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Journal ArticleDOI

Backward Stochastic Differential Equations in Finance

TLDR
In this article, different properties of backward stochastic differential equations and their applications to finance are discussed. But the main focus of this paper is on the theory of contingent claim valuation, especially cases with constraints.
Abstract
We are concerned with different properties of backward stochastic differential equations and their applications to finance. These equations, first introduced by Pardoux and Peng (1990), are useful for the theory of contingent claim valuation, especially cases with constraints and for the theory of recursive utilities, introduced by Duffie and Epstein (1992a, 1992b).

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Stochastic Equations in Infinite Dimensions

TL;DR: In this paper, the existence and uniqueness of nonlinear equations with additive and multiplicative noise was investigated. But the authors focused on the uniqueness of solutions and not on the properties of solutions.
Journal ArticleDOI

Solving high-dimensional partial differential equations using deep learning

TL;DR: A deep learning-based approach that can handle general high-dimensional parabolic PDEs using backward stochastic differential equations and the gradient of the unknown solution is approximated by neural networks, very much in the spirit of deep reinforcement learning with the gradient acting as the policy function.
Journal ArticleDOI

Backward stochastic differential equations and partial differential equations with quadratic growth

TL;DR: In this paper, the authors provide existence, comparison and stability results for one-dimensional backward stochastic differential equations (BSDEs) when the coefficient (or generator) $F(t,Y, Z)$ is continuous and has a quadratic growth in $Z$ and the terminal condition is bounded.
Journal ArticleDOI

Ambiguity, risk, and asset returns in continuous time

TL;DR: In this article, a continuous-time intertemporal version of multiple-priors utility, where aversion to ambiguity is admissible, is presented. But the model is restricted to a representative agent asset market setting.
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Ambiguity, risk and asset returns in continuous time

TL;DR: In this article, a continuous-time intertemporal version of multiple-priors utility, where aversion to ambiguity is admissible, is proposed for a representative agent asset market setting, which delivers restrictions on excess returns that admit interpretations reflecting a premium for risk and a separate premium for ambiguity.
References
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Journal ArticleDOI

The Pricing of Options and Corporate Liabilities

TL;DR: In this paper, a theoretical valuation formula for options is derived, based on the assumption that options are correctly priced in the market and it should not be possible to make sure profits by creating portfolios of long and short positions in options and their underlying stocks.
Book

Theory of rational option pricing

TL;DR: In this paper, the authors deduced a set of restrictions on option pricing formulas from the assumption that investors prefer more to less, which are necessary conditions for a formula to be consistent with a rational pricing theory.
Book

Brownian Motion and Stochastic Calculus

TL;DR: In this paper, the authors present a characterization of continuous local martingales with respect to Brownian motion in terms of Markov properties, including the strong Markov property, and a generalized version of the Ito rule.
Book

Continuous martingales and Brownian motion

Daniel Revuz, +1 more
TL;DR: In this article, the authors present a comprehensive survey of the literature on limit theorems in distribution in function spaces, including Girsanov's Theorem, Bessel Processes, and Ray-Knight Theorem.
Journal ArticleDOI

Optimum consumption and portfolio rules in a continuous-time model☆

TL;DR: In this paper, the authors considered the continuous-time consumption-portfolio problem for an individual whose income is generated by capital gains on investments in assets with prices assumed to satisfy the geometric Brownian motion hypothesis, which implies that asset prices are stationary and lognormally distributed.
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