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利用混频数据改进密度预测：贝叶斯方法AI 解读

We construct a modeling framework that allows for the flexible contribution of intraday information to predict the conditional distribution of asset returns. A flexible spline specification is proposed to capture the contribution of intraday returns inside GARCH and stochastic volatility specifications for daily returns. The “focused Bayesian” prediction framework is adopted to allow the intraday contribution to the simpler GARCH specification to be trained according to the…

大型偏斜 t Copula 模型与日内股票收益的非对称依赖AI 解读

Skew-t copula models are attractive for the modeling of financial data because they allow for asymmetric and extreme tail dependence.We show that the copula implicit in the skew-t distribution of Azzalini and Capitanio allows for a higher level of pairwise asymmetric dependence than two popular alternative skew-t copulas.Estimation of this copula in high dimensions is challenging, and we propose a fast and accurate Bayesian variational inference (VI) approach to do so.The…

高频跳跃检验：应选择哪一种检验？AI 解读

We conduct an extensive evaluation of price jump tests based on high-frequency financial data. After providing a concise review of multiple alternative tests, we document the size and power of all tests in a range of empirically relevant scenarios. Particular focus is given to the robustness of test performance to the presence of jumps in volatility and microstructure noise, and to the impact of sampling frequency. The paper concludes by providing guidelines for empirical…

异方差数据的时间序列Copula模型AI 解读

Summary We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We suggest copulas for first‐order Markov series, and then extend them to higher orders and multivariate series. We derive the copula of a volatility proxy, based on which we propose new measures of volatility dependence, including co‐movement and spillover in multivariate series. In general, these depend upon the marginal distributions of the series. Using…

利用高频测度推断价格与波动率的自激跳跃AI 解读

Summary Dynamic jumps in the price and volatility of an asset are modelled using a joint Hawkes process in conjunction with a bivariate jump diffusion. A state‐space representation is used to link observed returns, plus nonparametric measures of integrated volatility and price jumps, to the specified model components, with Bayesian inference conducted using a Markov chain Monte Carlo algorithm. An evaluation of marginal likelihoods for the proposed model relative to a large…

波动率及其风险溢价的概率预测AI 解读

The object of this paper is to produce distributional forecasts of asset price volatility and its associated risk premia using a non-linear state space approach. Option and spot market information on the latent variance process is captured by using dual ‘model-free’ variance measures to define a bivariate observation equation in the state space model. The premium for variance diffusive risk is defined as linear in the latent variance (in the usual fashion) whilst the premium…