Efficiently calibrates SABR/LIBOR models to real market caplets and swaptions data.
problem Calibration of stochastic volatility models to real market data.
method Proposes a parallelized simulated annealing algorithm for multi-GPUs.
result Numerical results show advantages of using multi-GPUs for SABR/LIBOR model calibration.
Calibrates historical and implied correlations in energy markets.
problem Challenges in aligning historical correlations of futures contracts with implied volatility smiles.
method Multiplicative multi-factor Heath-Jarrow-Morton model combined with stochastic volatility from lifted Heston model, using Kemna-Vorst approximation and Fourier-based techniques.
result Remarkable joint historical and implied calibration fits on the German power market.
New method calibrates LV surfaces for exotic derivatives with smoother, more stable Greeks.
problem Challenges in LV calibration leading to spiky surfaces and unstable Greeks.
method Automatic local regression to pre-process market observables and smooth LV surfaces.
result Significantly smoother LV surfaces and greatly improved Greek stability with negligible additional cost.
The paper explores using machine learning for yield curve calibration in multiple markets.
problem Calibration challenges in multiple yield curve markets.
method Gaussian process regression and Adam optimizer.
result Good results for single curve markets, but many challenges for multi curve markets.
New method calibrates crypto option prices more robustly.
problem Large bid-ask spreads and missing quotes in crypto markets.
method Designs a novel calibration procedure for crypto options.
result Calibration is more robust and accurate than standard methods.
Improved financial market calibration reveals large excess volatility.
problem Large excess volatility in financial markets.
method Extended Chiarella model to handle long-term value drifts, calibrated on multiple asset classes.
result Large excess volatility (factor ≈ 4 for stock indices) and bimodal mispricing distribution.
Paper proposes method to calibrate market simulator for various scenarios.
problem Calibrate market simulator to represent different market conditions.
method Two-step method using GAN with self-attention to train discriminator and optimize simulator parameters.
result Demonstrates effectiveness of method in capturing various market scenarios.
The paper develops a new model for rough volatility in commodity markets.
problem Calibration of rough volatility models for commodity futures prices.
method Developed a general rough volatility model with automatic calibration and treatment of the Samuelson effect.
result Calibrated rBergomi and rHeston models to WTI Crude Oil futures options data.
This paper enhances uplift modeling for multi-treatment marketing campaigns.
problem Optimizing marketing strategies by selecting individuals likely to respond to different treatments.
method Leveraging score ranking and calibration techniques.
result Improves overall performance of marketing campaigns.
Proposes efficient calibration method for LIBOR Market Model with stochastic volatility.
problem Calibrating LIBOR Market Model with stochastic volatility.
method Derives analytical gradient of swaptions prices for DDSVLMM and uses it for gradient-based optimization.
result Analytical gradient-based calibration is highly competitive and efficient for DDSVLMM.
We present a method for constructing the log-optimal portfolio using the well-calibrated forecasts of market values. Dawid's notion of calibration and the Blackwell approachability theorem are used for computing well-calibrated forecasts. We select a portfolio using this "artificial" probability distribution of market …
Method calibrates basket options using rearranged samples from constituent processes.
problem Calibrate basket options with non-linear dependency structure.
method Propose a method to extract dependency structure from market data through systematic sampling rearrangement, then calibrate a local volatility model.
result Efficiently calibrates basket options with near-perfect accuracy.
Paper proposes a new method to simulate realistic markets from data.
problem Lack of accurate market simulators leading to misleading conclusions.
method Proposes a world agent model trained on historical data without agent calibration.
result Models consistently outperform previous methods in realism and responsiveness.
This paper tackles non-identifiability in financial market simulations using multivariate time series data.
problem Non-identifiability issue in social simulation models, leading to indistinguishable simulated time series data.
method Proposes a maximization-based aggregation function to form a new calibration objective function using multiple time series features.
result Significant improvements in alleviating non-identifiability and achieving higher simulation fidelity.
Model accurately calibrates FX market skew for exotic options.
problem Inconsistent prices from different models for FX derivatives.
method Fully parameterized local volatility model with numerical methods.
result Model provides reliable prices for daily trading.
Deep learning calibrates HJM forward curves for commodity options pricing.
problem Calibrating HJM forward curves for accurate option pricing in commodity markets.
method Introduced a neural network to approximate true option prices from model parameters, calibrated using observed option prices.
result Neural network calibration yields high accuracy in recovering option prices, even with model parameter approximation loss.
New method calibrates financial market simulators using neural networks.
problem Calibrating market simulators to specific trading periods.
method Neural density estimators and embedding networks.
result Approach accurately identifies high-probability parameter sets.
We present a stochastic-local volatility model for derivative contracts on commodity futures able to describe forward-curve and smile dynamics with a fast calibration to liquid market quotes. A parsimonious parametrization is introduced to deal with the limited number of options quoted in the market. Cleared commodity …
Researchers calibrate an adaptive Farmer-Joshi model to recover stylized facts in financial markets.
problem Recovering stylized facts in financial markets using the Farmer-Joshi model.
method Calibrated an adaptive Farmer-Joshi model using genetic and Nelder-Mead algorithms, incorporating agent adaptation.
result The adaptive model recovers additional stylized facts, including auto-correlations and kurtosis, compared to the original model.
Marketron model extended to option markets, solving incomplete market challenges.
problem Tackling the challenge of incomplete markets in option pricing.
method Utility-based pricing approach, dual solution of optimal investment problem, Hamilton-Jacobi-Bellman (HJB) equation, novel calibration method.
result The Marketron model calibrated to option markets can reproduce statistical properties of underlying asset's log-returns.
Survey of Optimal Transport for model calibration.
problem Model calibration using Optimal Transport.
method General framework and numerical algorithms for various models.
result Calibration of volatility models and path-dependent options.
We present a universal algorithm for online trading in Stock Market which performs asymptotically at least as good as any stationary trading strategy that computes the investment at each step using a fixed function of the side information that belongs to a given RKHS (Reproducing Kernel Hilbert Space). Using a universa…
This paper improves risk control for financial markets by calibrating VaR forecasts using conformal methods.
problem Nonstationary and regime-dependent losses in financial markets.
method Regime-weighted conformal risk control (RWC) for VaR forecasting.
result RWC improves regime-conditional stability in some settings with modest conservativeness changes.
WamOL uses PINNs to efficiently calibrate IVS from sparse data.
problem Calibrating time-dependent IVS from sparse market data.
method Physics-Informed Neural Networks (PINNs) with adaptive reweighting.
result WamOL outperforms in calibrating intraday IVS from uneven data.
The study calibrates VIX and VXX options using a multi-factor model.
problem Calibration failure of VIX and VXX options using stochastic or local volatility models.
method Presented a multi-factor stochastic-local volatility model.
result Joint calibration of VIX and VXX options successfully achieved.
New model predicts stock performance in large equity markets.
problem Predicting stock performance in large equity markets over long time horizons.
method Rank-based volatility stabilized models calibrated to empirical data.
result The model exhibits relative arbitrage and statistically fits empirical features.
In this paper we calibrate chaotic models for interest rates to market data using a polynomial-exponential parametrization for the chaos coefficients. We identify a subclass of one-variable models that allow us to introduce complexity from higher order chaos in a controlled way while retaining considerable analytic tra…
Simulates multi-asset spot and option markets using normalizing flows.
problem High-dimensionality of market call prices and dynamic preservation across simulators.
method Normalizing flows for efficient low-dimensional representations, conditional invertibility for joint distribution calibration.
result Calibrated simulators maintain dynamics of each underlying and accurately represent market call prices.
Paper proposes real-time VaR estimation using quantile regression forest with conformal calibration.
problem Real-time estimation of Value at Risk (VaR) in rapidly changing markets.
method Quantile regression forest trained offline, real-time VaR estimates via observed risk factors, conformalized estimator for reliability.
result The proposed method provides reliable real-time VaR estimates.
The paper proposes a new method to calibrate option pricing models that accurately match both volatility surfaces and variance term structures.
problem Calibrated models often produce inaccurate variance term structures relative to market observations.
method The paper introduces a joint calibration framework that augments the conventional objective function with a penalty term for variance term structure deviations, using a hyperparameter to balance volatility surface and variance term structure weights.
result The proposed method accurately fits observed option prices while delivering realistic term structures of variance.
Space mapping calibrates financial models, shown feasible for Heston model.
problem Calibrating financial models with few observable parameters and non-linear constraints.
method Space mapping approach using a coarse surrogate model and fine model calibration.
result Space mapping approach feasible for Heston model calibration.
This paper proposes a parametric approach for stochastic modeling of limit order markets. The models are obtained by augmenting classical perfectly liquid market models by few additional risk factors that describe liquidity properties of the order book. The resulting models are easy to calibrate and to analyze using st…
Develops a diagnostic framework for interest rate model calibration, showing equivalence to Weighted Least Squares and revealing boundary-dominated leverage and local parameter instability.
problem Calibration of stochastic interest rate models
method Diagnostic framework using non-linear regression and analytical tractability of At-The-Money caps
result Reveals boundary-dominated leverage and local parameter instability
A new unsupervised learning method calibrates rough volatility models efficiently.
problem Efficient calibration of rough volatility models with minimal data.
method Unsupervised learning using BSDE representation and neural networks.
result The proposed scheme minimizes loss and approximates BSDE solution.
Calibrates Hawkes models for market events, revealing power-law feedback kernels.
problem Estimating the influence of past events and price changes on future market events.
method Proposes a calibration procedure for Quadratic Hawkes models, analyzing the kernel components.
result Empirically calibrated kernel components reveal power-law behavior, suggesting system near critical point.
The paper calibrates a model to market quotes efficiently and arbitrage-free.
problem Calibrating a model to market option quotes efficiently and without arbitrage.
method Piecewise-linear local variance function for efficient calibration.
result Arbitrage-free interpolation of class C2 achieved under one millisecond. A fast calibration method for rough volatility models with jumps.
problem Calibrating stochastic volatility models to market data efficiently.
method Structure-preserving approach: split pricing formula, precompute data-independent integrals, and approximate market-dependent remainder with neural networks.
result Calibration achieves high accuracy and speed, and a pure-jump rough volatility model adequately captures VIX dynamics.
Following closely the construction of the Schrodinger bridge, we build a new class of Stochastic Volatility Models exactly calibrated to market instruments such as for example Vanillas, options on realized variance or VIX options. These models differ strongly from the well-known local stochastic volatility models, in p…
We introduce a novel multi-factor Heston-based stochastic volatility model, which is able to reproduce consistently typical multi-dimensional FX vanilla markets, while retaining the (semi)-analytical tractability typical of affine models and relying on a reasonable number of parameters. A successful joint calibration t…
Energy companies need efficient procedures to perform market calibration of stochastic models for commodities. If the Black framework is chosen for option pricing, the bottleneck of the market calibration is the computation of the variance of the asset. Energy commodities are commonly represented by multi-factor linear…
The Heston model optimizes portfolio management based on real market data.
problem Choosing between active and passive investment strategies.
method Calibrated Heston model to real stock market data.
result Passive strategy can outperform active strategy depending on market parameters.
The paper models rating transitions and calibrates them to market data for XVA calculations.
problem Calibrating rating models to both historical and market data for accurate XVA calculations.
method Modeling rating transitions as a Markov chain, calibrating to historical and market data, proposing a novel calibration procedure.
result Improved XVA scheme through better calibration of rating models.
Paper improves CDO calibration using Magnus Expansion and Deep Learning.
problem Calibrating CDO to iTraxx market data.
method Large basket approximation, SPDE, Magnus expansion, Deep Learning.
result Highly accurate calibration to market data.
Unified kernel for prediction markets reduces belief variance forecast error.
problem Lack of standardized tools for quoting and hedging belief risk in prediction markets.
method Logit jump-diffusion model with risk-neutral drift, calibration pipeline, and coherent derivative layer.
result Model reduces forecast error compared to diffusion-only and probability-space baselines.
We introduce a local volatility model for the valuation of options on commodity futures by using European vanilla option prices. The corresponding calibration problem is addressed within an online framework, allowing the use of multiple price surfaces. Since uncertainty in the observation of the underlying future price…
This paper applies Heath-Jarrow-Morton framework to energy markets for practical use.
problem Applying complex financial models to energy markets for practical use.
method Calibration by PCA, Monte Carlo simulations, derivatives pricing.
result Calibrated model accurately simulates European power and gas markets.
Price changes are induced by aggressive market orders in stock market. We introduce a bivariate marked Hawkes process to model aggressive market order arrivals at the microstructural level. The order arrival intensity is marked by an exogenous part and two endogenous processes reflecting the self-excitation and cross-e…
New framework allows selective removal of stale data in option calibration.
problem Inability to remove old data from calibrated option pricing models without full retraining.
method Introduces operator-theoretic Gauss-Newton framework for selective forgetting.
result Provides stability guarantees and perturbation bounds for selective data removal.