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.
The paper tackles robust control for insurance contracts under uncertain transition rates.
We use copulas to improve SLAM in uncertain environments.
Novel pricing method for equity-indexed annuities under uncertain volatility and stochastic interest rate.
This paper analyzes the robust growth rate of leveraged ETFs under uncertain parameters.
We present a representation for describing transition models in complex uncertain domains using relational rules. For any action, a rule selects a set of relevant objects and computes a distribution over properties of just those objects in the resulting state given their properties in the previous state. An iterative g…
Two-dimensional transition rates improve life insurance reserve calculations.
A novel approach models rating transitions using Lie groups and Deep Learning.
Neural models learn continuous-time Markov chain transition rates from data.
The idea of forward rates stems from interest rate theory. It has natural connotations to transition rates in multi-state models. The generalization from the forward mortality rate in a survival model to multi-state models is non-trivial and several definitions have been proposed. We establish a theoretical framework f…
In banking practice, rating transition matrices have become the standard approach of deriving multi-year probabilities of default (PDs) from one-year PDs, the latter normally being available from Basel ratings. Rating transition matrices have gained in importance with the newly adopted IFRS 9 accounting standard. Here,…
This paper studies the properties of the optimal portfolio-consumption strategies in a {finite horizon} robust utility maximization framework with different borrowing and lending rates. In particular, we allow for constraints on both investment and consumption strategies, and model uncertainty on both drift and volatil…
Incremental learning suffers from two challenging problems; forgetting of old knowledge and intransigence on learning new knowledge. Prediction by the model incrementally learned with a subset of the dataset are thus uncertain and the uncertainty accumulates through the tasks by knowledge transfer. To prevent overfitti…
We present a continuous-time maximum likelihood estimation methodology for credit rating transition probabilities, taking into account the presence of censored data. We perform rolling estimates of the transition matrices with exponential time weighting with varying horizons and discuss the underlying dynamics of trans…
The paper uses filtering techniques to predict rating transitions.
Tabular Q-learning outperforms advanced RL methods in monetary policy.
Study optimal timing to divest from assets with uncertain future scenarios.
The paper uses machine learning and Lie groups to improve rating transitions and XVA calculations.
We consider the problem of estimating the transition rate matrix of a continuous-time Markov chain from a finite-duration realisation of this process. We approach this problem in an imprecise probabilistic framework, using a set of prior distributions on the unknown transition rate matrix. The resulting estimator is a …
Study on learning strategies in matching markets with uncertain preferences.
The paper develops ML algorithms for calibrating credit rating transition models for high and low default portfolios.
New CVs preserve transition rates in molecular dynamics.
Risk management is an important practice in the banking industry. In this paper we develop a new methodology to estimate and predict the probability of default (PD) based on the rating transition matrices, which relates the rating transition matrices to the macroeconomic variables. Our method can overcome the shortcomi…
We study the problem of approximate ranking from observations of pairwise interactions. The goal is to estimate the underlying ranks of objects from data through interactions of comparison or collaboration. Under a general framework of approximate ranking models, we characterize the exact optimal statistical error …
New models for short rates show longer periods at higher rates.
Study option pricing in sideways markets and target zones.
ISOKANN learns collective variables and effective dynamics for metastable transitions.
Study analyzes optimal execution under uncertain volatility and liquidity.
We study the point of transition between complete and incomplete financial models thanks to Dirichlet Forms methods. We apply recent techniques, developped by Bouleau, to hedging procedures in order to perturbate parameters and stochastic processes, in the case of a volatility parameter fixed but uncertain for traders;…
New learning rate approach reveals phase transitions in SGD performance.
Catapult phase in neural nets shows exponential loss growth before quick decrease.
Study preferences over uncertain time payments, finds growth-optimality better than expected utility theory.
Method determines credit transition matrix from cumulative default probabilities.
The paper explores how to handle uncertain evidence in probabilistic models.
New method calculates Shapley values for uncertain functions.
New algorithm solves uncertain Markov decision processes using Wasserstein uncertainty.
Although many successful ensemble clustering approaches have been developed in recent years, there are still two limitations to most of the existing approaches. First, they mostly overlook the issue of uncertain links, which may mislead the overall consensus process. Second, they generally lack the ability to incorpora…
Graph diffusion processes approximate manifold heat semigroups using graph transition matrices.
We propose a Markov chain model for credit rating changes. We do not use any distributional assumptions on the asset values of the rated companies but directly model the rating transitions process. The parameters of the model are estimated by a maximum likelihood approach using historical rating transitions and heurist…
We establish minimax optimal rates of convergence for estimation in a high dimensional additive model assuming that it is approximately sparse. Our results reveal an interesting phase transition behavior universal to this class of high dimensional problems. In the {\it sparse regime} when the components are sufficientl…
There are various parametric models for analyzing pairwise comparison data, including the Bradley-Terry-Luce (BTL) and Thurstone models, but their reliance on strong parametric assumptions is limiting. In this work, we study a flexible model for pairwise comparisons, under which the probabilities of outcomes are requir…
In this paper, within the framework of uncertainty theory, the valuation of equity warrants is investigated. Different from the methods of probability theory, the equity warrants pricing problem is solved by using the method of uncertain calculus. Based on the assumption that the firm price follows an uncertain differe…
Proposes DenoiseBid to correct uncertain CTR and CVR estimates for autobidding.
The paper models rating transitions and calibrates them to market data for XVA calculations.
We consider an interest rate model with log-normally distributed rates in the terminal measure in discrete time. Such models are used in financial practice as parametric versions of the Markov functional model, or as approximations to the log-normal Libor market model. We show that the model has two distinct regimes, a…
In most sampling algorithms, including Hamiltonian Monte Carlo, transition rates between states correspond to the probability of making a transition in a single time step, and are constrained to be less than or equal to 1. We derive a Hamiltonian Monte Carlo algorithm using a continuous time Markov jump process, and ar…
At the heart of technology transitions lie complex processes of social and industrial dynamics. The quantitative study of sustainability transitions requires modelling work, which necessitates a theory of technology substitution. Many, if not most, contemporary modelling approaches for future technology pathways overlo…
Quantum methods model uncertain volatility in financial markets.
New model predicts dynamic volatility in uncertain financial markets.