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Predicting the Volatility Index Retu...

University of Toronto (Canada).

Predicting the Volatility Index Returns Using Machine Learning.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Predicting the Volatility Index Returns Using Machine Learning./
Author:	Yu, Michael.
Description:	1 online resource (23 pages)
Notes:	Source: Masters Abstracts International, Volume: 57-02.
Contained By:	Masters Abstracts International57-02(E).
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9780355453157

Predicting the Volatility Index Returns Using Machine Learning.
Yu, Michael.

Predicting the Volatility Index Returns Using Machine Learning. - 1 online resource (23 pages)

Source: Masters Abstracts International, Volume: 57-02.

Thesis (M.Sc.)

Includes bibliographical references

We probe how predictable the short term future behaviour of the Chicago Board Options Exchange (CBOE) Volatility Index (ticker symbol VIX) is given past market price data within the constraints of a simple classic machine learning framework. We use past VIX and SPX price time windows as input to predict the movement direction, i.e. sign of the return, of VIX over the next 1 to 6 weekdays. For successful cases of pre- dicting return direction from one particular weekday to another particular future weekday, we have moderately reliable accuracies of between about 55% and 65% depending on the particular time bridge. We find that 1 day returns are difficult to predict except for a few particular cases, and as the prediction window grows we have models that can predict more and more accurately up to a consistent 62% for both 5 days and 6 days in the future.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355453157Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Predicting the Volatility Index Returns Using Machine Learning.
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500 $a Source: Masters Abstracts International, Volume: 57-02.
500 $a Adviser: Luis Seco.
502 $a Thesis (M.Sc.) $c University of Toronto (Canada) $d 2017.
504 $a Includes bibliographical references
520 $a We probe how predictable the short term future behaviour of the Chicago Board Options Exchange (CBOE) Volatility Index (ticker symbol VIX) is given past market price data within the constraints of a simple classic machine learning framework. We use past VIX and SPX price time windows as input to predict the movement direction, i.e. sign of the return, of VIX over the next 1 to 6 weekdays. For successful cases of pre- dicting return direction from one particular weekday to another particular future weekday, we have moderately reliable accuracies of between about 55% and 65% depending on the particular time bridge. We find that 1 day returns are difficult to predict except for a few particular cases, and as the prediction window grows we have models that can predict more and more accurately up to a consistent 62% for both 5 days and 6 days in the future.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Finance. $3 559073
650 4 $a Mathematics. $3 527692
650 4 $a Artificial intelligence. $3 559380
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