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Monte Carlo strategies in option pri...
~
Yin, Leicheng.
Monte Carlo strategies in option pricing for SABR model.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
Monte Carlo strategies in option pricing for SABR model./
作者:
Yin, Leicheng.
面頁冊數:
1 online resource (90 pages)
附註:
Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
Contained By:
Dissertation Abstracts International77-11B(E).
標題:
Statistics. -
電子資源:
click for full text (PQDT)
ISBN:
9781339809182
Monte Carlo strategies in option pricing for SABR model.
Yin, Leicheng.
Monte Carlo strategies in option pricing for SABR model.
- 1 online resource (90 pages)
Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
Thesis (Ph.D.)
Includes bibliographical references
Option pricing problems have always been a hot topic in mathematical finance. The SABR model is a stochastic volatility model, which attempts to capture the volatility smile in derivatives markets. To price options under SABR model, there are analytical and probability approaches. The probability approach i.e. the Monte Carlo method suffers from computation inefficiency due to high dimensional state spaces. In this work, we adopt the probability approach for pricing options under the SABR model. The novelty of our contribution lies in reducing the dimensionality of Monte Carlo simulation from the high dimensional state space (time series of the underlying asset) to the 2-D or 3-D random vectors (certain summary statistics of the volatility path).
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9781339809182Subjects--Topical Terms:
556824
Statistics.
Index Terms--Genre/Form:
554714
Electronic books.
Monte Carlo strategies in option pricing for SABR model.
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Option pricing problems have always been a hot topic in mathematical finance. The SABR model is a stochastic volatility model, which attempts to capture the volatility smile in derivatives markets. To price options under SABR model, there are analytical and probability approaches. The probability approach i.e. the Monte Carlo method suffers from computation inefficiency due to high dimensional state spaces. In this work, we adopt the probability approach for pricing options under the SABR model. The novelty of our contribution lies in reducing the dimensionality of Monte Carlo simulation from the high dimensional state space (time series of the underlying asset) to the 2-D or 3-D random vectors (certain summary statistics of the volatility path).
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