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Dynamic Portfolio Analysis Based on Realized Higher Moments
Realized higher moments, which are the expansion of realized volatility in high-frequency time series, is proposed in the paper to measure the time-varying financial risk. The dynamic assets allocation is settled by Taylor series expansion of utility function. We apply realized higher moments to por...
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creator | Jiang Cui-xia Liu Jing-dong |
description | Realized higher moments, which are the expansion of realized volatility in high-frequency time series, is proposed in the paper to measure the time-varying financial risk. The dynamic assets allocation is settled by Taylor series expansion of utility function. We apply realized higher moments to portfolio analysis, and derive dynamic portfolio strategy. Our model repair two defects in traditional portfolio theory, without considering higher moments risk and settle problem statically. High frequency financial data in Chinese stock markets are selected to make empirical research. The empirical results show that higher moments risk possess volatility cluster, and dynamic portfolio is obviously superior to static portfolio. |
doi_str_mv | 10.1109/ICNC.2009.273 |
format | conference_proceeding |
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The dynamic assets allocation is settled by Taylor series expansion of utility function. We apply realized higher moments to portfolio analysis, and derive dynamic portfolio strategy. Our model repair two defects in traditional portfolio theory, without considering higher moments risk and settle problem statically. High frequency financial data in Chinese stock markets are selected to make empirical research. The empirical results show that higher moments risk possess volatility cluster, and dynamic portfolio is obviously superior to static portfolio.</description><identifier>ISSN: 2157-9555</identifier><identifier>ISBN: 0769537367</identifier><identifier>ISBN: 9780769537368</identifier><identifier>DOI: 10.1109/ICNC.2009.273</identifier><identifier>LCCN: 2009903793</identifier><language>eng</language><publisher>IEEE</publisher><subject>dynamic portfolio ; Forward contracts ; Frequency estimation ; Frequency measurement ; high-frequency ; higher moments ; Mathematics ; Paper technology ; Portfolios ; Pricing ; realized volatility ; Risk analysis ; Time measurement ; Time series analysis</subject><ispartof>2009 Fifth International Conference on Natural Computation, 2009, Vol.6, p.360-364</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5366428$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54555,54920,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5366428$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Jiang Cui-xia</creatorcontrib><creatorcontrib>Liu Jing-dong</creatorcontrib><title>Dynamic Portfolio Analysis Based on Realized Higher Moments</title><title>2009 Fifth International Conference on Natural Computation</title><addtitle>ICNC</addtitle><description>Realized higher moments, which are the expansion of realized volatility in high-frequency time series, is proposed in the paper to measure the time-varying financial risk. The dynamic assets allocation is settled by Taylor series expansion of utility function. We apply realized higher moments to portfolio analysis, and derive dynamic portfolio strategy. Our model repair two defects in traditional portfolio theory, without considering higher moments risk and settle problem statically. High frequency financial data in Chinese stock markets are selected to make empirical research. The empirical results show that higher moments risk possess volatility cluster, and dynamic portfolio is obviously superior to static portfolio.</description><subject>dynamic portfolio</subject><subject>Forward contracts</subject><subject>Frequency estimation</subject><subject>Frequency measurement</subject><subject>high-frequency</subject><subject>higher moments</subject><subject>Mathematics</subject><subject>Paper technology</subject><subject>Portfolios</subject><subject>Pricing</subject><subject>realized volatility</subject><subject>Risk analysis</subject><subject>Time measurement</subject><subject>Time series analysis</subject><issn>2157-9555</issn><isbn>0769537367</isbn><isbn>9780769537368</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotjjtPwzAURi1BJdrSkYnFfyDh2jf2xWIq4dFK5SHUvbITG4zyQHGW8Ospguk7w9HRx9iFgFwIMFfb8rnMJYDJJeEJWwBpo5BQ0ymbS6EoM0qpGVv8OgaQDJ6xVUqfAICCiMDM2c3d1Nk2Vvy1H8bQN7Hn6842U4qJ39rka953_M3bJn4feRPfP_zAn_rWd2M6Z7Ngm-RX_7tk-4f7fbnJdi-P23K9y6KBMSOQtTXKgXZBoNNGUDBK6qo6fiWhaxu0RqhVCLUsHAQoMMhQqcI5FbzFJbv8y0bv_eFriK0dpoNCrQt5jT8OMkip</recordid><startdate>200908</startdate><enddate>200908</enddate><creator>Jiang Cui-xia</creator><creator>Liu Jing-dong</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200908</creationdate><title>Dynamic Portfolio Analysis Based on Realized Higher Moments</title><author>Jiang Cui-xia ; Liu Jing-dong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-702da95b06bf13b6917f9526cc373716daf6630d5ffd24b0f043f2fc54bb5fea3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>dynamic portfolio</topic><topic>Forward contracts</topic><topic>Frequency estimation</topic><topic>Frequency measurement</topic><topic>high-frequency</topic><topic>higher moments</topic><topic>Mathematics</topic><topic>Paper technology</topic><topic>Portfolios</topic><topic>Pricing</topic><topic>realized volatility</topic><topic>Risk analysis</topic><topic>Time measurement</topic><topic>Time series analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Jiang Cui-xia</creatorcontrib><creatorcontrib>Liu Jing-dong</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library Online</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jiang Cui-xia</au><au>Liu Jing-dong</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Dynamic Portfolio Analysis Based on Realized Higher Moments</atitle><btitle>2009 Fifth International Conference on Natural Computation</btitle><stitle>ICNC</stitle><date>2009-08</date><risdate>2009</risdate><volume>6</volume><spage>360</spage><epage>364</epage><pages>360-364</pages><issn>2157-9555</issn><isbn>0769537367</isbn><isbn>9780769537368</isbn><abstract>Realized higher moments, which are the expansion of realized volatility in high-frequency time series, is proposed in the paper to measure the time-varying financial risk. The dynamic assets allocation is settled by Taylor series expansion of utility function. We apply realized higher moments to portfolio analysis, and derive dynamic portfolio strategy. Our model repair two defects in traditional portfolio theory, without considering higher moments risk and settle problem statically. High frequency financial data in Chinese stock markets are selected to make empirical research. The empirical results show that higher moments risk possess volatility cluster, and dynamic portfolio is obviously superior to static portfolio.</abstract><pub>IEEE</pub><doi>10.1109/ICNC.2009.273</doi><tpages>5</tpages></addata></record> |
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ispartof | 2009 Fifth International Conference on Natural Computation, 2009, Vol.6, p.360-364 |
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source | IEEE Xplore All Conference Series |
subjects | dynamic portfolio Forward contracts Frequency estimation Frequency measurement high-frequency higher moments Mathematics Paper technology Portfolios Pricing realized volatility Risk analysis Time measurement Time series analysis |
title | Dynamic Portfolio Analysis Based on Realized Higher Moments |
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