Counterparty risk: credit valuation adjustment variability and value-at-risk

Breton, Michèle; Marzouk, Oussama

doi:10.21314/jor.2019.411

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“…For instance, if the counterparty ends up in financial distress, it is reasonable to assume that the bank (which in this chapter is assumed to be the risk-free party) would be more willing to exercise the callable derivatives, in order to lower its exposure to the counterparty. Even though it seems common to ignore the effect of a defaultable counterparty when computing risky derivative values, it has been discussed in the literature, see e.g., [55], [38], [39] and [56]. In the case of a single derivative [55] states that the exercise region for a risk-free derivative is always a subset of the exercise region for a risky counterpart.…”

Section: Risky Valuation and Cvamentioning

confidence: 99%

“…To accurately compute the risky option value, one needs to adjust the exercise strategy for the risk of a default of the counterparty. Therefore, it is not sufficient to compute the exposure of the option when computing the CVA (for a discussion see e.g., [38], [39]). With minor adjustments to the proposed algorithm, one could compute this kind of advanced CVA (as opposed to the approximation, which is based on the exposure).…”

Section: Introductionmentioning

confidence: 99%

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Neural networks for stochastic control and decision making in mathematical finance

Andersson

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As the title suggests, this thesis is devoted to machine learning algorithms for stochastic control with applications in mathematical finance. In the first two chapters, we utilize neural networks to find the optimal exercise strategies of high-dimensional American options. Moreover, we compute option values along stochastic trajectories of the underlying assets, which is crucial in modern risk management. In Chapter 3, we approximate strongly coupled FBSDEs with a sequence of neural networks. More specifically, we are concerend with high-dimensional FBSDEs stemming from stochastic optimal control problems. These equations are inherently difficult to approximate due the coupling, and most classical methods as well as modern day machine learning algorithms break down. Our approach is novel in the sense that it combines mathematical structure from the initial control problem with the associate FBSDE which is shown to be both robust and accurate for a wide range of problems. In the final chapter, we consider time-inconsistent portfolio optimization problems. In general, for time-inconsistent problems the dynamic programming principle does not hold and in turn, the global problem cannot be divided into local sub-problems. Instead, we take on a fully data driven neural network-based approach to approximate the optimal allocation strategy directly. Beyond the classical instruments stocks and bonds, we also allow the investor to buy and sell options. In this way, we increase the flexibility in shaping the terminal wealth distribution.

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Section: Risky Valuation and Cvamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural networks for stochastic control and decision making in mathematical finance

Andersson

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Counterparty risk: credit valuation adjustment variability and value-at-risk

Cited by 1 publication

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Neural networks for stochastic control and decision making in mathematical finance

Neural networks for stochastic control and decision making in mathematical finance

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