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.
Model quantifies uncertainty's impact on European option prices.
Quantum methods model uncertain volatility in financial markets.
In this paper, we solve portfolio rebalancing problem when security returns are represented by uncertain variables considering transaction costs. The performance of the proposed model is studied using constant-proportion portfolio insurance (CPPI) as rebalancing strategy. Numerical results showed that uncertain paramet…
Bayesian filtering optimizes portfolio weights over time with uncertain parameters.
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…
A machine learning method optimizes portfolio and hedging under uncertain market parameters.
New model predicts dynamic volatility in uncertain financial markets.
The target of this paper is to consider model the risky asset price on the financial market under the Knightian uncertainty, and pricing the ask and bid prices of the uncertain risk. We use the nonlinear analysis tool, i.e., G-frame work [26], to construct the model of the risky asset price and bid-ask pricing for the …
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…
Paper uses ML for high-dimensional option pricing under uncertain volatility model.
Study on learning strategies in matching markets with uncertain preferences.
We study an optimal execution problem with uncertain market impact to derive a more realistic market model. We construct a discrete-time model as a value function for optimal execution. Market impact is formulated as the product of a deterministic part increasing with execution volume and a positive stochastic noise pa…
TIP-Search optimizes market prediction accuracy and timeliness under uncertain load.
A method for dynamic portfolio choice with uncertain parameters using Pontryagin projection.
We present a novel methodology for predicting future outcomes that uses small numbers of individuals participating in an imperfect information market. By determining their risk attitudes and performing a nonlinear aggregation of their predictions, we are able to assess the probability of the future outcome of an uncert…
Framework for games with uncertain parameters, ensuring no player can improve by changing strategy.
This paper is a continuation of Ishitani and Kato (2015), in which we derived a continuous-time value function corresponding to an optimal execution problem with uncertain market impact as the limit of a discrete-time value function. Here, we investigate some properties of the derived value function. In particular, we …
Novel pricing method for equity-indexed annuities under uncertain volatility and stochastic interest rate.
A machine learning approach to compute Black-Scholes prices with uncertain volatility.
The paper analyzes investment and consumption strategies under uncertain market conditions.
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;…
Optimizes investment under uncertain time horizons with non-concave utility.
Study validates SV models with jump component and long memory parameter, using robustness and sensitivity analysis.
This paper first describes a class of uncertain stochastic control systems with Markovian switching, and derives an Itô-Liu formula for Markov-modulated processes. And we characterize an optimal control law, which satisfies the generalized Hamilton-Jacobi-Bellman (HJB) equation with Markovian switching. Then, by using …
Paper optimizes financial trading strategies under uncertain market conditions.
This paper develops a learning framework for optimal strategies in multi-stage decentralized matching markets.
We study the Markowitz portfolio selection problem with unknown drift vector in the multidimensional framework. The prior belief on the uncertain expected rate of return is modeled by an arbitrary probability law, and a Bayesian approach from filtering theory is used to learn the posterior distribution about the drift …
Integrates estimation and optimization for uncertain parameters.
This paper improves learning uncertain Bayesian networks from incomplete data.
Study on stock trading model with uncertain market status, proving free boundaries and optimal strategies.
Risk is an inherent feature of agricultural production and marketing and accurate measurement of it helps inform more efficient use of resources. This paper examines three tail quantile-based risk measures applied to the estimation of extreme agricultural financial risk for corn and soybean production in the US: Value …
Study finds optimal retirement timing in uncertain wage scenarios.
This paper studies robust forward investment and consumption preferences within a zero-volatility context. Different from previous works, we consider an incomplete financial market model due to general investment portfolio constraints. We provide a new PDE characterization and a novel semi-explicit saddle-point constru…
We consider the problem of belief aggregation: given a group of individual agents with probabilistic beliefs over a set of uncertain events, formulate a sensible consensus or aggregate probability distribution over these events. Researchers have proposed many aggregation methods, although on the question of which is be…
In this paper, we propose an equilibrium pricing model in a dynamic multi-period stochastic framework with uncertain income streams. In an incomplete market, there exist two traded risky assets (e.g. stock/commodity and weather derivative) and a non-traded underlying (e.g. temperature). The risk preferences are of expo…
This paper deals with an optimal position management problem for a market maker who has to face uncertain customer order flows in an illiquid market, where the market maker's continuous trading incurs a stochastic linear price impact. Although the execution timing is uncertain, the market maker can also ask its OTC cou…
This paper analyzes the robust growth rate of leveraged ETFs under uncertain parameters.
This research predicts cryptocurrency price volatility using deep learning models.
New algorithms learn stable matchings from uncertain user preferences.
Study option pricing in sideways markets and target zones.
We employ the thermal optimal path method to explore both the long-term and short-term interaction patterns between the onshore CNY and offshore CNH exchange rates (2012-2015). For the daily data, the CNY and CNH exchange rates show a weak alternate lead-lag structure in most of the time periods. When CNY and CNH displ…
Adapts MBDOE for real-time parameter estimation in complex systems.
Study a market with uncertain informed traders, finding price impact depends on both asset value and informed trader count distribution.
Solves super-hedging for financial models with uncertain prices.
In the present paper, given an evolving mixture of probability densities, we define a candidate diffusion process whose marginal law follows the same evolution. We derive as a particular case a stochastic differential equation (SDE) admitting a unique strong solution and whose density evolves as a mixture of Gaussian d…
Unified framework predicts S&P500 index direction using transfer learning and causal graph.
Federated learning uses worst-case optimization to handle uncertain local data impacts.
We investigate financial markets under model risk caused by uncertain volatilities. For this purpose we consider a financial market that features volatility uncertainty. To have a mathematical consistent framework we use the notion of G-expectation and its corresponding G-Brownian motion recently introduced by Peng (20…