Quantile forecasting based on a bivariate hysteretic autoregressive model with GARCH errors and time ‐varying correlations

Chen Cathy W.S.; Than‐Thi Hong; So Mike K.P.; Sriboonchitta Songsak

摘要

Abstract > To understand and predict chronological dependence in the second‐order moments of asset returns, this paper considers a multivariate hysteretic autoregressive (HAR) model with generalized autoregressive conditional heteroskedasticity (GARCH) specification and time‐varying correlations, by providing a new method to describe a nonlinear dynamic structure of the target time series. The hysteresis variable governs the nonlinear dynamics of the proposed model in which the regime switch can be delayed if the hysteresis variable lies in a hysteresis zone. The proposed setup combines three useful model components for modeling economic and financial data: (1) the multivariate HAR model, (2) the multivariate hysteretic volatility models, and (3) a dynamic conditional correlation structure. This research further incorporates an adapted multivariate Student <fi>t</fi> innovation based on a scale mixture normal presentation in the HAR model to tolerate for dependence and different shaped innovation components. This study carries out bivariate volatilities, Value at Risk, and marginal expected shortfall based on a Bayesian sampling scheme through adaptive Markov chain Monte Carlo (MCMC) methods, thus allowing to statistically estimate all unknown model parameters and forecasts simultaneously. Lastly, the proposed methods herein employ both simulated and real examples that help to jointly measure for industry downside tail risk. </abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> <div class="translation abstracttxt"> <span class="zhankaihshouqi fivelineshidden" id="abstract"> <span>机译：</span><Abstract Type =“Main”XML：Lang =“en”XML：ID =“ASMB2479-ABS-ABS-0001”> <标题类型=“main”>抽象</ title> > 为了理解和预测资产回报的二阶时刻的年代学依赖，本文通过提供一种描述一个新方法来考虑具有广义自回归条件异质痉挛（GARCH）规范和时变相关性的多变量滞回自回归（HAR）模型。目标时间序列的非线性动态结构。滞后变量管理所提出的模型的非线性动态，其中如果滞后变量位于滞后区中，则可以延迟方案开关。该建议的设置结合了三种有用的模型组件来建模经济和财务数据：（1）多变量Har模型，（2）多变量滞后波动模型，以及（3）动态条件相关结构。该研究进一步包含适应的多变量学生 <fi> t </ fi> 基于规模混合的创新，在HAR模型中普通呈现，以容忍依赖性和不同形状的创新组分。本研究通过自适应Markov链蒙特卡罗（MCMC）方法执行基于贝叶斯采样方案的贝叶斯采样方案的抗竞争挥发性，风险价值和边际预期短缺，从而允许在统计上估计所有未知的模型参数并同时预测。最后，本文所提出的方法采用模拟和实际例子，有助于共同衡量行业下行尾风险。 </ p> </摘要> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> </div> <div class="record"> <h2 class="all_title" id="enpatent33" >著录项</h2> <ul> <li> <span class="lefttit">来源</span> <div style="width: 86%;vertical-align: text-top;display: inline-block;"> <a href='/journal-foreign-19344/'>《Applied stochastic models in business and industry》</a> <b style="margin: 0 2px;">|</b><span>2019年第6期</span><b style="margin: 0 2px;">|</b><span>共21页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Chen Cathy W.S.&option=202" target="_blank" rel="nofollow">Chen Cathy W.S.;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Than‐Thi Hong&option=202" target="_blank" rel="nofollow">Than‐Thi Hong;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=So Mike K.P.&option=202" target="_blank" rel="nofollow">So Mike K.P.;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Sriboonchitta Songsak&option=202" target="_blank" rel="nofollow">Sriboonchitta Songsak;</a> </p> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zkzz" style="display: none;">展开▼</span> </div> </li> <li> <div style="display: flex;"> <span class="lefttit">作者单位</span> <div style="position: relative;margin-left: 3px;max-width: 639px;"> <div class="threelineshidden zhankaihshouqi" id="fOrgthree"> <p>Department of StatisticsFeng Chia UniversityTaichung Taiwan;</p> <p>Department of StatisticsFeng Chia UniversityTaichung Taiwan;</p> <p>Department of Information SystemsBusiness Statistics and Operations Management Hong Kong University of Science and TechnologyHong Kong;</p> <p>Faculty of EconomicsChiang Mai UniversityChiang Mai Thailand;</p> </div> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zhdw" style="display: none;">展开▼</span> </div> </div> </li> <li > <span class="lefttit">收录信息</span> <span style="width: 86%;vertical-align: text-top;display: inline-block;"></span> </li> <li> <span class="lefttit">原文格式</span> <span>PDF</span> </li> <li> <span class="lefttit">正文语种</span> <span>eng</span> </li> <li> <span class="lefttit">中图分类</span> <span><a href="https://www.zhangqiaokeyan.com/clc/1147.html" title="应用数学">应用数学;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=hysteresis&option=203" rel="nofollow">hysteresis;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=marginal expected shortfall&option=203" rel="nofollow">marginal expected shortfall;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Markov chain Monte Carlo method&option=203" rel="nofollow">Markov chain Monte Carlo method;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=multivariate student t distribution&option=203" rel="nofollow">multivariate student t distribution;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=out‐of‐sample forecasting&option=203" rel="nofollow">out‐of‐sample forecasting;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=scale mixture of normal distributions&option=203" rel="nofollow">scale mixture of normal distributions;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=value at risk&option=203" rel="nofollow">value at risk;</a> </p> <div class="translation"> 机译：滞后;边缘预期短缺;马尔可夫链蒙特卡罗方法;多变量学生T分布;采样超出预测;规模的正常分布的混合;价值; </div> </li> </ul> </div> </div> <div class="literature cardcommon" id="literaturereference" style="display:none"> <div class="similarity "> <h3 class="all_title" id="enpatent111">引文网络</h3> <div class="referencetab clearfix"> <ul id="referencedaohang"> <li dataid="referenceul">参考文献</li> <li dataid="citationul">引证文献</li> <li dataid="commonreferenceul">共引文献</li> <li dataid="commoncitationul">同被引文献</li> <li dataid="tworeferenceul">二级参考文献</li> <li dataid="twocitationul">二级引证文献</li> </ul> </div> <div class="reference_details" id="referenceList"> <ul id="referenceul"></ul> <ul id="citationul"></ul> <ul id="commonreferenceul"></ul> <ul id="commoncitationul"></ul> <ul id="tworeferenceul"></ul> <ul id="twocitationul"></ul> </div> </div> </div> <div class="literature cardcommon"> <div class="similarity "> <h3 class="all_title" id="enpatent66">相似文献</h3> <div class="similaritytab clearfix"> <ul> <li class="active" >外文文献</li> <li >中文文献</li> <li >专利</li> </ul> </div> <div class="similarity_details"> <ul > <li> <div> <b>1. </b><a class="enjiyixqcontent" href="/journal-foreign-detail/0704022680091.html">Quantile forecasting based on a bivariate hysteretic autoregressive model with GARCH errors and time ‐varying correlations</a> <b>[J]</b> . <span> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Chen Cathy W.S.&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor">Chen Cathy W.S.,</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Than‐Thi Hong&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor">Than‐Thi Hong,</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=So Mike K.P.&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor">So Mike K.P.,</a> <a href="/journal-foreign-19344/" target="_blank" rel="nofollow" class="tuijian_authcolor">Applied stochastic models in business and industry .</a> <span>2019</span><span>,第6期</span> </span> </div> <p class="zwjiyix translation" style="max-width: initial;height: auto;word-break: break-all;white-space: initial;text-overflow: initial;overflow: initial;"> <span>机译：基于双重滞回自回归模型的分位式预测，加速误差和时间 - 相应的相关性</span> </p> </li> <li> <div> <b>2. </b><a class="enjiyixqcontent" href="/academic-journal-foreign_applied-energy_thesis/020415418002.html">Short-term electricity demand forecasting using autoregressive based time varying model incorporating representative data adjustment</a> <b>[J]</b> . <span> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Vu D. 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