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The role of fat-tails, multiple variance components, and pricing kernels in option pricing.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	The role of fat-tails, multiple variance components, and pricing kernels in option pricing./
Author:	Babaoglu, Kadir Gokhan.
Description:	1 online resource (104 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-08(E), Section: A.
Contained By:	Dissertation Abstracts International78-08A(E).
Subject:	Finance. -
Online resource:	click for full text (PQDT)
ISBN:	9781369667486

The role of fat-tails, multiple variance components, and pricing kernels in option pricing.
Babaoglu, Kadir Gokhan.

The role of fat-tails, multiple variance components, and pricing kernels in option pricing. - 1 online resource (104 pages)

Source: Dissertation Abstracts International, Volume: 78-08(E), Section: A.

Thesis (Ph.D.)

Includes bibliographical references

My dissertation, composed of two chapters, explores the pricing of index and individual equity options contracts. These chapters make three modeling choices on (i) state variables, (ii) return innovations and (iii) the pricing kernel, and answer the question about what we can learn from stocks and options data.

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

Mode of access: World Wide Web

ISBN: 9781369667486Subjects--Topical Terms:

559073
Finance.
Index Terms--Genre/Form:

554714
Electronic books.

The role of fat-tails, multiple variance components, and pricing kernels in option pricing.
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245 1 4 $a The role of fat-tails, multiple variance components, and pricing kernels in option pricing.
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300 $a 1 online resource (104 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 78-08(E), Section: A.
500 $a Adviser: Peter Christoffersen.
502 $a Thesis (Ph.D.) $c University of Toronto (Canada) $d 2016.
504 $a Includes bibliographical references
520 $a My dissertation, composed of two chapters, explores the pricing of index and individual equity options contracts. These chapters make three modeling choices on (i) state variables, (ii) return innovations and (iii) the pricing kernel, and answer the question about what we can learn from stocks and options data.
520 $a Both chapters specify a variance-dependent pricing kernel, which allows non-monotonicity when projected onto returns. While first chapter employs Inverse Gaussian distribution to capture fat-tailed dynamics of returns, second chapter chooses to model distribution of returns as a normal shock plus Compound Poisson jumps. Regarding the state variables, Chapter 1 uses long-run and short-run variance components, whereas Chapter 2 defines normal and jump variance components as the state variables.
520 $a The first chapter nests multiple volatility components, fat tails and a variance-dependent pricing kernel in a single option model and compare their contribution to describing returns and option data. All three features lead to statistically significant model improvements. A variance-dependent pricing kernel is economically most important and improves option fit by 17% on average and more so for two-factor models. A second volatility component improves the option fit by 9% on average. Fat tails improve option fit by just over 4% on average, but more so when a variance-dependent pricing kernel is applied. Overall these three model features are complements rather than substitutes: the importance of one feature increases in conjunction with the others.
520 $a Focusing on individual equity options, second chapter develops a new factor model that explores (i) if a separate beta for market jumps is needed, (ii) cross-sectional differences in jump betas of stocks, and (iii) the role of jump betas in explaining equity option prices. Differentiating between normal beta and jump beta, the model predicts that a stock with higher sensitivity to market jumps (normal shocks) have higher out-of-the-money (at-the-money) option prices. The results show that jump betas are needed to adequately explain equity options.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Finance. $3 559073
655 7 $a Electronic books. $2 local $3 554714
690 $a 0508
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Toronto (Canada). $b Management. $3 1179118
773 0 $t Dissertation Abstracts International $g 78-08A(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10194597 $z click for full text (PQDT)