2018
DOI: 10.1007/s00780-018-0377-3
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An ergodic BSDE approach to forward entropic risk measures: representation and large-maturity behavior

Abstract: Using elements from the theory of ergodic backward stochastic differential equations (BSDE), we study the behavior of forward entropic risk measures. We provide their general representation results (via both BSDE and convex duality) and examine their behavior for risk positions of long maturities. We show that forward entropic risk measures converge to some constant exponentially fast. We also compare them with their classical counterparts and derive a parity result.

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Cited by 26 publications
(28 citation statements)
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“…It bypasses a number of aforementioned difficulties inherited in the associated SPDE. See also [12] for a further development of this method to study forward entropic risk measures.…”
mentioning
confidence: 99%
“…It bypasses a number of aforementioned difficulties inherited in the associated SPDE. See also [12] for a further development of this method to study forward entropic risk measures.…”
mentioning
confidence: 99%
“…Note that ( 15) and ( 16) are the martingale characterization of the lower value of the game in (12), whereas ( 18) and ( 19) characterize the upper value of the game in (13).…”
Section: A Stochastic Differential Game Approachmentioning
confidence: 99%
“…The aim of this paper is to study optimal investment evaluated by a forward performance criterion in a stochastic factor market model, in which the probability measure that models future stock price evolutions is ambiguous. The forward performance process, as an adapted stochastic dynamic utility evolving forward in time, has been introduced and developed in [41]- [45] (see also [24] and [55], and more recently [2], [3], [6], [12], [23], [28], [33], [37], [39] and [51]). This new concept differs from the classical expected utility function, in which the objective is to solve a stochastic control problem in a backward way via dynamic programming principle.…”
Section: Introductionmentioning
confidence: 99%
“…A key feature is that the Hamiltonian H(t, x, p) is convex and coercive in p. In particular, this covers the case that H has quadratic growth in p, a case that corresponds to a rich class of equations in mathematical finance arising, for example, in optimal investment with homothetic risk preferences ( [18]), exponential indifference valuation ( [8,16,17]) and entropic risk measures ( [9]), just to name a few. Note that if u < f in Q T , equation (1.1) reduces exactly to the semilinear parabolic PDE considered in [19]:…”
Section: Introductionmentioning
confidence: 99%