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Large Deviations and Asymptotic Methods in Finance

Overview of attention for book
Cover of 'Large Deviations and Asymptotic Methods in Finance'

Table of Contents

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    Book Overview
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    Chapter 1 Probability Distribution in the SABR Model of Stochastic Volatility
  3. Altmetric Badge
    Chapter 2 Large Deviations and Asymptotic Methods in Finance
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    Chapter 3 Unifying the BGM and SABR Models: A Short Ride in Hyperbolic Geometry
  5. Altmetric Badge
    Chapter 4 Second Order Expansion for Implied Volatility in Two Factor Local Stochastic Volatility Models and Applications to the Dynamic $$\lambda $$ -Sabr Model
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    Chapter 5 General Asymptotics of Wiener Functionals and Application to Implied Volatilities
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    Chapter 6 Implied Volatility of Basket Options at Extreme Strikes
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    Chapter 7 Small-Time Asymptotics for the At-the-Money Implied Volatility in a Multi-dimensional Local Volatility Model
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    Chapter 8 A Remark on Gatheral’s ‘Most-Likely Path Approximation’ of Implied Volatility
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    Chapter 9 Implied Volatility from Local Volatility: A Path Integral Approach
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    Chapter 10 Extrapolation Analytics for Dupire’s Local Volatility
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    Chapter 11 The Gärtner-Ellis Theorem, Homogenization, and Affine Processes
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    Chapter 12 Asymptotics for $$d$$ -Dimensional Lévy-Type Processes
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    Chapter 13 Asymptotic Expansion Approach in Finance
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    Chapter 14 On Small Time Asymptotics for Rough Differential Equations Driven by Fractional Brownian Motions
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    Chapter 15 On Singularities in the Heston Model
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    Chapter 16 On the probability density function of baskets
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    Chapter 17 On Small-Noise Equations with Degenerate Limiting System Arising from Volatility Models
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    Chapter 18 Long Time Asymptotics for Optimal Investment
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    Chapter 19 Systemic Risk and Default Clustering for Large Financial Systems
  21. Altmetric Badge
    Chapter 20 Estimation of Volatility Functionals: The Case of a $$\sqrt{n}$$ Window
Attention for Chapter 16: On the probability density function of baskets
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Chapter title
On the probability density function of baskets
Chapter number 16
Book title
Large Deviations and Asymptotic Methods in Finance
Published in
arXiv, June 2013
DOI 10.1007/978-3-319-11605-1_16
Book ISBNs
978-3-31-911604-4, 978-3-31-911605-1
Authors

Christian Bayer, Peter Friz, Peter Laurence, Peter K. Friz

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 20%
Student > Postgraduate 1 20%
Student > Master 1 20%
Unknown 2 40%
Readers by discipline Count As %
Mathematics 1 20%
Computer Science 1 20%
Engineering 1 20%
Unknown 2 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 October 2023.
All research outputs
#16,847,412
of 25,552,205 outputs
Outputs from arXiv
#312,545
of 928,033 outputs
Outputs of similar age
#128,959
of 210,192 outputs
Outputs of similar age from arXiv
#1,451
of 8,360 outputs
Altmetric has tracked 25,552,205 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 928,033 research outputs from this source. They receive a mean Attention Score of 4.3. This one has gotten more attention than average, scoring higher than 61% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 210,192 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8,360 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.