Realized volatility and jumps in the Athens Stock Exchange

Vortelinos, Dimitrios I.; Thomakos, Dimitrios D.

doi:10.1080/09603107.2011.605751

Cited by 8 publications

(6 citation statements)

References 28 publications

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“…In nutshell our results indicate that decomposition of realized variance in continuous and jump components enriches the volatility forecasting, and the long memory of volatility is largely coming from the continuous component and consistent with the literature (Andersen et al, 2007;Vortelinos and Thomakos, 2012).…”

Section: Volatility Forecasting: Realized Volatilities Jumps and Leverage Effectssupporting

confidence: 89%

“…The empirical literature provides strong evidences of presence of jumps in nancial assets/markets (Alexeev et al, 2017;Bollerslev et al, 2016;Lahaye et al, 2011;Todorov and Bollerslev, 2010). Andersen et al (2007), Corsi et al (2010) and Vortelinos and Thomakos (2012) suggest that accounting for jump in realized volatility modeling and forecasting using high-frequency data is important. Eraker et al (2003) and Bollerslev et al (2016) provide further evidence of signicant risk premia for the jump component (Bollerslev et al, 2016;Eraker et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

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Realized Volatility, Jump and Beta: evidence from Canadian Stock Market

Gajurel

Chowdhury

2021

Applied Economics

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Inclusion of jump component in the price process has been a long debate in nance literature. In this paper, we identify and characterize jump risks in the Canadian stock market using high-frequency data from the Toronto Stock Exchange. Our results provide a strong evidence of jump clustering -about 90% of jumps occur within rst 30 minutes of market opening for trade, and about 55% of jumps are due to the overnight returns. While average intraday jump is negative, jumps induced by overnight returns bring a cancellation eect yielding average size of the jumps to zero. We show that the economic signicance of jump component in volatility forecasting is very nominal. Our results further demonstrate that market jumps and overnight returns bring signicant changes in systematic risk (beta) of stocks. While the average eect of market jumps on beta is not signicantly dierent than zero, the eect of overnight returns on beta is signicant. Overall, our results suggest that jump risk is non-systematic in nature.

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Section: Volatility Forecasting: Realized Volatilities Jumps and Leverage Effectssupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Realized Volatility, Jump and Beta: evidence from Canadian Stock Market

Gajurel

Chowdhury

2021

Applied Economics

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“…Subsequently, a series of studies address the modelling and forecasting of realized volatility. (See, for example, Barndorff-Nielsen and Shephard, 2006;Andersen et al, 2007;Forsberg and Ghysels, 2007;Corsi, 2009;Curci and Corsi, 2012;Louzis et al, 2012;Vortelinos and Thomakos, 2012;Xu, 2012;Souček and Todorova, 2013;Christoffersen et al, 2014;Iliescu and Dutta, 2016). Furthermore, the HAR-RV model has been 2 Also see, for example, Bessembinder and Seguin (1993); Chen et al (1995); Liew and Brooks (1998); Girma and Mougoue (2002); Motladiile and Smit (2003); Serletis and Shahmoradi (2006).…”

Section: Realized Volatility Measuresmentioning

confidence: 99%

The information content of open interest for the realized range-based volatility: evidence from Chinese futures market

Tseng¹,

Lai²,

Wen³

2018

Pressacademia

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Purpose -The paper studies the impact of the infroamtion content of open interst on the realized range-based vaolatility of Chinese futures markets. Methodology-We employ a hybrid range-based estimator to measure the integrated variance in the heterogeneous autoregressive (HAR) model, which also incorporates the variable of open interest into the HAR model on index futures prices of China Securities Index (CSI) 300. Findings-Our findings demonstrate that the variable of open interest has a significant explanatory power with regard to the future realized volatility of the CSI 300 index futures. Conclusion-The modified model enhances volatility forecasting performance, thereby indicating it has more accurate predictive power. Our results provide supports for the implication of the sequential information arrival hypothesis.

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“…A better approach is to use the realized variance (RV) Quantile forecasts measures that estimate the latent conditional variance more accurately by exploiting highfrequency intraday returns' data. Several studies document that incorporating realized measures in volatility forecasting models leads to substantial statistical and economic gains (Andersen et al, 2001a(Andersen et al, , 2001bAndersen and Bollerslev, 1998;Barndorff-Nielsen, 2002;Barndorff-Nielsen and Shephard, 2002;Christoffersen et al, 2014;Fleming et al, 2003;Koopman et al, 2005;Pong et al, 2004;Vortelinos, 2013;Vortelinos and Thomakos, 2012;Sharma and Vipul, 2016b). Recently, Hansen et al (2012) introduced the Realized GARCH model that jointly models the conditional moments and the RV process.…”

Section: Introductionmentioning

confidence: 99%

Quantile forecasts using the Realized GARCH-EVT approach

Paul

Sharma

2018

SEF

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Purpose-This study aims to implement a novel approach of using the Realized generalized autoregressive conditional heteroskedasticity (GARCH) model within the conditional extreme value theory (EVT) framework to generate quantile forecasts. The Realized GARCH-EVT models are estimated with different realized volatility measures. The forecasting ability of the Realized GARCH-EVT models is compared with that of the standard GARCH-EVT models. Design/methodology/approach-One-step-ahead forecasts of Value-at-Risk (VaR) and expected shortfall (ES) for five European stock indices, using different two-stage GARCH-EVT models, are generated. The forecasting ability of the standard GARCH-EVT model and the asymmetric exponential GARCH (EGARCH)-EVT model is compared with that of the Realized GARCH-EVT model. Additionally, five realized volatility measures are used to test whether the choice of realized volatility measure affects the forecasting performance of the Realized GARCH-EVT model. Findings-In terms of the out-of-sample comparisons, the Realized GARCH-EVT models generally outperform the standard GARCH-EVT and EGARCH-EVT models. However, the choice of the realized estimator does not affect the forecasting ability of the Realized GARCH-EVT model. Originality/value-It is one of the earliest implementations of the two-stage Realized GARCH-EVT model for generating quantile forecasts. To the best of the authors' knowledge, this is the first study that compares the performance of different realized estimators within Realized GARCH-EVT framework. In the context of high-frequency data-based forecasting studies, a sample period of around 11 years is reasonably large. More importantly, the data set has a cross-sectional dimension with multiple European stock indices, whereas most of the earlier studies are based on the US market.

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Realized volatility and jumps in the Athens Stock Exchange

Cited by 8 publications

References 28 publications

Realized Volatility, Jump and Beta: evidence from Canadian Stock Market

Realized Volatility, Jump and Beta: evidence from Canadian Stock Market

The information content of open interest for the realized range-based volatility: evidence from Chinese futures market

Quantile forecasts using the Realized GARCH-EVT approach

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