A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study optimal portfolio strategies with time-varying discount rates.
Study time-inconsistent consumption-investment in incomplete markets with general discount functions.
In this paper, we study the dividend strategies for a shareholder with non-constant discount rate in a diffusion risk model. We assume that the dividends can only be paid at a bounded rate and restrict ourselves to the Markov strategies. This is a time inconsistent control problem. The extended HJB equation is given an…
New RL approach handles non-exponential discounting for sequential decisions.
The paper analyzes optimal dividend and capital injection strategies under time-inconsistent preferences.
We optimize discounts to maximize influence spread in social networks.
We consider a discounted reward control problem in continuous time stochastic environment where the discount rate might be an unbounded function of the control process. We provide a set of general assumptions to ensure that there exists a smooth classical solution to the corresponding HJB equation. Moreover, some verif…
Intertemporal decision making involves choices among options whose effects occur at different moments. These choices are influenced not only by the effect of rewards value perception at different moments, but also by the time perception effect. One of the main difficulties that affect standard experiments involving int…
Reinforcement learning (RL) typically defines a discount factor as part of the Markov Decision Process. The discount factor values future rewards by an exponential scheme that leads to theoretical convergence guarantees of the Bellman equation. However, evidence from psychology, economics and neuroscience suggests that…
Study optimal stopping for group with diverse discount rates using an attitude function.
Study optimal stopping times for multi-dimensional processes with non-exponential discounting.
A central problem in ranking is to design a ranking measure for evaluation of ranking functions. In this paper we study, from a theoretical perspective, the widely used Normalized Discounted Cumulative Gain (NDCG)-type ranking measures. Although there are extensive empirical studies of NDCG, little is known about its t…
Investment decisions shift earlier as patience decreases, with implications for pasting conditions.
New theory extends LQ control to non-exponential discount scenarios.
This paper presents an algorithm for pricing perpetual American put options with asset-dependent discounting.
In the "positive interest" models of Flesaker-Hughston, the nominal discount bond system is determined by a one-parameter family of positive martingales. In the present paper we extend this analysis to include a variety of distributions for the martingale family, parameterised by a function that determines the behaviou…
The study uses reproducing kernels to model bond discount curves.
New findings reveal discount regularization can be seen as a strong prior, leading to poor performance in unevenly sampled data.
New RL difficulty shown for discounted settings.
We consider in this paper a general two-sided jump-diffusion risk model that allows for risky investments as well as for correlation between the two Brownian motions driving insurance risk and investment return. We first introduce the model and then find the integro-differential equations satisfied by the Gerber-Shiu f…
New method uses PINNs to efficiently compute Gerber-Shiu functions.
In a continuous time stochastic economy, this paper considers the problem of consumption and investment in a financial market in which the representative investor exhibits a change in the discount rate. The investment opportunities are a stock and a riskless account. The market coefficients and discount factor switches…
The paper analyzes perpetual American options with asset-dependent discounting.
New algorithm reduces online regression error in RKHS.
Lower discount factors act as a regularizer in RL, improving performance.
Proposes a new framework for discount models.
In the spirit of [Surya07'], we develop an average problem approach to prove the optimality of threshold type strategies for optimal stopping of Lévy models with a continuous additive functional (CAF) discounting. Under spectrally negative models, we specialize this in terms of conditions on the reward function and ran…
N-discount optimality was introduced as a hierarchical form of policy- and value-function optimality, with Blackwell optimality lying at the top level of the hierarchy Veinott (1969); Blackwell (1962). We formalize notions of myopic discount factors, value functions and policies in terms of Blackwell optimality in MDPs…
Paper tackles time inconsistency in portfolio management with stochastic volatility and power utility.
For environmental problems such as global warming future costs must be balanced against present costs. This is traditionally done using an exponential function with a constant discount rate, which reduces the present value of future costs. The result is highly sensitive to the choice of discount rate and has generated …
A firm with heterogeneous shareholders optimizes dividends under ambiguity aggregation.
Paper proposes an efficient RL algorithm for discounted MDPs using feature mapping.
The policy gradient theorem describes the gradient of the expected discounted return with respect to an agent's policy parameters. However, most policy gradient methods drop the discount factor from the state distribution and therefore do not optimize the discounted objective. What do they optimize instead? This has be…
We consider an economic agent (a household or an insurance company) modelling its surplus process by a deterministic process or by a Brownian motion with drift. The goal is to maximise the expected discounted spendings/dividend payments, given that the discounting factor is given by an exponential CIR process. In the d…
Study on investment strategy for agents with periodic preferences and discounting.
Paper develops a discounted algorithm for online convex optimization that adapts to unknown discount factors.
Paper introduces non-linear discounting models for default compensation and climate valuation.
The well-known theorem of Dybvig, Ingersoll and Ross shows that the long zero-coupon rate can never fall. This result, which, although undoubtedly correct, has been regarded by many as surprising, stems from the implicit assumption that the long-term discount function has an exponential tail. We revisit the problem in …
NVMDP framework tackles non-stationary MDPs with varying discount rates.
Study improves dividend discount model using VAR process.
This paper considers an insurance surplus process modeled by a spectrally negative Lévy process. Instead of the time of ruin in the traditional setting, we apply the time of drawdown as the risk indicator in this paper. We study the joint distribution of the time of drawdown, the running maximum at drawdown, the last m…
We introduce and analyze a form of variance-reduced -learning. For -discounted MDPs with finite state space and action space , we prove that it yields an -accurate estimate of the optimal -function in the -norm using $\mathcal{O} \left(\left(\frac{D}{ ε^2 (1-γ)^3} \ri…
New algorithm for nonstationary GLBs reduces computation and memory costs.
We propose a model for the credit markets in which the random default times of bonds are assumed to be given as functions of one or more independent "market factors". Market participants are assumed to have partial information about each of the market factors, represented by the values of a set of market factor informa…
Study uses FDA to analyze discount functions of different temperaments.
New algorithm reduces reinforcement learning regret to sqrt(T) without strong dynamics assumptions.
We study an infinite-horizon discrete-time optimal stopping problem under non-exponential discounting. A new method, which we call the iterative approach, is developed to find subgame perfect Nash equilibria. When the discount function induces decreasing impatience, we establish the existence of an equilibrium through …
In many finite horizon episodic reinforcement learning (RL) settings, it is desirable to optimize for the undiscounted return - in settings like Atari, for instance, the goal is to collect the most points while staying alive in the long run. Yet, it may be difficult (or even intractable) mathematically to learn with th…