Jump-diffusion volatility models for variance swaps: An empirical performance analysis |
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| Affiliation: | 1. School of Public Administration, Nanjing university of Finance and Economics, China;2. Department of History of Science, Technology and Medicine, Peking University, China;1. Audencia Business School, Research Center: Markets Technology and Society, 8 Route de la Joneliere, 44312 Cedex 3, Nantes, France;2. Heriot Watt University, Accounting, Economics and Finance SEEC, CFI, Edinburgh, Scotland EH14 4AS, UK;1. School of Finance and Accounting, Fuzhou University of International Studies and Trade, Fuzhou, China;2. International Business School, Beijing Foreign Studies University, Beijing, China;1. International University, Quarter 6, Linh Trung Ward, Thu Duc City, Ho Chi Minh City, Viet Nam;2. Vietnam National University, Ho Chi Minh City, Viet Nam;3. Excelia Business School, 102 Rue de Coureilles - Les Minimes, 17024 La Rochelle, France;4. EDHEC Business School, 24 avenue Gustave Delory, CS 50411, 59057 Roubaix Cedex 1, Lille, France;1. Finance, Accounting, and Control, Indian Institute of Management Amritsar, Punjab 143105, India;2. Finance and Accounting, Indian Institute of Management Lucknow, Prabandh Nagar, IIM Road, Lucknow, Uttar Pradesh 226013, India |
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| Abstract: | This paper studies a class of tractable jump-diffusion models, including stochastic volatility models with various specifications of jump intensity for stock returns and variance processes. We employ the Markov chain Monte Carlo (MCMC) method to implement model estimation, and investigate the performance of all models in capturing the term structure of variance swap rates and fitting the dynamics of stock returns. It is evident that the stochastic volatility models, equipped with self-exciting jumps in the spot variance and linearly-dependent jumps in the central-tendency variance, can produce consistent model estimates, aptly explain the stylized facts in variance swaps, and boost pricing performance. Moreover, our empirical results show that large self-exciting jumps in the spot variance, as an independent risk source, facilitate term structure modeling for variance swaps, whilst the central-tendency variance may jump with small sizes, but signaling substantial regime changes in the long run. Both types of jumps occur infrequently, and are more related to market turmoils over the period from 2008 to 2021. |
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| Keywords: | Variance swaps Jump-diffusion volatility models Jump intensity Self-exciting jump process Markov Chain Monte Carlo (MCMC) |
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