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Density forecast comparisons for stock prices, obtained from high-frequency returns and daily option prices

Rui Fan Orcid Logo, Stephen J. Taylor, Matteo Sandri

Journal of Futures Markets, Volume: 38, Issue: 1, Pages: 83 - 103

Swansea University Author: Rui Fan Orcid Logo

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DOI (Published version): 10.1002/fut.21859

Abstract

This paper presents the first comparison of the accuracy of density forecasts for stock prices. Six sets of forecasts are evaluated for DJIA stocks, across four forecast horizons. Two forecasts are risk-neutral densities implied by the Black-Scholes and Heston models. The third set are historical lo...

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Published in: Journal of Futures Markets
ISSN: 02707314
Published: Wiley 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa33240
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first_indexed 2017-05-05T13:04:04Z
last_indexed 2018-10-27T03:56:51Z
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spelling 2018-10-26T19:09:16.9408911 v2 33240 2017-05-05 Density forecast comparisons for stock prices, obtained from high-frequency returns and daily option prices 4259d5c5f4095e7abae064ef8775c92b 0000-0002-8147-5758 Rui Fan Rui Fan true false 2017-05-05 BAF This paper presents the first comparison of the accuracy of density forecasts for stock prices. Six sets of forecasts are evaluated for DJIA stocks, across four forecast horizons. Two forecasts are risk-neutral densities implied by the Black-Scholes and Heston models. The third set are historical lognormal densities with dispersion determined by forecasts of realized variances obtained from 5-minute returns. Three further sets are defined by transforming risk-neutral and historical densities into real-world densities. The most accurate method applies the risk transformation to the Black-Scholes densities. This method outperforms all others for 87% of the comparisons made using the likelihood criterion. Journal Article Journal of Futures Markets 38 1 83 103 Wiley 02707314 8 12 2017 2017-12-08 10.1002/fut.21859 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2018-10-26T19:09:16.9408911 2017-05-05T12:30:39.6068308 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Rui Fan 0000-0002-8147-5758 1 Stephen J. Taylor 2 Matteo Sandri 3 0033240-05052017123151.pdf density2017.pdf 2017-05-05T12:31:51.6330000 Output 1296777 application/pdf Accepted Manuscript true 2019-06-05T00:00:00.0000000 true eng
title Density forecast comparisons for stock prices, obtained from high-frequency returns and daily option prices
spellingShingle Density forecast comparisons for stock prices, obtained from high-frequency returns and daily option prices
Rui Fan
title_short Density forecast comparisons for stock prices, obtained from high-frequency returns and daily option prices
title_full Density forecast comparisons for stock prices, obtained from high-frequency returns and daily option prices
title_fullStr Density forecast comparisons for stock prices, obtained from high-frequency returns and daily option prices
title_full_unstemmed Density forecast comparisons for stock prices, obtained from high-frequency returns and daily option prices
title_sort Density forecast comparisons for stock prices, obtained from high-frequency returns and daily option prices
author_id_str_mv 4259d5c5f4095e7abae064ef8775c92b
author_id_fullname_str_mv 4259d5c5f4095e7abae064ef8775c92b_***_Rui Fan
author Rui Fan
author2 Rui Fan
Stephen J. Taylor
Matteo Sandri
format Journal article
container_title Journal of Futures Markets
container_volume 38
container_issue 1
container_start_page 83
publishDate 2017
institution Swansea University
issn 02707314
doi_str_mv 10.1002/fut.21859
publisher Wiley
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
document_store_str 1
active_str 0
description This paper presents the first comparison of the accuracy of density forecasts for stock prices. Six sets of forecasts are evaluated for DJIA stocks, across four forecast horizons. Two forecasts are risk-neutral densities implied by the Black-Scholes and Heston models. The third set are historical lognormal densities with dispersion determined by forecasts of realized variances obtained from 5-minute returns. Three further sets are defined by transforming risk-neutral and historical densities into real-world densities. The most accurate method applies the risk transformation to the Black-Scholes densities. This method outperforms all others for 87% of the comparisons made using the likelihood criterion.
published_date 2017-12-08T03:40:54Z
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score 11.012678

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