A Multiplicative Error Model with Heterogeneous Components for Forecasting Realized Volatility
To forecast realized volatility, this paper introduces a multiplicative error model that incorporates heterogeneous components: weekly and monthly realized volatility measures. While the model captures the long‐memory property, estimation simply proceeds using quasi‐maximum likelihood estimation. Th...
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Veröffentlicht in: | Journal of forecasting 2015-04, Vol.34 (3), p.209-219 |
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creator | Han, Heejoon Park, Myung D. Zhang, Shen |
description | To forecast realized volatility, this paper introduces a multiplicative error model that incorporates heterogeneous components: weekly and monthly realized volatility measures. While the model captures the long‐memory property, estimation simply proceeds using quasi‐maximum likelihood estimation. This paper investigates its forecasting ability using the realized kernels of 34 different assets provided by the Oxford‐Man Institute's Realized Library. The model outperforms benchmark models such as ARFIMA, HAR, Log‐HAR and HEAVY‐RM in within‐sample fitting and out‐of‐sample (1‐, 10‐ and 22‐step) forecasts. It performed best in both pointwise and cumulative comparisons of multi‐step‐ahead forecasts, regardless of loss function (QLIKE or MSE). Copyright © 2015 John Wiley & Sons, Ltd. |
doi_str_mv | 10.1002/for.2333 |
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Forecast</addtitle><description>To forecast realized volatility, this paper introduces a multiplicative error model that incorporates heterogeneous components: weekly and monthly realized volatility measures. While the model captures the long‐memory property, estimation simply proceeds using quasi‐maximum likelihood estimation. This paper investigates its forecasting ability using the realized kernels of 34 different assets provided by the Oxford‐Man Institute's Realized Library. The model outperforms benchmark models such as ARFIMA, HAR, Log‐HAR and HEAVY‐RM in within‐sample fitting and out‐of‐sample (1‐, 10‐ and 22‐step) forecasts. It performed best in both pointwise and cumulative comparisons of multi‐step‐ahead forecasts, regardless of loss function (QLIKE or MSE). Copyright © 2015 John Wiley & Sons, Ltd.</description><subject>Benchmarking</subject><subject>Comparative analysis</subject><subject>Econometrics</subject><subject>Economic models</subject><subject>Estimation</subject><subject>forecasting</subject><subject>Forecasting techniques</subject><subject>long-memory property</subject><subject>Maximum likelihood method</subject><subject>multiplicative error model</subject><subject>realized volatility</subject><subject>Studies</subject><subject>Volatility</subject><issn>0277-6693</issn><issn>1099-131X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNp10MFOGzEQBmCrohKBVuIRLPXCZcFee-31EUUkgJKAEAVOWI53NjV11sF2oOnTd1NQqyL1NJdP__wzCB1QckQJKY_bEI9KxtgHNKBEqYIyer-DBqSUshBCsV20l9IjIUTWtByghxM8XfvsVt5Zk90z4NMYQ8TT0IDHLy5_w2eQIYYFdBDWCQ_DchU66HLC_So8ChGsSdl1C3wNxruf0ODb4Pss7_LmE_rYGp_g89vcR19HpzfDs2JyOT4fnkwKy8uaFco21FTzirFWcktrpVgjlaxawVszryWRvDE1NY1lSqq6lhYqoAxEwy2IsmX76PA1dxXD0xpS1kuXLHhvfrfWVMiylpxx3tMv7-hjWMeub9crwRUvKZV_A20MKUVo9Sq6pYkbTYnePlr31-vto3tavNIX52HzX6dHl9f_epcy_PjjTfyuhWSy0nezsb64n1zNxOhK37Bfg5COog</recordid><startdate>201504</startdate><enddate>201504</enddate><creator>Han, Heejoon</creator><creator>Park, Myung D.</creator><creator>Zhang, Shen</creator><general>Blackwell Publishing Ltd</general><general>Wiley Periodicals Inc</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope></search><sort><creationdate>201504</creationdate><title>A Multiplicative Error Model with Heterogeneous Components for Forecasting Realized Volatility</title><author>Han, Heejoon ; Park, Myung D. ; Zhang, Shen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4283-9cd1a5b533f74c18993d7975f64fab87074da81adc3979887ce5e13e6d4ce62f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Benchmarking</topic><topic>Comparative analysis</topic><topic>Econometrics</topic><topic>Economic models</topic><topic>Estimation</topic><topic>forecasting</topic><topic>Forecasting techniques</topic><topic>long-memory property</topic><topic>Maximum likelihood method</topic><topic>multiplicative error model</topic><topic>realized volatility</topic><topic>Studies</topic><topic>Volatility</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Han, Heejoon</creatorcontrib><creatorcontrib>Park, Myung D.</creatorcontrib><creatorcontrib>Zhang, Shen</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><jtitle>Journal of forecasting</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Han, Heejoon</au><au>Park, Myung D.</au><au>Zhang, Shen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Multiplicative Error Model with Heterogeneous Components for Forecasting Realized Volatility</atitle><jtitle>Journal of forecasting</jtitle><addtitle>J. Forecast</addtitle><date>2015-04</date><risdate>2015</risdate><volume>34</volume><issue>3</issue><spage>209</spage><epage>219</epage><pages>209-219</pages><issn>0277-6693</issn><eissn>1099-131X</eissn><coden>JOFODV</coden><abstract>To forecast realized volatility, this paper introduces a multiplicative error model that incorporates heterogeneous components: weekly and monthly realized volatility measures. While the model captures the long‐memory property, estimation simply proceeds using quasi‐maximum likelihood estimation. This paper investigates its forecasting ability using the realized kernels of 34 different assets provided by the Oxford‐Man Institute's Realized Library. The model outperforms benchmark models such as ARFIMA, HAR, Log‐HAR and HEAVY‐RM in within‐sample fitting and out‐of‐sample (1‐, 10‐ and 22‐step) forecasts. It performed best in both pointwise and cumulative comparisons of multi‐step‐ahead forecasts, regardless of loss function (QLIKE or MSE). 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subjects | Benchmarking Comparative analysis Econometrics Economic models Estimation forecasting Forecasting techniques long-memory property Maximum likelihood method multiplicative error model realized volatility Studies Volatility |
title | A Multiplicative Error Model with Heterogeneous Components for Forecasting Realized Volatility |
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