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 reveals a hidden cost in derivatives markets through option-implied discount factors.
Study shows physical drift affects put-call parity enforcement, not just option payoffs.
Paper defines conditions for feasible correlation matrices from factor structures.
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.
Investigates portfolio selection with transaction costs and stochastic volatility, using deep learning for computation.
Paper introduces non-linear discounting models for default compensation and climate valuation.
Currency volatility shocks predict lower excess returns, and buying weak transmitters outperforms selling strong ones.
Lower discount factors act as a regularizer in RL, improving performance.
Proposes a new framework for discount models.
Low-frequency historical data, high-frequency historical data and option data are three major sources, which can be used to forecast the underlying security's volatility. In this paper, we propose two econometric models, which integrate three information sources. In GARCH-Itô-OI model, we assume that the option-implied…
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…
This paper improves MARL for networked systems through new protocols and discount factors.
A study finds that only a few factors explain corporate bond risk, rendering extensive bond factor literature redundant.
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.
The paper assesses how equity tail risk impacts US Treasury bond returns.
Q-Learning overestimation bias influenced by learning rate, discount factor, and reward signal.
Prediction markets and crypto options show persistent pricing gaps.
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…
This paper provides a neural approach to represent option implied information.
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.
iCOS method estimates risk-neutral densities and option prices without model assumptions.
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.
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…
We derive a general multivariate theory for realised characteristics of `model-free discretisation-invariant swaps', so-called because the standard no-arbitrage assumption of martingale forward prices is sufficient to derive fair-value swap rates for such characteristics which have no jump or discretisation errors. Thi…
2024 saw Bitcoin ETF approval, offering regulated exposure.
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…
Sample complexity bounds are a common performance metric in the Reinforcement Learning literature. In the discounted cost, infinite horizon setting, all of the known bounds have a factor that is a polynomial in , where is the discount factor. For a large discount factor, these bounds seem to imply that …
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…
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 -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…
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.