arXiv research

New method for pricing European options in rough LSV models.

We propose a novel and generic calibration technique for four-factor foreign-exchange hybrid local-stochastic volatility models with stochastic short rates. We build upon the particle method introduced by Guyon and Labordère [Nonlinear Option Pricing, Chapter 11, Chapman and Hall, 2013] and combine it with new variance…

Derives new equations for stochastic volatility models.

Derives new equations for volatility models and option pricing.

Paper studies particle method for LSV model calibration, proving convergence and error bounds.

New method reduces Monte Carlo error in option pricing and Greeks estimation.

The paper proposes a neural network method to calibrate LSV models without interpolation.

In this paper, we study a semi-martingale optimal transport problem and its application to the calibration of Local-Stochastic Volatility (LSV) models. Rather than considering the classical constraints on marginal distributions at initial and final time, we optimise our cost function given the prices of a finite number…

A new LSV model uses relative quantities for better trading and risk management.

Study short-maturity Asian option pricing in LSV models using large deviations theory.

Proves existence and uniqueness of calibrated LSV model.

Stochastic volatility (SV) and local stochastic volatility (LSV) processes can be used to model the evolution of various financial variables such as FX rates, stock prices, and so on. Considerable efforts have been devoted to pricing derivatives written on underliers governed by such processes. Many issues remain, thou…

Using classical Taylor series techniques, we develop a unified approach to pricing and implied volatility for European-style options in a general local-stochastic volatility setting. Our price approximations require only a normal CDF and our implied volatility approximations are fully explicit (ie, they require no spec…

Pricing and hedging exotic options using local stochastic volatility models drew a serious attention within the last decade, and nowadays became almost a standard approach to this problem. In this paper we show how this framework could be extended by adding to the model stochastic interest rates and correlated jumps in…

We present a novel Monte Carlo based LSV calibration algorithm that applies to all stochastic volatility models, including the non-Markovian rough volatility family. Our framework overcomes the limitations of the particle method proposed by Guyon and Henry-Labordère (2012) and theoretically guarantees a variance reduct…

This paper solves the inversion problem for jump processes using Markovian projections.

By Gyongy's theorem, a local and stochastic volatility (LSV) model is calibrated to the market prices of all European call options with positive maturities and strikes if its local volatility function is equal to the ratio of the Dupire local volatility function over the root conditional mean square of the stochastic v…

We consider an investor who seeks to maximize her expected utility derived from her terminal wealth relative to the maximum performance achieved over a fixed time horizon, and under a portfolio drawdown constraint, in a market with local stochastic volatility (LSV). In the absence of closed-form formulas for the value …

Explicitly constructed 3XOR instances hard for Sum-of-Squares hierarchy.

Study simulates Heston-type local stochastic volatility model using particle method.

BERT fine-tuning is unstable due to optimization issues, not forgetting or dataset size.

The paper introduces BCART models for aggregate claim amount, improving frequency-severity and joint modeling.

The paper uses model-based trees to create interpretable surrogate models for complex machine learning models.

Gauge Flow Models use a learnable Gauge Field in Generative Flow Models.

The study examines how model predictions hold up under model extensions.

Revises Bayesian model averaging for foundation models.

Paper introduces symmetric divergence link models for probability distributions.

New method to handle credit portfolio model uncertainties.

The paper tests stock return models and uses LSTM to predict stock returns.

Researchers review challenges in interpreting additive models, especially neural additive models.

CRS model improves ranking data modeling with theoretical guarantees.

Sigma models linked to Gross-Neveu models via quiver varieties.

Interpretable machine learning has become a strong competitor for traditional black-box models. However, the possible loss of the predictive performance for gaining interpretability is often inevitable, putting practitioners in a dilemma of choosing between high accuracy (black-box models) and interpretability (interpr…

Simple models are preferred over complex models, but over-simplistic models could lead to erroneous interpretations. The classical approach is to start with a simple model, whose shortcomings are assessed in residual-based model diagnostics. Eventually, one increases the complexity of this initial overly simple model a…

Matryoshka hides secret models in a carrier model, achieving high capacity and robustness.

Seq2Seq models speed up epidemic model predictions.

This work develops scalable model selection methods with fast update and selection.

Paper proposes BMPO to optimize policies using bidirectional models.

Copulas outperform marginal models in multivariate risk forecasting, reducing model risk by narrowing down the set of models.

BayesBlend blends multiple models' predictions for better insurance loss predictions.

The paper identifies when larger models improve predictions and proposes a switcher model.

Reduced-order models that accurately abstract high fidelity models and enable faster simulation is vital for real-time, model-based diagnosis applications. In this paper, we outline a novel hybrid modeling approach that combines machine learning inspired models and physics-based models to generate reduced-order models …

Improved diffusion model generation speed with speculative sampling.

We propose a generalization of neural network sequence models. Instead of predicting one symbol at a time, our multi-scale model makes predictions over multiple, potentially overlapping multi-symbol tokens. A variation of the byte-pair encoding (BPE) compression algorithm is used to learn the dictionary of tokens that …

The paper extends statistical inference methods for black-box generative models.

PMM uses Bayesian inference to generate data from noisy approximations.

Unified model improves sampling speed and quality.

In science and especially in economics, agent-based modeling has become a widely used modeling approach. These models are often formulated as a large system of difference equations. In this study, we discuss two aspects, numerical modeling and the probabilistic description for two agent-based computational economic mar…