Horizon-unbiased investment with ambiguity

Lin, Qian; Sun, Xianming; Zhou, Chao

doi:10.1016/j.jedc.2020.103896

Cited by 9 publications

(7 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2022), Ismail and Pham (2019), Liang and Ma (2020), Lin and Riedel (2021), Lin et al. (2020), Matoussi et al. (2015), Neufeld and Nutz (2018), Pham et al.…”

Section: Application To Portfolio Optimizationmentioning

confidence: 99%

Markov decision processes under model uncertainty

Neufeld¹,

Sester

Sikic

2023

Mathematical Finance

View full text Add to dashboard Cite

We introduce a general framework for Markov decision problems under model uncertainty in a discretetime infinite horizon setting. By providing a dynamic programming principle, we obtain a local-to-global paradigm, namely solving a local, that is, a one timestep robust optimization problem leads to an optimizer of the global (i.e., infinite time-steps) robust stochastic optimal control problem, as well as to a corresponding worst-case measure. Moreover, we apply this framework to portfolio optimization involving data of the 𝑆&𝑃 500.We present two different types of ambiguity sets; one is fully data-driven given by a Wasserstein-ball around the empirical measure, the second one is described by a parametric set of multivariate normal distributions, where the corresponding uncertainty sets of the parameters are estimated from the data. It turns out that in scenarios where the market is volatile or bearish, the optimal portfolio strategies from the corresponding robust optimization problem outperforms the ones without model uncertainty, showcasing the importance of taking model uncertainty into account.

show abstract

“…(2022), Ismail and Pham (2019), Liang and Ma (2020), Lin and Riedel (2021), Lin et al. (2020), Matoussi et al. (2015), Neufeld and Nutz (2018), Pham et al.…”

Section: Application To Portfolio Optimizationmentioning

confidence: 99%

Markov decision processes under model uncertainty

Neufeld¹,

Sester

Sikic

2023

Mathematical Finance

View full text Add to dashboard Cite

show abstract

“…On the other hand, model uncertainty has been a crucial aspect in classical optimal investment and consumption problems, as demonstrated in works such as [6,8,18,22,23,30,32,42,45,46]. In reality, agents often confront ambiguity regarding financial market model uncertainty.…”

Section: Introduction (π C)mentioning

confidence: 99%

“…In forward theory, Källblad et al in [27] initiated the study of the agent's robust forward investment preference (see Definition 1 below, with and being a non-singleton) and their optimal investment strategy, focusing on the dual representation of robust forward investment preference. However, relatively little is known about forward investment and consumption preferences with uncertainty parameters, with an exception found in [30].…”

Section: Introduction (π C)mentioning

confidence: 99%

Optimal investment and consumption with forward preferences and uncertain parameters

Chong,

Liang

2024

PUQR

View full text Add to dashboard Cite

We introduce and analyze a class of forward performance criteria in incomplete markets in the presence of model ambiguity. Incompleteness stems from general investment constraints, while model uncertainty is represented by a convex and compact set of plausible model parameter processes. Following the max-min criteria in traditional (backward) robust control, we formulate similar criteria for the robust forward performance processes and focus on the rich class of time-monotone processes. We provide a novel PDE characterization and a semi-explicit saddle-point construction of the robust forward performance criteria and their optimal policies. Furthermore, we present additional results within the class of homothetic constant relative risk aversion (CRRA) processes. Within this class, we investigate the relationship between forward performance processes on wealth and those on consumption, establishing an interesting dominance through time.

show abstract

“…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 Game Theoretical Approach to Homothetic Robust Forward Investment Performance Processes in Stochastic Factor Models

Li¹,

Liang

2020

SSRN Journal

View full text Add to dashboard Cite

This paper studies an optimal forward investment problem in an incomplete market with model uncertainty, in which the underlying stocks depend on the correlated stochastic factors. The uncertainty stems from the probability measure chosen by an investor to evaluate the performance. We obtain directly the representation of the homothetic robust forward performance processes in factor-form by combining the zero-sum stochastic differential game and ergodic BSDE approach. We also establish the connections with the risk-sensitive zero-sum stochastic differential games over an infinite horizon with ergodic payoff criteria, as well as with the classical robust expected utilities for long time horizons. Finally, we give an example to illustrate that our approach can be applied to address a type of robust forward investment performance processes with negative realization processes.

show abstract

Horizon-unbiased investment with ambiguity

Cited by 9 publications

References 55 publications

Markov decision processes under model uncertainty

Markov decision processes under model uncertainty

Optimal investment and consumption with forward preferences and uncertain parameters

A Game Theoretical Approach to Homothetic Robust Forward Investment Performance Processes in Stochastic Factor Models

Contact Info

Product

Resources

About