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Comparison of forecasting performances: Does normalization and variance stabilization method beat GARCH(1,1)‐type models? Empirical evidence from the stock markets

机译：预测性能的比较：是否正常化和方差稳定方法击败GARCH（1,1）型模型？来自股票市场的经验证据

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Abstract xml:id="for2478-para-0001"> In this paper, we present a comparison between the forecasting performances of the normalization and variance stabilization method (NoVaS) and the GARCH(1,1), EGARCH(1,1) and GJR‐GARCH(1,1) models. Hence the aim of this study is to compare the out‐of‐sample forecasting performances of the models used throughout the study and to show that the NoVaS method is better than GARCH(1,1)‐type models in the context of out‐of sample forecasting performance. We study the out‐of‐sample forecasting performances of GARCH(1,1)‐type models and NoVaS method based on generalized error distribution, unlike normal and Student's <fi>t</fi> ‐distribution. Also, what makes the study different is the use of the return series, calculated logarithmically and arithmetically in terms of forecasting performance. For comparing the out‐of‐sample forecasting performances, we focused on different datasets, such as S&P 500, logarithmic and arithmetic B?ST 100 return series. The key result of our analysis is that the NoVaS method performs better out‐of‐sample forecasting performance than GARCH(1,1)‐type models. The result can offer useful guidance in model building for out‐of‐sample forecasting purposes, aimed at improving forecasting accuracy. </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 XMLNS =“http://www.wiley.com/namespaces/wiley”type =“main”xml：id =“for2478-abs-0001”xml：lang =“en”> <标题类型=“main”>抽象</ title> xml：id =“for2478-para-0001”> 在本文中，我们展示了归一化和方差稳定方法（NOVAS）和GARCH（1,1），EGARCH（1,1）和GJR-GARCH（1,1）模型的预测性能之间的比较。因此，本研究的目的是比较整个研究中使用的模型的样本预测性能，并表明NoVAS方法在外面的背景下比GARCH（1,1）型模型更好样本预测性能。我们基于广义误差分布研究GARCH（1,1）型模型和NOVAS方法的样本预测性能，与正常和学生不同 <fi> t </ fi> -分配。此外，该研究不同的是利用返回系列，在预测性能方面计算对数和算术计算。为了比较样本外预测性能，我们专注于不同的数据集，例如S＆amp; P 500，对数和算术B？ST 100返回系列。我们分析的关键结果是，NoVAS方法比GARCH（1,1）型模型进行更好的样本预测性能。结果可以在模型建设中提供有用的指导，用于预测预测目的，旨在提高预测准确性。 </ 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-34460/'>《Journal of Forecasting》</a> <b style="margin: 0 2px;">|</b><span>2018年第2期</span><b style="margin: 0 2px;">|</b><span>共18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=Gulay Emrah&option=202" target="_blank" rel="nofollow">Gulay Emrah;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Emec Hamdi&option=202" target="_blank" rel="nofollow">Emec Hamdi;</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 EconometricsDokuz Eylul UniversityBuca Izmir Turkey;</p> <p>Department of EconometricsDokuz Eylul UniversityBuca Izmir Turkey;</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/3359.html" title="未来学">未来学;</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=ARCH/GARCH models&option=203" rel="nofollow">ARCH/GARCH models;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=financial time series&option=203" rel="nofollow">financial time series;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=forecasting&option=203" rel="nofollow">forecasting;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=forecasting performance measures&option=203" rel="nofollow">forecasting performance measures;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=NoVaS&option=203" rel="nofollow">NoVaS;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=volatility&option=203" rel="nofollow">volatility;</a> </p> <div class="translation"> 机译：拱/加高模型;金融时间系列;预测;预测绩效措施;NOVAS;波动性; </div> </li> </ul> </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/0704025548354.html">Comparison of forecasting performances: Does normalization and variance stabilization method beat GARCH(1,1)‐type models? 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