Presentation 2014-01-21
Long-short Strategy Based on the Nonlinear Portfolio Model with the DCC Model
Satoshi INOSE, Tomoya SUZUKI, Kazuo YAMANAKA,
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Abstract(in English) The nonlinear portfolio model which we proposed in the previous studies estimates future return rates by a nonlinear prediction model, but these future risks have not been estimated aggressively. In the present study, we modify the nonlinear portfolio model by applying the DCC model, which is one of multivariate risk estimation models. Moreover, we build two portfolios not only with long positions of the stocks whose the expected return rates are positive but also with short positions of the stocks whose expected return rates are negative. This long and short strategy might improve the portfolio effect for spreading investment risks because it can increase the number of stocks to compose the whole portfolio. Therefore, the present study performed some investment simulations based on real stock data to demonstrate that our proposed portfolio model can realize higher profits and lower risks, simultaneously.
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Keyword(in English) nonlinear prediction model / multivariate volatility prediction model / portfolio theory
Paper # NLP2013-131
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Conference Information
Committee NLP
Conference Date 2014/1/14(1days)
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Registration To Nonlinear Problems (NLP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Long-short Strategy Based on the Nonlinear Portfolio Model with the DCC Model
Sub Title (in English)
Keyword(1) nonlinear prediction model
Keyword(2) multivariate volatility prediction model
Keyword(3) portfolio theory
1st Author's Name Satoshi INOSE
1st Author's Affiliation Graduate School of Science and Engineering, Ibaraki University()
2nd Author's Name Tomoya SUZUKI
2nd Author's Affiliation College of Engineering, Ibaraki University
3rd Author's Name Kazuo YAMANAKA
3rd Author's Affiliation College of Engineering, Ibaraki University
Date 2014-01-21
Paper # NLP2013-131
Volume (vol) vol.113
Number (no) 383
Page pp.pp.-
#Pages 6
Date of Issue