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Heterogeneous Autoregressive Model of the Realized Volatility: Evidence from Czech Stock Market

机译：已实现波动率的异构自回归模型：来自捷克股票市场的证据

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This paper deals with a conditional volatility GARCH model and model based on realized volatility which is able to account for the main empirical features observed in data in financial markets. Inspired by well-known Heterogeneous Market Hypothesis and by the asymmetric behavior of volatility between long and short time horizons, we used an additive cascade of different volatility components generated by the actions of different types of market participants. This additive volatility cascade leads to a simple AR-type model in the realized volatility with the feature of considering volatilities realized over different time horizons. HAR-RV model successfully achieves the purpose of reproducing the main empirical features of volatility like long memory, fat tails, and self-similarity in a very simple and parsimoniously way. The aim of this paper is to compare estimates got by simple AR(1)-GARCH(1, 1) model and HAR-RV model using data from the Czech stock market represented by PX index. In our paper we work with daily, weekly and monthly returns of mentioned stock index. Preliminary results on the estimation and forecast of the HAR - RV model on PX stock index data show remarkably good in-sample forecasting performance which steadily and substantially outperforms those of standard models represented by AR(1)-GARCH(1, 1) model. There will be also very fruitful to compare results estimated by mentioned models in different time periods. We especially mention an impact of the global financial crisis on Czech stock market volatility. Therefore, in this paper we will investigate pre-crisis, crisis and post-crisis periods.
机译：本文研究了条件波动率GARCH模型和基于实际波动率的模型，该模型能够解释金融市场数据中观察到的主要经验特征。受著名的异构市场假设和长短时间范围内波动的不对称行为的启发，我们使用了由不同类型的市场参与者的行为产生的不同波动成分的累加级联。该累加波动率级联导致已实现波动率具有简单的AR型模型，其特征在于考虑了在不同时间范围内实现的波动率。 HAR-RV模型成功地实现了以非常简单和简约的方式再现波动性的主要经验特征（如长记忆，胖尾巴和自相似性）的目的。本文的目的是使用来自PX指数代表的捷克股市数据，比较简单的AR（1）-GARCH（1，1）模型和HAR-RV模型获得的估计。在本文中，我们使用提到的股票指数的每日，每周和每月收益。根据PX股指数据对HAR-RV模型进行估计和预测的初步结果显示，样本内的预测性能非常好，在性能上稳步并大大超过了以AR（1）-GARCH（1，1）模型为代表的标准模型。比较上述模型在不同时间段内估算的结果也将非常有成果。我们特别提到全球金融危机对捷克股市波动的影响。因此，在本文中，我们将调查危机前，危机和危机后时期。

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