中获得的VaR估计值给出了可比的结果。长记忆特征和不对称性都可以改善VaR预测。然而，当考虑损失函数时，基于每日数据的模型允许最小化监管损失函数，而基于已实现波动率的模型允许最小化资本的机会成本。
In this paper we evaluate alternative volatility forecasting methods under Value at Risk (VaR) modelling. We calculate one-step-ahead forecasts of daily VaR for the WIG20 index quoted on the Warsaw Stock Exchange within the period from 2007 to 2011. Our analysis extends the existing research by broadening the class of the models, including both the GARCH class models based on daily data and models for realized volatility based on intraday returns (HAR-RV, HAR-RV-J and ARFIMA). We find that the VaR estimates obtained from the models for daily returns and realized volatility give comparable results. Both long memory features and asymmetry are found to improve the VaR forecasts. However, when loss functions are considered, the models based on daily data allow minimizing regulatory loss function, whereas the models based on realized volatility allow minimizing the opportunity cost of capital.