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聚焦预测目标的贝叶斯预测AI 解读

Summary We propose a new method for conducting Bayesian prediction that delivers accurate predictions without correctly specifying the unknown true data generating process. A prior is defined over a class of plausible predictive models. After observing data, we update the prior to a posterior over these models, via a criterion that captures a user‐specified measure of predictive accuracy. Under regularity, this update yields posterior concentration onto the element of 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…

分数阶单整过程错设模型的估计问题AI 解读

This short paper provides a comprehensive set of new theoretical results on the impact of mis-specifying the short run dynamics in fractionally integrated processes. We show that four alternative parametric estimators – frequency domain maximum likelihood, Whittle, time domain maximum likelihood and conditional sum of squares – converge to the same pseudo-true value under common mis-specification, and that they possess a common asymptotic distribution. The results are…

用预滤波筛自助法修正半参数长记忆估计偏差AI 解读

This paper investigates bootstrap-based bias correction of semiparametric estimators of the long memory parameter, d , in fractionally integrated processes. The re-sampling method involves the application of the sieve bootstrap to data prefiltered by a preliminary semiparametric estimate of the long memory parameter. Theoretical justification for using the bootstrap technique to bias adjust log periodogram and semiparametric local Whittle estimators of the memory parameter…

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…

分数单整过程筛法 Bootstrap 的高阶改进AI 解读

This paper investigates the accuracy of bootstrap-based inference in the case of long memory fractionally integrated processes. The re-sampling method is based on the semi-parametric sieve, whereby the dynamics in the process used to produce the bootstrap draws are captured by an autoregressive approximation. Application of the sieve method to data pre-filtered by a semi-parametric estimate of the long memory parameter is also explored. Higher-order improvements yielded by…

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…

计数数据的有效概率预测AI 解读

Summary Efficient probabilistic forecasts of integer-valued random variables are derived. The optimality is achieved by estimating the forecast distribution non-parametrically over a given broad model class and proving asymptotic (non-parametric) efficiency in that setting. The method is developed within the context of the integer auto-regressive class of models, which is a suitable class for any count data that can be interpreted as a queue, stock, birth-and-death process…

美国赤字的可持续性：基于多个内生断点的新方法AI 解读

Recent empirical work has questioned the consistency of US fiscal policy with an intertemporal budget constraint. Empirical results have tended to indicate that the deficit process has undergone at least one structural shift during recent decades, with the deficit becoming either unsustainable or sustainable in only a weak sense in the post-shift period. In this paper, we re-examine sustainability using a new approach, based on a cointegration model with multiple endogenous…