Bayesian prediction of jumps in large panels of time series data
We take a new look at the problem of disentangling the volatility and jumps processes of daily stock returns. We first provide a computational framework for the univariate stochastic volatility model with Poisson-driven jumps that offers a competitive inference alternative to the existing tools. This methodology is then extended to a large set of stocks for which we assume that their unobserved jump intensities co-evolve in time through a dynamic factor model. To evaluate the proposed modelling approach we conduct out-of-sample forecasts and we compare the posterior predictive distributions obtained from the different models. We provide evidence that joint modelling of jumps improves the predictive ability of the stochastic volatility models.
Code (0)
등록된 구현이 없습니다.
Tasks
Time SeriesTime Series AnalysisSimilar Papers 제목 키워드 기반
Bayesian Dynamic Factor Models for High-dimensional Matrix-valued Time Series
High-dimensional matrix-valued time series are of significant interest in economics and finance, with prominent examples including cross region macroeconomic panels and firms' financial data panels. We introduce a class …
Time SeriesSparse Kalman Filtering Approaches to Covariance Estimation from High Frequency Data in the Presence of Jumps
Estimation of the covariance matrix of asset returns from high frequency data is complicated by asynchronous returns, market mi- crostructure noise and jumps. One technique for addressing both asynchronous returns and ma…
Combining Bayesian and Frequentist Inference for Laboratory-Specific Performance Guarantees in Copy Number Variation Detection
Targeted amplicon panels are widely used in oncology diagnostics, but providing per-gene performance guarantees for copy number variant (CNV) detection remains challenging due to amplification artifacts, process-mismatch…
A Modified Levy Jump-Diffusion Model Based on Market Sentiment Memory for Online Jump Prediction
In this paper, we propose a modified Levy jump diffusion model with market sentiment memory for stock prices, where the market sentiment comes from data mining implementation using Tweets on Twitter. We take the market s…
Reinforcement Re-ranking with 2D Grid-based Recommendation Panels
Modern recommender systems usually present items as a streaming, one-dimensional ranking list. Recently there is a trend in e-commerce that the recommended items are organized grid-based panels with two dimensions where …
Recommendation SystemsRe-Ranking