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.
New RL approach handles non-exponential discounting for sequential decisions.
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…
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…
Study optimal stopping for group with diverse discount rates using an attitude function.
Investment decisions shift earlier as patience decreases, with implications for pasting conditions.
The paper analyzes optimal dividend and capital injection strategies under time-inconsistent preferences.
We optimize discounts to maximize influence spread in social networks.
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…
This paper presents an algorithm for pricing perpetual American put options with asset-dependent discounting.
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…
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.
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…
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.
Study optimal stopping times for multi-dimensional processes with non-exponential discounting.
Lower discount factors act as a regularizer in RL, improving performance.
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.
New theory extends LQ control to non-exponential discount scenarios.
A firm with heterogeneous shareholders optimizes dividends under ambiguity aggregation.
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.
In this paper, we study a time-inconsistent consumption-investment problem with random endowments in a possibly incomplete market under general discount functions. We provide a necessary condition and a verification theorem for an open-loop equilibrium consumption-investment pair in terms of a coupled forward-backward …
Paper introduces non-linear discounting models for default compensation and climate valuation.
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…
Study uses FDA to analyze discount functions of different temperaments.
New algorithm reduces reinforcement learning regret to sqrt(T) without strong dynamics assumptions.
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…
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…
Empirical study on long-term discount rates using historical bond prices.
New Q-learning algorithm reduces sample complexity for large discount factors.
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…
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 …
New method uses PINNs to efficiently compute Gerber-Shiu functions.
Study analyzes how discounts affect train ticket purchases and rescheduling in Switzerland.
We demonstrate a limitation of discounted expected utility, a standard approach for representing the preference to risk when future cost is discounted. Specifically, we provide an example of the preference of a decision maker that appears to be rational but cannot be represented with any discounted expected utility. A …
Proves lower discount rates are needed for future losses.
There is an observed basis between repo discounting, implied from market repo rates, and bond discounting, stripped from the market prices of the underlying bonds. Here, this basis is explained as a convexity effect arising from the decorrelation between the discount rates for derivatives and bonds. Using a Hull-White …
Paper proposes an efficient RL algorithm for discounted MDPs using feature mapping.
Proposes a new framework for discount models.
Revises derivative pricing post financial crisis by defining a discount rate.