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.
Paper tackles uncertainty prediction for deep sequential regression.
Study optimizes financial strategies in markets with uncertain drift.
New approach to portfolio optimization shows entropy regularization is ineffective.
In this paper, we study term structure movements in the spirit of Heath, Jarrow, and Morton [Econometrica 60(1), 77-105] under volatility uncertainty. We model the instantaneous forward rate as a diffusion process driven by a G-Brownian motion. The G-Brownian motion represents the uncertainty about the volatility. With…
We study a problem of finding an optimal stopping strategy to liquidate an asset with unknown drift. Taking a Bayesian approach, we model the initial beliefs of an individual about the drift parameter by allowing an arbitrary probability distribution to characterise the uncertainty about the drift parameter. Filtering …
We give explicit solutions for utility maximization of terminal wealth problem in the presence of Knightian uncertainty in continuous time in a complete market. We assume there is uncertainty on both drift and volatility of the underlying stocks, which induce nonequivalent measures on canonical space o…
In practice, one must recognize the inevitable incompleteness of information while making decisions. In this paper, we consider the optimal redeeming problem of stock loans under a state of incomplete information presented by the uncertainty in the (bull or bear) trends of the underlying stock. This is called drift unc…
In this paper, we study the mean-variance portfolio selection problem under partial information with drift uncertainty. First we show that the market model is complete even in this case while the information is not complete and the drift is uncertain. Then, the optimal strategy based on partial information is derived, …
In this paper we investigate a utility maximization problem with drift uncertainty in a multivariate continuous-time Black-Scholes type financial market which may be incomplete. We impose a constraint on the admissible strategies that prevents a pure bond investment and we include uncertainty by means of ellipsoidal un…
Optimal liquidation of an asset with unknown constant drift and stochastic regime-switching volatility is studied. The uncertainty about the drift is represented by an arbitrary probability distribution; the stochastic volatility is modelled by -state Markov chain. Using filtering theory, an equivalent reformulation…
Study approximates worst-case stock trading under uncertainty, quantifying sensitivity.
This paper aims to make a new contribution to the study of lifetime ruin problem by considering investment in two hedge funds with high-watermark fees and drift uncertainty. Due to multi-dimensional performance fees that are charged whenever each fund profit exceeds its historical maximum, the value function is expecte…
This work introduces a bias-variance decomposition for proper scores, improving uncertainty estimation in predictive models.
New method detects concept drift in data streams with missing values.
The paper analyzes investment and consumption strategies under uncertain market conditions.
We study robust notions of good-deal hedging and valuation under combined uncertainty about the drifts and volatilities of asset prices. Good-deal bounds are determined by a subset of risk-neutral pricing measures such that not only opportunities for arbitrage are excluded but also deals that are too good, by restricti…
Investment strategy in uncertain markets improved by learning and risk-ambiguity preferences.
One important assumption underlying common classification models is the stationarity of the data. However, in real-world streaming applications, the data concept indicated by the joint distribution of feature and label is not stationary but drifting over time. Concept drift detection aims to detect such drifts and adap…
This paper solves a Bayes sequential impulse control problem for a diffusion, whose drift has an unobservable parameter with a change point. The partially-observed problem is reformulated into one with full observations, via a change of probability measure which removes the drift. The optimal impulse controls can be ex…
Generative model learns shape drift for quantifying domain uncertainty in hemodynamics.
Classifiers deployed in the real world operate in a dynamic environment, where the data distribution can change over time. These changes, referred to as concept drift, can cause the predictive performance of the classifier to drop over time, thereby making it obsolete. To be of any real use, these classifiers need to d…
DRIFT uses neural flows to replace distributional regression models.
Study finds cheapest possible payoff under ambiguity, linking to maxmin expected utility.
We study a robust portfolio optimization problem under model uncertainty for an investor with logarithmic or power utility. The uncertainty is specified by a set of possible Lévy triplets; that is, possible instantaneous drift, volatility and jump characteristics of the price process. We show that an optimal investment…
HawkesLLM models text generation with temporal influence, improving semantic alignment under limited memory.
The paper proposes a method for distribution-free prediction sets that adapt to unknown temporal changes.
Study confirms complex crypto market dynamics via non-linear potentials.
Paper develops a hybrid DNN approach for RUL prediction with adaptive drift.
Proposes FedPop for personalised federated learning with uncertainty quantification.
Improved growth strategies by incorporating stochastic factors in asset returns.
Develops PromptShift-CRC for drift-aware conformal risk control in foundation models under prompt and domain shift.
Efficient approach improves prediction calibration for domain shifts.
Study optimizes growth rate for investors with long-only constraints.
Study portfolio optimization with partial info and drawdown constraints using deep learning.
SDE-Net quantifies uncertainty in deep nets using stochastic dynamics.
In this paper we study the valuation problem of an insurance company by maximizing the expected discounted future dividend payments in a model with partial information that allows for a changing economic environment. The surplus process is modeled as a Brownian motion with drift. This drift depends on an underlying Mar…
Post-hoc calibration improves uncertainty under domain shift.
Bayesian Markowitz portfolio problem shows entropy regularization is ineffective.
Machine learning identifies melting points in thermocouples for automatic calibration.
Introduces Neural-Brownian Motion for modeling dynamics under learned uncertainty.
We consider classical Merton problem of terminal wealth maximization in finite horizon. We assume that the drift of the stock is following Ornstein-Uhlenbeck process and the volatility of it is following GARCH(1) process. In particular, both mean and volatility are unbounded. We assume that there is Knightian uncertain…
Motivated by the needs of online large-scale recommender systems, we specialize the decoupled extended Kalman filter (DEKF) to factorization models, including factorization machines, matrix and tensor factorization, and illustrate the effectiveness of the approach through numerical experiments on synthetic and on real-…
Study dynamic risk measures with distributional uncertainty using optimal transport.
The paper addresses pricing interest rate derivatives in markets with volatility uncertainty.
Develops a deterministic method to approximate NSDEs for better uncertainty quantification.
NP-PROV separates mean and variance spaces to improve function uncertainty.
We introduce the concept of virtual volatility. This simple but new measure shows how to quantify the uncertainty in the forecast of the drift component of a random walk. The virtual volatility also is a useful tool in understanding the stochastic process for a given portfolio. In particular, and as an example, we were…
We study time consistent dynamic pricing mechanisms of European contingent claims under uncertainty by using G framework introduced by Peng ([24]). We consider a financial market consisting of a riskless asset and a risky stock with price process modelled by a geometric generalized G-Brownian motion, which features the…