Affiliations: [a]
School of Maritime Economics and Management, Dalian Maritime University, Dalian, China
| [b]
School of Data Science and Artificial Intelligence, Dongbei University of Finance and Economics, Dalian, China
| [c]
School of Science, Dalian Maritime University, Dalian, China
Correspondence:
[*]
Corresponding author. Wenjuan Jia, School of Data Science and Artificial Intelligence, Dongbei University of Finance and Economics, Dalian 116025, China. Email: wenjuan_jia@yeah.net.
Note: [1] This work is supported in part by the Natural Science Foundation of China under Grant 62006033 and Grant 62173053, the Dalian High-level Talents Innovation Support Plan 2021RQ061, the Educational Commission of Liaoning Province of China under Grant LJKZ1036, and the Fundamental Research Funds for the Central Universities under Grant 3132023725.
Abstract: The implied volatility plays a pivotal role in the options market, and a collection of implied volatilities across strike and maturity is known as the implied volatility surface (IVS). To capture the dynamics of IVS, this study examines the latent states of IVS and their relationship based on the regime-switching framework of the hidden Markov model (HMM). The cross-sectional models are first built for daily implied volatilities, and the obtained regression factors are regarded as the proxies of the IVS. Then, having these latent factors, the HMM is employed to model the dynamics of IVS. Take the advantages of HMM, the hidden state for each daily data is identified to achieve the corresponding time distribution, the characteristics, and the transition between the hidden states. The empirical study is conducted on the Shanghai 50ETF options, and the analysis results indicate that the HMM can capture the latent factors of IVS. The achieved states reflect different financial characteristics, and some of their typical features and transfer are associated with certain events. In addition, the HMM exploited to predict the regression factors of the cross-sectional models enables the further forecasting of implied volatilities. The autoregressive integrated moving average model, the vector auto-regression model, and the support vector regression model are regarded as benchmarks for comparison. The results show that the HMM performs better in the implied volatility prediction compared with other models.
