Realized Volatility Forecast of Stock Index Under Structural Breaks

2014 ◽  
Vol 34 (1) ◽  
pp. 57-82 ◽  
Author(s):  
Ke Yang ◽  
Langnan Chen ◽  
Fengping Tian
Keyword(s):  
Structural Breaks ◽  
Realized Volatility ◽  
Stock Index ◽  
Volatility Forecast

Does VPIN provide predictive information for realized volatility forecasting: evidence from Chinese stock index futures market

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Conghua Wen ◽  
Fei Jia ◽  
Jianli Hao
Keyword(s):  
High Frequency ◽  
Realized Volatility ◽  
Futures Market ◽  
Stock Index ◽  
Stock Index Futures ◽  
Trading Behavior ◽  
Volatility Forecasting ◽  
Content Type ◽  
Index Futures ◽  
Intraday Data

PurposeUsing intraday data, the authors explore the forecast ability of one high frequency order flow imbalance measure (OI) based on the volume-synchronized probability of informed trading metric (VPIN) for predicting the realized volatility of the index futures on the China Securities Index 300 (CSI 300).Design/methodology/approachThe authors employ the heterogeneous autoregressive model for realized volatility (HAR-RV) and compare the forecast ability of models with and without the predictive variable, OI.FindingsThe empirical results demonstrate that the augmented HAR model incorporating OI (HARX-RV) can generate more precise forecasts, which implies that the order imbalance measure contains substantial information for describing the volatility dynamics.Originality/valueThe study sheds light on the relation between high frequency trading behavior and volatility forecasting in China's index futures market and reveals the underlying market mechanisms of liquidity-induced volatility.

scholarly journals Predictability of OTC Option Volatility for Future Stock Volatility

2020 ◽  
Vol 12 (12) ◽  
pp. 5200
Author(s):  
Jungmu Kim ◽  
Yuen Jung Park
Keyword(s):  
Information Content ◽  
Unbiased Estimator ◽  
Implied Volatility ◽  
Realized Volatility ◽  
Stock Index ◽  
Options Market ◽  
Over The Counter ◽  
Index Options ◽  
Stock Volatility ◽  
Future Realized Volatility

This study explores the information content of the implied volatility inferred from stock index options in the over-the-counter (OTC) market, which has rarely been studied in the literature. Using OTC calls, puts, and straddles on the KOSPI 200 index, we find that implied volatility generally outperforms historical volatility in predicting future realized volatility, although it is not an unbiased estimator. The results are more apparent for options with shorter maturity. However, while implied volatility has strong predictability during normal periods, historical volatility is superior to implied volatility during a period of crisis due to the liquidity contraction of the OTC options market. This finding suggests that the OTC options market can play a role in conveying important information to predict future volatility.

Structural breaks in unbiased volatility estimator: Modeling and forecasting

2017 ◽  
Vol 11 (1) ◽  
pp. 27-50 ◽  
Author(s):  
Dilip Kumar
Keyword(s):  
Long Memory ◽  
Structural Breaks ◽  
Realized Volatility ◽  
Extreme Value ◽  
Trading Strategy ◽  
Risk Averse ◽  
Memory Property ◽  
Economic Significance ◽  
Forecasting Performance ◽  
Modeling And Forecasting

The study provides a framework to model the unbiased extreme value volatility estimator (The AddRS estimator) in presence of structural breaks. We observe that the structural breaks in the volatility based on the AddRS estimator can partly explain its long memory property. We evaluate the forecasting performance of the proposed framework and compare the results with the corresponding results of the models from the GARCH family. The forecasts evaluation exercises consider the cases when future breaks are known as well as unknown. Our findings indicate that the proposed framework outperform the sophisticated GARCH class of models in forecasting realized volatility. Moreover, we devise a trading strategy based on the forecasts of the variance to highlight the economic significance of the proposed framework. We find that a risk averse investor can make substantial gain using the volatility forecasts based on the proposed frameworks in comparison to the GARCH family of models.

ON DISTINGUISHING BETWEEN RANDOM WALK AND CHANGE IN THE MEAN ALTERNATIVES

2009 ◽  
Vol 25 (2) ◽  
pp. 411-441 ◽  
Author(s):  
Alexander Aue ◽  
Lajos Horváth ◽  
Marie Hušková ◽  
Shiqing Ling
Keyword(s):  
Random Walk ◽  
Structural Breaks ◽  
Stock Index ◽  
Partial Sums ◽  
Test Statistics ◽  
Test Procedures ◽  
Change In The Mean ◽  
German Stock ◽  
The Mean ◽  
Almost All

We study test procedures that detect structural breaks in underlying data sequences. In particular, we wish to discriminate between different reasons for these changes, such as (1) shifting means, (2) random walk behavior, and (3) constant means but innovations switching from stationary to difference stationary behavior. Almost all procedures presently available in the literature are simultaneously sensitive to all three types of alternatives.The test statistics under investigation are based on functionals of the partial sums of observations. These cumulative sum–type (CUSUM-type) statistics have limit distributions if the mean remains constant and the errors satisfy the central limit theorem but tend to infinity in the case when any of the alternatives (1), (2), or (3) holds. On removing the effect of the shifting mean, however, divergence of the test statistics will only occur under the random walk behavior, which in turn enables statisticians not only to detect structural breaks but also to specify their causes.The results are underlined by a simulation study and an application to returns of the German stock index DAX.

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