Semi-parametric time series modelling with autocopulas
In this paper we present an application of the use of autocopulas for modelling financial time series showing serial dependencies that are not necessarily linear. The approach presented here is semi-parametric in that it is characterized by a non-parametric autocopula and parametric marginals. One a...
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| creator | Ware, Antony Asadzadeh, Ilnaz |
| description | In this paper we present an application of the use of autocopulas for
modelling financial time series showing serial dependencies that are not
necessarily linear. The approach presented here is semi-parametric in that it
is characterized by a non-parametric autocopula and parametric marginals. One
advantage of using autocopulas is that they provide a general representation of
the auto-dependency of the time series, in particular making it possible to
study the interdependence of values of the series at different extremes
separately. The specific time series that is studied here comes from daily cash
flows involving the product of daily natural gas price and daily temperature
deviations from normal levels. Seasonality is captured by using a time
dependent normal inverse Gaussian (NIG) distribution fitted to the raw values. |
| doi_str_mv | 10.48550/arxiv.1507.04767 |
| format | Article |
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modelling financial time series showing serial dependencies that are not
necessarily linear. The approach presented here is semi-parametric in that it
is characterized by a non-parametric autocopula and parametric marginals. One
advantage of using autocopulas is that they provide a general representation of
the auto-dependency of the time series, in particular making it possible to
study the interdependence of values of the series at different extremes
separately. The specific time series that is studied here comes from daily cash
flows involving the product of daily natural gas price and daily temperature
deviations from normal levels. Seasonality is captured by using a time
dependent normal inverse Gaussian (NIG) distribution fitted to the raw values.</description><identifier>DOI: 10.48550/arxiv.1507.04767</identifier><language>eng</language><subject>Quantitative Finance - Risk Management</subject><creationdate>2015-07</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1507.04767$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1507.04767$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Ware, Antony</creatorcontrib><creatorcontrib>Asadzadeh, Ilnaz</creatorcontrib><title>Semi-parametric time series modelling with autocopulas</title><description>In this paper we present an application of the use of autocopulas for
modelling financial time series showing serial dependencies that are not
necessarily linear. The approach presented here is semi-parametric in that it
is characterized by a non-parametric autocopula and parametric marginals. One
advantage of using autocopulas is that they provide a general representation of
the auto-dependency of the time series, in particular making it possible to
study the interdependence of values of the series at different extremes
separately. The specific time series that is studied here comes from daily cash
flows involving the product of daily natural gas price and daily temperature
deviations from normal levels. Seasonality is captured by using a time
dependent normal inverse Gaussian (NIG) distribution fitted to the raw values.</description><subject>Quantitative Finance - Risk Management</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj8tuwjAURL1hUQEf0FX9Awl2_LjJskIUKiF10eyjm_gaLMVN5IQ-_r5AuxrNZs4cxh6lyHVpjNhg-g6fuTQCcqHBwgOz7xRDNmLCSHMKHZ9DJD5RCjTxODjq-_Bx4l9hPnO8zEM3jJcepxVbeOwnWv_nktUvu3p7yI5v-9ft8zFDC5DpEqkSDq7oljoDpAstnBSFda1VLXhvQClrDUhrKu8r1xZClrrT6tpNoZbs6W_2frwZU4iYfpqbQHMXUL8ggT-g</recordid><startdate>20150716</startdate><enddate>20150716</enddate><creator>Ware, Antony</creator><creator>Asadzadeh, Ilnaz</creator><scope>GOX</scope></search><sort><creationdate>20150716</creationdate><title>Semi-parametric time series modelling with autocopulas</title><author>Ware, Antony ; Asadzadeh, Ilnaz</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a677-48ae90d7550bec57e4240d1026db63b7ff573366571659ff9db20184c43659523</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Quantitative Finance - Risk Management</topic><toplevel>online_resources</toplevel><creatorcontrib>Ware, Antony</creatorcontrib><creatorcontrib>Asadzadeh, Ilnaz</creatorcontrib><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ware, Antony</au><au>Asadzadeh, Ilnaz</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Semi-parametric time series modelling with autocopulas</atitle><date>2015-07-16</date><risdate>2015</risdate><abstract>In this paper we present an application of the use of autocopulas for
modelling financial time series showing serial dependencies that are not
necessarily linear. The approach presented here is semi-parametric in that it
is characterized by a non-parametric autocopula and parametric marginals. One
advantage of using autocopulas is that they provide a general representation of
the auto-dependency of the time series, in particular making it possible to
study the interdependence of values of the series at different extremes
separately. The specific time series that is studied here comes from daily cash
flows involving the product of daily natural gas price and daily temperature
deviations from normal levels. Seasonality is captured by using a time
dependent normal inverse Gaussian (NIG) distribution fitted to the raw values.</abstract><doi>10.48550/arxiv.1507.04767</doi><oa>free_for_read</oa></addata></record> |
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| title | Semi-parametric time series modelling with autocopulas |
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