International Review of Financial Analysis ( IF 8.235 ) Pub Date : 2023-02-24 , DOI: 10.1016/j.irfa.2023.102606 Xing Jin, Yi Hong
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.
中文翻译:
方差互换的跳跃扩散波动率模型:实证性能分析
本文研究了一类易于处理的跳跃扩散模型,包括具有各种跳跃强度规格的随机波动率模型,用于股票收益和方差过程。我们采用马尔可夫链蒙特卡罗 (MCMC) 方法来实现模型估计,并研究所有模型在捕获方差互换利率的期限结构和拟合股票收益动态方面的性能。很明显,随机波动率模型配备了即期方差的自激跳跃和中心趋势方差的线性相关跳跃,可以产生一致的模型估计,恰当地解释方差互换中的程式化事实,并提高定价性能. 此外,我们的实证结果表明,作为独立的风险源,即期方差的较大自激跳跃,促进方差互换的期限结构建模,而中央趋势方差可能会随着小规模跳跃,但从长远来看预示着实质性的制度变化。这两种类型的跳跃都很少发生,并且更多地与 2008 年至 2021 年期间的市场动荡有关。