arXiv research

This paper improves MARL for networked systems through new protocols and discount factors.

This paper shows how forward rate interpolations are equivalent to discount factor interpolations in yield curve construction.

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.

Lower discount factors act as a regularizer in RL, improving performance.

Proposes a new framework for discount models.

The valuation process that economic agents undergo for investments with uncertain payoff typically depends on their statistical views on possible future outcomes, their attitudes toward risk, and, of course, the payoff structure itself. Yields vary across different investment opportunities and their interrelations are …

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…

Asset prices contain information about the probability distribution of future states and the stochastic discounting of those states as used by investors. To better understand the challenge in distinguishing investors' beliefs from risk-adjusted discounting, we use Perron-Frobenius Theory to isolate a positive martingal…

In an effort to better understand the different ways in which the discount factor affects the optimization process in reinforcement learning, we designed a set of experiments to study each effect in isolation. Our analysis reveals that the common perception that poor performance of low discount factors is caused by (to…

A study finds that only a few factors explain corporate bond risk, rendering extensive bond factor literature redundant.

New Q-learning algorithm reduces sample complexity for large discount factors.

UCBVI-γ algorithm minimizes regret in discounted MDPs.

By analysing the restrictions that ensure the existence of capital market equilibrium, we show that the coefficient of relative risk aversion and the subjective discount factor cannot be high simultaneously as they are supposed to be to make the standard asset pricing consistent with financial stylised facts.

Q-Learning overestimation bias influenced by learning rate, discount factor, and reward signal.

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…

The paper develops a new model for high-dimensional spatial arbitrage pricing.

Study reveals a hidden cost in derivatives markets through option-implied discount factors.

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…

Deep neural networks decompose SDF into linear and nonlinear components.

The paper reviews historical and modern approaches to asset pricing probability measures.

Study uses put-call parity to estimate cost of funding in equity derivatives markets.

The paper proposes a new SDF scaled by time-varying volatility from S&P 500 options.

MSFA clusters high-dimensional spatial data using spline-based covariance structures.

This paper proves existence of the long bond, long forward measure and long-term factorization of the stochastic discount factor (SDF) of Alvarez and Jermann (2005) and Hansen and Scheinkman (2009) in Heath-Jarrow-Morton (HJM) models in the function space framework of Filipovic (2001). A sufficient condition on the wei…

2024 saw Bitcoin ETF approval, offering regulated exposure.

This study prioritizes temporal resolution over spatial in energy systems models due to higher influence.

We show that different rates should be used for borrowing and discount rates, and that the risk-free rate should be used for discounting when assessing and comparing the cost of energy accross diffferent producers and technologies, on the example of photovoltaics. Recent quantitative models using the same rate for borr…

This paper considers the problem of consumption and investment in a financial market within a continuous time stochastic economy. The investor exhibits a change in the discount rate. The investment opportunities are a stock and a riskless account. The market coefficients and discount factor switch according to a finite…

Solves equity premium puzzle with time-varying variables.

Paper proposes an efficient RL algorithm for discounted MDPs using feature mapping.

In-sample overfitting is a drawback of any backtest-based investment strategy. It is thus of paramount importance to have an understanding of why and how the in-sample overfitting occurs. In this article we propose a simple framework that allows one to model and quantify in-sample PnL overfitting. This allows us to com…

MRTL learns interpretable spatial patterns efficiently.

Optimal online linear regression in dynamic environments using discounted Vovk-Azoury-Warmuth forecaster.

The objective of the present paper is to analyse various features of the Smith-Wilson method used for discounting under the EU regulation Solvency II, with special attention to hedging. In particular, we show that all key rate duration hedges of liabilities beyond the Last Liquid Point will be peculiar. Moreover, we sh…

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…

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…

We introduce and analyze a form of variance-reduced $Q$-learning. For $γ$-discounted MDPs with finite state space $\mathcal{X}$ and action space $\mathcal{U}$, we prove that it yields an $ε$-accurate estimate of the optimal $Q$-function in the $\ell_\infty$-norm using $\mathcal{O} \left(\left(\frac{D}{ ε^2 (1-γ)^3} \ri…

Solves the equity premium puzzle without calibrated values.

New findings reveal discount regularization can be seen as a strong prior, leading to poor performance in unevenly sampled data.

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…

New model solves equity premium puzzle with risk aversion coefficient.

New algorithm reduces online regression error in RKHS.

Unified framework linking firm signals and cross-asset spillovers for SDF estimation.

Smart app tracks relapse history and predicts relapse based on spatial-temporal factors.

Reward tweaking optimizes behavior for long-term goals by adjusting the reward function.

In this paper we study perpetual American call and put options in an exponential Lévy model. We consider a negative effective discount rate which arises in a number of financial applications including stock loans and real options, where the strike price can potentially grow at a higher rate than the original discount f…

DMSTF models spatio-temporal data with deep Markov priors.