Cross-Commodity Spot Price Modeling with Stochastic Volatility and Leverage For Energy Markets
Spot prices in energy markets exhibit special features, such as price spikes, mean reversion, stochastic volatility, inverse leverage effect, and dependencies between the commodities. In this paper a multivariate stochastic volatility model is introduced which captures these features. The second-ord...
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| Veröffentlicht in: | Advances in applied probability 2013-06, Vol.45 (2), p.545-571 |
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| container_title | Advances in applied probability |
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| creator | Benth, F. E. Vos, L. |
| description | Spot prices in energy markets exhibit special features, such as price spikes, mean reversion, stochastic volatility, inverse leverage effect, and dependencies between the commodities. In this paper a multivariate stochastic volatility model is introduced which captures these features. The second-order structure and stationarity of the model are analyzed in detail. A simulation method for Monte Carlo generation of price paths is introduced and a numerical example is presented. |
| doi_str_mv | 10.1239/aap/1370870129 |
| format | Article |
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E.</creatorcontrib><creatorcontrib>Vos, L.</creatorcontrib><title>Cross-Commodity Spot Price Modeling with Stochastic Volatility and Leverage For Energy Markets</title><title>Advances in applied probability</title><addtitle>Advances in Applied Probability</addtitle><description>Spot prices in energy markets exhibit special features, such as price spikes, mean reversion, stochastic volatility, inverse leverage effect, and dependencies between the commodities. In this paper a multivariate stochastic volatility model is introduced which captures these features. The second-order structure and stationarity of the model are analyzed in detail. A simulation method for Monte Carlo generation of price paths is introduced and a numerical example is presented.</description><subject>15A04</subject><subject>60G10</subject><subject>60G51</subject><subject>60H30</subject><subject>91G20</subject><subject>91G60</subject><subject>Commodities</subject><subject>Commodity prices</subject><subject>Computer simulation</subject><subject>Eigenvalues</subject><subject>Electricity</subject><subject>Energy industry</subject><subject>Energy market</subject><subject>Financial leverage</subject><subject>General Applied Probability</subject><subject>Inverse</subject><subject>leverage</subject><subject>Market prices</subject><subject>Markets</subject><subject>Mathematical models</subject><subject>Matrices</subject><subject>Modeling</subject><subject>Monte Carlo methods</subject><subject>Monte Carlo simulation</subject><subject>Ornstein-Uhlenbeck process</subject><subject>Price volatility</subject><subject>Probability</subject><subject>Spikes</subject><subject>Stochastic models</subject><subject>stochastic volatility</subject><subject>Stochasticity</subject><subject>Studies</subject><subject>subordinator</subject><subject>Volatility</subject><issn>0001-8678</issn><issn>1475-6064</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNptkUFrGzEQhUVJoY6ba24FQS69bCKtpJV0S1nsNGDTQpocs6jaWVvO7sqV5AT_-66xcULa0zDizTdvnhA6p-SS5kxfGbO-okwSJQnN9Qc0olyKrCAFP0EjQgjNVCHVJ3Qa42pomVRkhB7L4GPMSt91vnZpi-_WPuGfwVnAc19D6_oFfnFpie-St0sTk7P4wbcmuXYnN32NZ_AMwSwAT33Akx7CYovnJjxBip_Rx8a0Ec4OdYzup5Nf5fds9uPmtvw2yyzPScokN1oLzUmtGgVCUiUpI1IUTIAA03DLOBMNKTTPlTA544Iz2TBma10AtWyMrvfcdfArsAk2tnV1tQ6uM2FbeeOq8n52eD2UIa_qNa8B8fWI-LOBmKrORQtta3rwm1hRnmtVUDI4GaOLd9KV34R-OHAAas2loiwfVJd7ld1FHKA52qGk2v3Yvw6-7AdWMflwVA-XF4zRHZAcgKb7HVy9gDd7_4_8C4RjoQI</recordid><startdate>20130601</startdate><enddate>20130601</enddate><creator>Benth, F. 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E. ; Vos, L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c420t-74a995940d8f8e5718713075635e5eaf4c3435f0694285a2345437f33cd96e1c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>15A04</topic><topic>60G10</topic><topic>60G51</topic><topic>60H30</topic><topic>91G20</topic><topic>91G60</topic><topic>Commodities</topic><topic>Commodity prices</topic><topic>Computer simulation</topic><topic>Eigenvalues</topic><topic>Electricity</topic><topic>Energy industry</topic><topic>Energy market</topic><topic>Financial leverage</topic><topic>General Applied Probability</topic><topic>Inverse</topic><topic>leverage</topic><topic>Market prices</topic><topic>Markets</topic><topic>Mathematical models</topic><topic>Matrices</topic><topic>Modeling</topic><topic>Monte Carlo methods</topic><topic>Monte Carlo simulation</topic><topic>Ornstein-Uhlenbeck process</topic><topic>Price volatility</topic><topic>Probability</topic><topic>Spikes</topic><topic>Stochastic models</topic><topic>stochastic volatility</topic><topic>Stochasticity</topic><topic>Studies</topic><topic>subordinator</topic><topic>Volatility</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Benth, F. 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E.</au><au>Vos, L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Cross-Commodity Spot Price Modeling with Stochastic Volatility and Leverage For Energy Markets</atitle><jtitle>Advances in applied probability</jtitle><addtitle>Advances in Applied Probability</addtitle><date>2013-06-01</date><risdate>2013</risdate><volume>45</volume><issue>2</issue><spage>545</spage><epage>571</epage><pages>545-571</pages><issn>0001-8678</issn><eissn>1475-6064</eissn><coden>AAPBBD</coden><abstract>Spot prices in energy markets exhibit special features, such as price spikes, mean reversion, stochastic volatility, inverse leverage effect, and dependencies between the commodities. In this paper a multivariate stochastic volatility model is introduced which captures these features. The second-order structure and stationarity of the model are analyzed in detail. A simulation method for Monte Carlo generation of price paths is introduced and a numerical example is presented.</abstract><cop>Cambridge, UK</cop><pub>Cambridge University Press</pub><doi>10.1239/aap/1370870129</doi><tpages>27</tpages></addata></record> |
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| ispartof | Advances in applied probability, 2013-06, Vol.45 (2), p.545-571 |
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| language | eng |
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| source | JSTOR Mathematics & Statistics; JSTOR Archive Collection A-Z Listing; Cambridge University Press Journals Complete |
| subjects | 15A04 60G10 60G51 60H30 91G20 91G60 Commodities Commodity prices Computer simulation Eigenvalues Electricity Energy industry Energy market Financial leverage General Applied Probability Inverse leverage Market prices Markets Mathematical models Matrices Modeling Monte Carlo methods Monte Carlo simulation Ornstein-Uhlenbeck process Price volatility Probability Spikes Stochastic models stochastic volatility Stochasticity Studies subordinator Volatility |
| title | Cross-Commodity Spot Price Modeling with Stochastic Volatility and Leverage For Energy Markets |
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