英文标题：
《Dynamic Quantile Function Models》
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作者：
Wilson Ye Chen, Gareth W. Peters, Richard H. Gerlach, Scott A. Sisson
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最新提交年份：
2021
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英文摘要：
Motivated by the need for effectively summarising, modelling, and forecasting the distributional characteristics of intra-daily returns, as well as the recent work on forecasting histogram-valued time-series in the area of symbolic data analysis, we develop a time-series model for forecasting quantile-function-valued (QF-valued) daily summaries for intra-daily returns. We call this model the dynamic quantile function (DQF) model. Instead of a histogram, we propose to use a $g$-and-$h$ quantile function to summarise the distribution of intra-daily returns. We work with a Bayesian formulation of the DQF model in order to make statistical inference while accounting for parameter uncertainty; an efficient MCMC algorithm is developed for sampling-based posterior inference. Using ten international market indices and approximately 2,000 days of out-of-sample data from each market, the performance of the DQF model compares favourably, in terms of forecasting VaR of intra-daily returns, against the interval-valued and histogram-valued time-series models. Additionally, we demonstrate that the QF-valued forecasts can be used to forecast VaR measures at the daily timescale via a simple quantile regression model on daily returns (QR-DQF). In certain markets, the resulting QR-DQF model is able to provide competitive VaR forecasts for daily returns.
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中文摘要：
出于有效总结、建模和预测日内收益分布特征的需要，以及最近在符号数据分析领域预测直方图值时间序列的工作，我们开发了一个用于预测分位数函数值（QF值）日内收益总结的时间序列模型。我们将此模型称为动态分位数函数（DQF）模型。我们建议使用$g$和$h$分位数函数来总结日内收益的分布，而不是直方图。我们使用DQF模型的贝叶斯公式，以便在考虑参数不确定性的同时进行统计推断；针对基于采样的后验推理，提出了一种高效的MCMC算法。使用10个国际市场指数和每个市场大约2000天的样本外数据，DQF模型在预测日内收益的VaR方面，与区间值和柱状图值时间序列模型相比，表现良好。此外，我们还证明了QF值预测可以通过一个简单的日收益分位数回归模型（QR-DQF）在每日时间尺度上预测VaR度量。在某些市场，由此产生的QR-DQF模型能够为每日回报提供有竞争力的VaR预测。
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分类信息：

一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Quantitative Finance 数量金融学
二级分类：Risk Management 风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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