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The economic value of using CAW-type models to forecast covariance matrix
Purpose The purpose of this paper is twofold: to improve the traditional conditional autoregressive Wishart (CAW) and heterogeneous autoregressive (HAR)-CAW model to account for heterogeneous leverage effect and to adjust the high-frequency volatility. The other is to confirm whether CAW-type models...
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Published in: | China finance review international 2019-08, Vol.9 (3), p.338-359 |
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container_title | China finance review international |
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creator | Zhao, Shuran Li, Jinchen Jiang, Yaping Ren, Peimin |
description | Purpose
The purpose of this paper is twofold: to improve the traditional conditional autoregressive Wishart (CAW) and heterogeneous autoregressive (HAR)-CAW model to account for heterogeneous leverage effect and to adjust the high-frequency volatility. The other is to confirm whether CAW-type models that have statistical advantages have economic advantages.
Design/methodology/approach
Based on the high-frequency data, this study proposed a new model to describe the volatility process according to the heterogeneous market hypothesis. Thus, the authors acquire needed and credible high-frequency data.
Findings
By designing two mean-variance frameworks and considering several economic performance measures, the authors find that compared with five other models based on daily data, CAW-type models, especially LHAR-CAW and HAR-CAW, indeed generate the substantial economic values, and matrix adjustment method significantly improves the three CAW-type performances.
Research limitations/implications
The findings in this study suggest that from the aspect of economics, LHAR-CAW model can more accurately built the dynamic process of return rates and covariance matrix, respectively, and the matrix adjustment can reduce bias of realized volatility as covariance matrix estimator of return rates, and greatly improves the performance of unadjusted CAW-type models.
Practical implications
Compared with traditional low-frequency models, investors should allocate assets according to the LHAR-CAW model so as to get more economic values.
Originality/value
This study proposes LHAR-CAW model with the matrix adjustment, to account for heterogeneous leverage effect and empirically show their economic advantage. The new model and the new bias adjustment approach are pioneering and promote the evolution of financial econometrics based on high-frequency data. |
doi_str_mv | 10.1108/CFRI-09-2018-0130 |
format | article |
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The purpose of this paper is twofold: to improve the traditional conditional autoregressive Wishart (CAW) and heterogeneous autoregressive (HAR)-CAW model to account for heterogeneous leverage effect and to adjust the high-frequency volatility. The other is to confirm whether CAW-type models that have statistical advantages have economic advantages.
Design/methodology/approach
Based on the high-frequency data, this study proposed a new model to describe the volatility process according to the heterogeneous market hypothesis. Thus, the authors acquire needed and credible high-frequency data.
Findings
By designing two mean-variance frameworks and considering several economic performance measures, the authors find that compared with five other models based on daily data, CAW-type models, especially LHAR-CAW and HAR-CAW, indeed generate the substantial economic values, and matrix adjustment method significantly improves the three CAW-type performances.
Research limitations/implications
The findings in this study suggest that from the aspect of economics, LHAR-CAW model can more accurately built the dynamic process of return rates and covariance matrix, respectively, and the matrix adjustment can reduce bias of realized volatility as covariance matrix estimator of return rates, and greatly improves the performance of unadjusted CAW-type models.
Practical implications
Compared with traditional low-frequency models, investors should allocate assets according to the LHAR-CAW model so as to get more economic values.
Originality/value
This study proposes LHAR-CAW model with the matrix adjustment, to account for heterogeneous leverage effect and empirically show their economic advantage. The new model and the new bias adjustment approach are pioneering and promote the evolution of financial econometrics based on high-frequency data.</description><identifier>ISSN: 2044-1398</identifier><identifier>EISSN: 2044-1401</identifier><identifier>DOI: 10.1108/CFRI-09-2018-0130</identifier><language>eng</language><publisher>Beijing: Emerald Publishing Limited</publisher><subject>Economic models ; Expected utility ; Fees & charges ; Hypotheses ; Investments ; Investors ; Management decisions ; Portfolio management ; Portfolio performance ; Risk aversion ; Securities prices ; Stochastic models ; Stock exchanges ; Stocks ; Volatility</subject><ispartof>China finance review international, 2019-08, Vol.9 (3), p.338-359</ispartof><rights>Emerald Publishing Limited</rights><rights>Emerald Publishing Limited 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c347t-bca478ca545d3d823cf641019c9890a5a0cc334818974e8712bfee782348dad53</citedby><cites>FETCH-LOGICAL-c347t-bca478ca545d3d823cf641019c9890a5a0cc334818974e8712bfee782348dad53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2273728410/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2273728410?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,11688,27924,27925,36060,44363,74895</link.rule.ids></links><search><creatorcontrib>Zhao, Shuran</creatorcontrib><creatorcontrib>Li, Jinchen</creatorcontrib><creatorcontrib>Jiang, Yaping</creatorcontrib><creatorcontrib>Ren, Peimin</creatorcontrib><title>The economic value of using CAW-type models to forecast covariance matrix</title><title>China finance review international</title><description>Purpose
The purpose of this paper is twofold: to improve the traditional conditional autoregressive Wishart (CAW) and heterogeneous autoregressive (HAR)-CAW model to account for heterogeneous leverage effect and to adjust the high-frequency volatility. The other is to confirm whether CAW-type models that have statistical advantages have economic advantages.
Design/methodology/approach
Based on the high-frequency data, this study proposed a new model to describe the volatility process according to the heterogeneous market hypothesis. Thus, the authors acquire needed and credible high-frequency data.
Findings
By designing two mean-variance frameworks and considering several economic performance measures, the authors find that compared with five other models based on daily data, CAW-type models, especially LHAR-CAW and HAR-CAW, indeed generate the substantial economic values, and matrix adjustment method significantly improves the three CAW-type performances.
Research limitations/implications
The findings in this study suggest that from the aspect of economics, LHAR-CAW model can more accurately built the dynamic process of return rates and covariance matrix, respectively, and the matrix adjustment can reduce bias of realized volatility as covariance matrix estimator of return rates, and greatly improves the performance of unadjusted CAW-type models.
Practical implications
Compared with traditional low-frequency models, investors should allocate assets according to the LHAR-CAW model so as to get more economic values.
Originality/value
This study proposes LHAR-CAW model with the matrix adjustment, to account for heterogeneous leverage effect and empirically show their economic advantage. The new model and the new bias adjustment approach are pioneering and promote the evolution of financial econometrics based on high-frequency data.</description><subject>Economic models</subject><subject>Expected utility</subject><subject>Fees & charges</subject><subject>Hypotheses</subject><subject>Investments</subject><subject>Investors</subject><subject>Management decisions</subject><subject>Portfolio management</subject><subject>Portfolio performance</subject><subject>Risk aversion</subject><subject>Securities prices</subject><subject>Stochastic models</subject><subject>Stock exchanges</subject><subject>Stocks</subject><subject>Volatility</subject><issn>2044-1398</issn><issn>2044-1401</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>M0C</sourceid><recordid>eNptkF1LwzAUhoMoOOZ-gHcBr6MnHzXJ5ShOCwNBJl6GLE21o21m0g73722ZXgiem3PgvB_wIHRN4ZZSUHf56qUgoAkDqghQDmdoxkAIQgXQ89-ba3WJFintYByVSa31DBWbD4-9C11oa4cPthk8DhUeUt2943z5Rvrj3uM2lL5JuA-4CtE7m3rswsHG2nZu_No-1l9X6KKyTfKLnz1Hr6uHTf5E1s-PRb5cE8eF7MnWWSGVs5nISl4qxl11LyhQ7bTSYDMLznEuFFVaCq8kZdvKezkKhSptmfE5ujnl7mP4HHzqzS4MsRsrDWOSS6bGuFFFTyoXQ0rRV2Yf69bGo6FgJmhmgmZAmwmamaCNHjh5fOujbcp_LX8482-d4mwx</recordid><startdate>20190819</startdate><enddate>20190819</enddate><creator>Zhao, Shuran</creator><creator>Li, Jinchen</creator><creator>Jiang, Yaping</creator><creator>Ren, Peimin</creator><general>Emerald Publishing Limited</general><general>Emerald Group Publishing Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>0U~</scope><scope>1-H</scope><scope>7RO</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X5</scope><scope>7XB</scope><scope>AFKRA</scope><scope>AXJJW</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>K6~</scope><scope>L.-</scope><scope>L.0</scope><scope>M0C</scope><scope>M2O</scope><scope>MBDVC</scope><scope>PQBIZ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope></search><sort><creationdate>20190819</creationdate><title>The economic value of using CAW-type models to forecast covariance matrix</title><author>Zhao, Shuran ; Li, Jinchen ; Jiang, Yaping ; Ren, Peimin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c347t-bca478ca545d3d823cf641019c9890a5a0cc334818974e8712bfee782348dad53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Economic models</topic><topic>Expected utility</topic><topic>Fees & charges</topic><topic>Hypotheses</topic><topic>Investments</topic><topic>Investors</topic><topic>Management decisions</topic><topic>Portfolio management</topic><topic>Portfolio performance</topic><topic>Risk aversion</topic><topic>Securities prices</topic><topic>Stochastic models</topic><topic>Stock exchanges</topic><topic>Stocks</topic><topic>Volatility</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Shuran</creatorcontrib><creatorcontrib>Li, Jinchen</creatorcontrib><creatorcontrib>Jiang, Yaping</creatorcontrib><creatorcontrib>Ren, Peimin</creatorcontrib><collection>CrossRef</collection><collection>Global News & ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>Asian Business Database</collection><collection>ProQuest_ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>Entrepreneurship Database (ProQuest)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest Central</collection><collection>Asian & European Business Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest Business Premium Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>ProQuest Business Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>ABI/INFORM global</collection><collection>ProQuest research library</collection><collection>Research Library (Corporate)</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><jtitle>China finance review international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Shuran</au><au>Li, Jinchen</au><au>Jiang, Yaping</au><au>Ren, Peimin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The economic value of using CAW-type models to forecast covariance matrix</atitle><jtitle>China finance review international</jtitle><date>2019-08-19</date><risdate>2019</risdate><volume>9</volume><issue>3</issue><spage>338</spage><epage>359</epage><pages>338-359</pages><issn>2044-1398</issn><eissn>2044-1401</eissn><abstract>Purpose
The purpose of this paper is twofold: to improve the traditional conditional autoregressive Wishart (CAW) and heterogeneous autoregressive (HAR)-CAW model to account for heterogeneous leverage effect and to adjust the high-frequency volatility. The other is to confirm whether CAW-type models that have statistical advantages have economic advantages.
Design/methodology/approach
Based on the high-frequency data, this study proposed a new model to describe the volatility process according to the heterogeneous market hypothesis. Thus, the authors acquire needed and credible high-frequency data.
Findings
By designing two mean-variance frameworks and considering several economic performance measures, the authors find that compared with five other models based on daily data, CAW-type models, especially LHAR-CAW and HAR-CAW, indeed generate the substantial economic values, and matrix adjustment method significantly improves the three CAW-type performances.
Research limitations/implications
The findings in this study suggest that from the aspect of economics, LHAR-CAW model can more accurately built the dynamic process of return rates and covariance matrix, respectively, and the matrix adjustment can reduce bias of realized volatility as covariance matrix estimator of return rates, and greatly improves the performance of unadjusted CAW-type models.
Practical implications
Compared with traditional low-frequency models, investors should allocate assets according to the LHAR-CAW model so as to get more economic values.
Originality/value
This study proposes LHAR-CAW model with the matrix adjustment, to account for heterogeneous leverage effect and empirically show their economic advantage. The new model and the new bias adjustment approach are pioneering and promote the evolution of financial econometrics based on high-frequency data.</abstract><cop>Beijing</cop><pub>Emerald Publishing Limited</pub><doi>10.1108/CFRI-09-2018-0130</doi><tpages>22</tpages></addata></record> |
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subjects | Economic models Expected utility Fees & charges Hypotheses Investments Investors Management decisions Portfolio management Portfolio performance Risk aversion Securities prices Stochastic models Stock exchanges Stocks Volatility |
title | The economic value of using CAW-type models to forecast covariance matrix |
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