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Nonlinear valuation and non-Gaussian...

Madan, Dilip B.

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Nonlinear valuation and non-Gaussian risks in finance

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nonlinear valuation and non-Gaussian risks in finance/ Dilip B. Madan, Wim Schoutens.
Author:	Madan, Dilip B.
other author:	Schoutens, Wim.
Published:	Cambridge ; New York, NY :Cambridge University Press, : 2022.,
Description:	xii, 268 p. :ill., digital ;25 cm.
Notes:	Title from publisher's bibliographic system (viewed on 24 Jan 2022).
[NT 15003449]:	Univariate risk representation using arrival rates -- Estimation of univariate arrival rates from time series data -- Estimation of univariate arrival rates from option surface data -- Multivariate arrival rates associated with prespecified univariate arrival rates -- The measure-distorted valuation as a financial objective -- Representing market realities -- Measure-distorted value-maximizing hedges in practice -- Conic hedging contributions and comparisons -- Designing optimal univariate exposures -- Multivariate static hedge designs using measure-distorted valuations -- Static portfolio allocation theory for measure-distorted valuations -- Dynamic valuation via nonlinear martingales and associated backward stochastic partial integro-differential equations -- Dynamic portfolio theory -- Enterprise valuation using infinite and finite horizon valuation of terminal liquidation -- Economic acceptability -- Trading Markovian models -- Market implied measure-distortion parameters.
Subject:	Financial risk management - Mathematical models. -
Online resource:	https://doi.org/10.1017/9781108993876
ISBN:	9781108993876

Nonlinear valuation and non-Gaussian risks in finance
Madan, Dilip B.

Nonlinear valuation and non-Gaussian risks in finance[electronic resource] /Dilip B. Madan, Wim Schoutens. - Cambridge ; New York, NY :Cambridge University Press,2022. - xii, 268 p. :ill., digital ;25 cm.

Title from publisher's bibliographic system (viewed on 24 Jan 2022).

Univariate risk representation using arrival rates -- Estimation of univariate arrival rates from time series data -- Estimation of univariate arrival rates from option surface data -- Multivariate arrival rates associated with prespecified univariate arrival rates -- The measure-distorted valuation as a financial objective -- Representing market realities -- Measure-distorted value-maximizing hedges in practice -- Conic hedging contributions and comparisons -- Designing optimal univariate exposures -- Multivariate static hedge designs using measure-distorted valuations -- Static portfolio allocation theory for measure-distorted valuations -- Dynamic valuation via nonlinear martingales and associated backward stochastic partial integro-differential equations -- Dynamic portfolio theory -- Enterprise valuation using infinite and finite horizon valuation of terminal liquidation -- Economic acceptability -- Trading Markovian models -- Market implied measure-distortion parameters.

What happens to risk as the economic horizon goes to zero and risk is seen as an exposure to a change in state that may occur instantaneously at any time? All activities that have been undertaken statically at a fixed finite horizon can now be reconsidered dynamically at a zero time horizon, with arrival rates at the core of the modeling. This book, aimed at practitioners and researchers in financial risk, delivers the theoretical framework and various applications of the newly established dynamic conic finance theory. The result is a nonlinear non-Gaussian valuation framework for risk management in finance. Risk-free assets disappear and low risk portfolios must pay for their risk reduction with negative expected returns. Hedges may be constructed to enhance value by exploiting risk interactions. Dynamic trading mechanisms are synthesized by machine learning algorithms. Optimal exposures are designed for option positioning simultaneously across all strikes and maturities.

ISBN: 9781108993876Subjects--Topical Terms:

910589
Financial risk management
--Mathematical models.

LC Class. No.: HG106 / .M333 2022

Dewey Class. No.: 332.015118

Nonlinear valuation and non-Gaussian risks in finance
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500 $a Title from publisher's bibliographic system (viewed on 24 Jan 2022).
505 0 $a Univariate risk representation using arrival rates -- Estimation of univariate arrival rates from time series data -- Estimation of univariate arrival rates from option surface data -- Multivariate arrival rates associated with prespecified univariate arrival rates -- The measure-distorted valuation as a financial objective -- Representing market realities -- Measure-distorted value-maximizing hedges in practice -- Conic hedging contributions and comparisons -- Designing optimal univariate exposures -- Multivariate static hedge designs using measure-distorted valuations -- Static portfolio allocation theory for measure-distorted valuations -- Dynamic valuation via nonlinear martingales and associated backward stochastic partial integro-differential equations -- Dynamic portfolio theory -- Enterprise valuation using infinite and finite horizon valuation of terminal liquidation -- Economic acceptability -- Trading Markovian models -- Market implied measure-distortion parameters.
520 $a What happens to risk as the economic horizon goes to zero and risk is seen as an exposure to a change in state that may occur instantaneously at any time? All activities that have been undertaken statically at a fixed finite horizon can now be reconsidered dynamically at a zero time horizon, with arrival rates at the core of the modeling. This book, aimed at practitioners and researchers in financial risk, delivers the theoretical framework and various applications of the newly established dynamic conic finance theory. The result is a nonlinear non-Gaussian valuation framework for risk management in finance. Risk-free assets disappear and low risk portfolios must pay for their risk reduction with negative expected returns. Hedges may be constructed to enhance value by exploiting risk interactions. Dynamic trading mechanisms are synthesized by machine learning algorithms. Optimal exposures are designed for option positioning simultaneously across all strikes and maturities.
650 0 $a Financial risk management $x Mathematical models. $3 910589
650 0 $a Finance $x Mathematical models. $3 578740
650 0 $a Nonlinear theories. $3 524352
650 0 $a Valuation. $3 682363
650 0 $a Gaussian processes. $3 672951
650 0 $a Multivariate analysis. $3 517467
700 1 $a Schoutens, Wim. $3 724517
856 4 0 $u https://doi.org/10.1017/9781108993876