佳作分享|VPIN能否提供预测已实现波动率的信息：来自中国股指期货市场的证据
Does VPIN provide predictive information for realized volatility forecasting: evidence from Chinese stock index futures market
VPIN能否提供预测已实现波动率的信息：来自中国股指期货市场的证据
Authors
Conghua Wen
Financial Mathematics, Xi'an Jiaotong-Liverpool University, Suzhou, China,and,
Fei Jia
Xi'an Jiaotong-Liverpool University, Suzhou, China,and,
Jianli Hao
Xi'an Jiaotong-Liverpool University, Suzhou, China
Citation
Wen, C., Jia, F. and Hao, J. (2020), "Does VPIN provide predictive information for realized volatility forecasting: evidence from Chinese stock index futures market", China Finance Review International, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/CFRI-05-2020-0049
摘要
利用日内高频交易数据，我们探索了如何利用一个基于VPIN构造的订单流不对称性变量（OI）预测沪深300股指期货已实现波动率及其样本外预测能力。我们利用HAR-RV模型：通过对比该模型加入OI之前和加入OI之后模型相应的样本外预测能力，我们发现加入OI之后的HARX-RV模型在预测中国股指期货已实现波动率方面的性能有显著提高。这一结果说明了我们构造的订单流不对称性变量（OI）对于解释和预测沪深300股指期货已实现波动率有显著成效。此研究的贡献在于我们尝试探索了中国股指期货市场中的高频交易行为和股指期货已实现波动率之间的关系。此外，我们还使用了门限回归模型用以尝试揭示中国股指期货市场中由市场流动性衰竭而引发的波动率变化（大）的相应市场机制。
Abstract
Purpose
Using 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/approach
The 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.
Findings
The 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/value
The 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.
Keywords
Realized volatility, Volatility forecasting, HAR model, Trading behavior, Equity futures
文章结构
Introduction
Model and methodology
2.1 Information model
2.2 From PIN to VPIN
2.3 Proxy for latent volatility process
2.4 HAR-RV and HARX-RV models
2.5 Forecasting performance evaluation metrics
Data and empirical analysis
3.1 Full sample analysis
3.2 Out-of-sample forecasting
3.3 Robustness checks
Mechanisms of liquidity-induced volatility
Economic benefits of HARX-RV value: quantifying the utility benefits
Conclusions
研究成果
Trading process as a dynamic game depicted by the information model.
Descriptive statistics of RV and OI.
Scatter plot of lagged IO and current period RV.
Full-sample volatility estimation and model fitting comparison for CSI 300 futures.
Out-of-sample model forecasting performance for CSI 300 futures.
Robustness: Out-ofsample model forecasting performance for SSE 50 futures.
Robustness: Out-ofsample model forecasting performance with window size of 700 for CSI 300 futures.
Robustness: Out-ofsample model forecasting performance with window size of 1000 for CSI 300 futures.
Subsample model forecasting performance of CSI 300 futures from April 16, 2010 to September 24, 2015.
Subsample model forecasting performance of CSI 300 futures from September 25, 2015 to September 30, 2019.
Threshold regression analysis of the HARX model using CSI 300 futures from April 16, 2010 to January 16, 2020.
Threshold regression analysis of the modified HARX model using CSI 300 futures from April 16, 2010 to January 16, 2020.
Realized utility comparison for different volatility forecast models.
主要结论
This paper explores the predictive ability of the order flow imbalance measure, OI, in forecasting future realized volatility. Employing the bulk volume classification algorithm, we construct our daily order flow imbalance measure, OI, and use it to build volatility models and perform forecasting. The empirical work confirms our hypothesis that, by incorporating OI in the HAR-RV model, not only does the augmented model, HARX-RV, lead to a better in-sample fit, it also improves the out-of-sample volatility forecasting performance. Further robustness checks and portfolio exercise reinforce the main findings with different target asset, various window sizes and subsample analysis. Thus, our paper extends the volatility forecasting literature by linking high-frequency trading behavior with realized volatility prediction. Furthermore, we also explore the underlying market mechanism of liquidity-induced volatility and discover that lower market liquidity is associated with higher future market volatility only when the market is illiquid, and information is asymmetric.
作者简介
Dr Conghua Wen is the senior associate professor at Xi’an Jiaotong-Liverpool University. Dr Wen has studied and worked in several prestigious universities. He has diverse research interests to explore mathematical/statistical modelling techniques with applications on Engineering, Business and Finance. He delivered multidisciplinary research and won external research grants from research councils, local government and local industries as the principal or core co-investigator.
Mr Fei Jia was a postgraduate student at Xi’an Jiaotong-Liverpool University and currently he is working in a private hedge fund.
Dr Jianli Hao is the senior associate professor at Xi’an Jiaotong-Liverpool University. She is a member of the Chinese Research Institute of Construction Management (CRIOCM) and the Chartered Institute of Building (CIOB). She also serves on the editorial board of the International Journal of Construction Management (IJCM) and is a review expert for several academic journals.
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