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arXiv research

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.

169,341 papers · 148 categories

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3570105140 · Jun 202019922001200920182026
48 results for model-based price

RL agent outperforms model-based approach in detecting price manipulation.

problem Detecting and exploiting price manipulation opportunities.
method Compared model-free RL with model-based approach in a market with Almgren-Chriss framework.
result RL consistently outperforms model-based approach, especially with noisy parameter estimates.

Clarifies conflicting results on hedging American options under model uncertainty.

problem Conflicting results on the cost of the cheapest super-replicating strategy for American options.
method Shows that Bayraktar et al. do not search over a sufficiently rich class of models.
result The cost of the cheapest super-replicating strategy can strictly exceed the highest model-based price under model uncertainty.

The paper develops a model using risk-neutral pricing for financial decision-making.

problem Developing a representative agent model for financial decision-making.
method The approach involves using a pricing kernel that is transition independent, solving the eigenpair problem of a second-order differential operator, and finding a one-parameter family of eigenpairs.
result The paper finds a representative agent model derived from the eigenpairs, providing a necessary and sufficient condition for their existence.

This research predicts Bitcoin prices using wavelet and deep stacking approach.

problem Predicting price fluctuations of Bitcoin due to its price volatility.
method Wavelet for noise removal, deep learning models (neural networks and transformers), feature selection.
result The model achieved high accuracy in predicting Bitcoin prices at different time intervals.

The use of kinetic modelling based on partial differential equations for the dynamics of stock price formation in financial markets is briefly reviewed. The importance of behavioral aspects in market booms and crashes and the role of agents' heterogeneity in emerging power laws for price distributions is emphasized and…

2010-07-09abs ↗pdf ↗

Study finds adding more information to robust option pricing does not improve bounds.

problem Exploring robust pricing of financial claims using minimal assumptions.
method Empirical study of variance options, incorporating intermediate market data.
result Incorporating more information does not improve robust pricing bounds.

In this paper we propose a closed-form approximation for the price of basket options under a multivariate Black-Scholes model, based on Taylor expansions and the calculation of mixed exponential-power moments of a Gaussian distribution. Our numerical results show that a second order expansion provides accurate prices o…

2014-04-11abs ↗pdf ↗

New models optimize quotes for automated market makers considering various price dynamics and demand variability.

problem Optimizing quotes for automated market makers in volatile price environments.
method Advanced models incorporating stochastic volatility, jumps, Hawkes processes, and Markov-modulated Poisson processes.
result Optimal quotes can be computed using numerical methods tailored to each model.

Paper uses RL to optimize insurance pricing on PCWs, improving efficiency and adaptability.

problem Optimizing pricing on price comparison websites while balancing competitiveness and profitability.
method Integrates model-based and model-free reinforcement learning methods for dynamic pricing.
result Hybrid RL agent outperforms existing methods in sample efficiency and cumulative reward.

Matrix approximation method for Bachelier option pricing and Greeks under stochastic volatility models

problem Computing option prices and Greeks for stochastic volatility models
method Matrix approximation using elementary linear algebra
result Option prices and Greeks computed for infinitely many strikes with a finite number of expectations

We develop an option pricing model based on a tug-of-war game. This two-player zero-sum stochastic differential game is formulated in the context of a multi-dimensional financial market. The issuer and the holder try to manipulate asset price processes in order to minimize and maximize the expected discounted reward. W…

2014-10-07abs ↗pdf ↗

The study compares on-chain option prices with a model and finds significant differences.

problem Measuring and comparing on-chain option prices with a model-based benchmark.
method Used a two-regime MS-AR-(GJR)-GARCH model to estimate volatility and GLS to compare prices.
result On-chain option prices are significantly higher than model-based benchmarks, especially for call options.

We introduce solvable stochastic dealer models, which can reproduce basic empirical laws of financial markets such as the power law of price change. Starting from the simplest model that is almost equivalent to a Poisson random noise generator, the model becomes fairly realistic by adding only two effects, the self-mod…

2008-09-02abs ↗pdf ↗

Modified model predicts stock price jumps using Twitter sentiment.

problem Predicting stock price jumps based on market sentiment.
method Modified Levy jump-diffusion model with memory from Twitter sentiment, optimized with UKF.
result Algorithm provides good performance in identifying asset return trends.

The Kelly Criterion is applied to prediction markets to analyze risk and return.

problem Mean beliefs in prediction markets often differ from actual prices.
method Logarithmic utility and Kullback-Leibler divergence are used to study risk and return adjustments.
result Misjudgment of bias and investment fraction affect portfolio growth rate.

New model improves option pricing with faster convergence and better generalization.

problem Improving classical option pricing models.
method Introducing a time value related decision function and proving a universal approximation theorem.
result The new decision function approximates on the entire domain of definition by neural networks.

Extends BBSM model to incorporate ESG ratings and path dynamics.

problem Price stock options considering historical market index dynamics and ESG ratings.
method Develops discrete, binary tree option pricing model under BBSM with ESG valuation.
result Model accurately fits stock price changes and European call option prices.

The paper introduces a new price model based on entropy that better fits high-frequency market data.

problem Understanding fair prices in high-frequency markets with bid-ask imbalance.
method A parametrized family of prices derived from the Maximum Entropy Principle, minimizing bias given volume imbalance.
result The model can generate higher kurtosis and heavy-tailed distributions compared to standard models.

Study proves duality in exotic option pricing under uncertain model and delayed information.

problem Pricing and hedging of multi-action exotic options under nondominated model uncertainty and delayed information.
method Reformulated superhedging problem as a European option problem, proving duality results.
result Superhedging price equals model-based price with future look-up power.

The paper solves a complex option pricing model using finite elements.

problem Risk-Adjusted Pricing Methodology (RAPM) Black-Scholes model with transaction costs.
method Spatial finite element models based on P1 and/or P2 elements, combined with a Crank-Nicolson-type temporal scheme.
result Results compare favorably with finite difference methods in the literature.

Paper provides a method to price electricity storage contracts using COS technique.

problem Valuation of electricity storage contracts considering physical and operational constraints.
method Uses Fourier-based COS method to price contracts based on stochastic polynomial process.
result The COS method accurately and efficiently prices electricity storage contracts.

Modeling price clustering in financial markets using discrete distributions.

problem Price clustering phenomenon in financial markets.
method Discrete price model based on mixture of double Poisson distributions with dynamic volatility and proportions.
result Higher instantaneous volatility weakens price clustering at ultra-high frequencies.

The paper proposes a class of financial market models which are based on inhomogeneous telegraph processes and jump diffusions with alternating volatilities. It is assumed that the jumps occur when the tendencies and volatilities are switching. We argue that such a model captures well the stock price dynamics under per…

2008-12-03abs ↗pdf ↗

Proposes a new model for simulating electricity prices and their correlation structure.

problem Simulating and understanding the complex dynamics of intraday electricity prices.
method Develops a multidimensional statistical model based on Poisson measures, estimating three key parameters.
result Demonstrates the model's effectiveness in battery valuation through dynamic programming.

Paper proposes a new method for probabilistic electricity price forecasting.

problem Accurate estimation of forecast uncertainties for optimal decision making.
method Implicit generative ensemble post-processing using an ensemble of point forecasting models.
result Method outperforms well-established model combination benchmarks.

The paper introduces a new insurance pricing model based on driving mileage.

problem Weak link between insurance premiums and mileage, leading to overdriving and accidents.
method Developed a Pay-As-You-Drive insurance pricing model using a counting process and non-homogeneous Poisson distribution.
result The model provides theoretical results for better insurance pricing based on driving behavior.

Local volatility model for commodity futures options with online calibration.

problem Valuation and calibration of options on commodity futures.
method Local volatility model, online calibration, model-based price adjustment, Tikhonov regularization.
result Improved option valuation and smile adherence through online calibration and regularization.

Model predicts price polarity of real estate properties using website information.

problem Predicting price polarity of real estate properties.
method Uses doc2vec and xgboost to learn correlations between price and text descriptions of properties.
result Text descriptions provide slightly higher accuracy than features alone.

The study analyzes pricing and hedging of STCDOs using an affine model with a catastrophic risk component.

problem Pricing and hedging of collateralized debt obligations (CDOs) with specific focus on mezzanine and equity tranches.
method Specified an affine two-factor model with a catastrophic risk component, estimated using QML and Kalman filter, derived variance-minimizing strategy, analyzed actual performance and simulated extreme loss scenarios.
result The variance-minimizing strategy is most effective for mezzanine tranches but fails for equity tranches.

Defines speculative bubbles in discrete-time models based on discounted stock price losing mass.

problem Characterizing speculative bubbles in discrete-time models.
method Introduces a new definition based on discounted stock price behavior and provides probabilistic characterizations.
result Speculative bubbles in discrete time are linked to solutions of a linear Volterra integral equation.

New method for super-hedging American options under model uncertainty.

problem Super-hedging American options in a market with dynamic and static trading strategies.
method Supremum over prices under randomized models, where European options are static and stocks are dynamic.
result Super-hedging price is the supremum of prices under randomized models.