Calibrates hybrid LSV models with stochastic rates using particle method and control variates.
problem Calibrating complex foreign exchange models with stochastic volatility and stochastic rates.
method Combines particle method with variance reduction techniques and control variates.
result Accelerates convergence in calibration process for a wide class of hybrid LSV models.
A new LSV model uses relative quantities for better trading and risk management.
problem Inability to use intuitive and stable parameters in LSV models.
method Develops a hybrid method using relative quantities for efficient derivative pricing and scenario generation.
result Shows improved stability and ease of use for model parameters.
New method for pricing European options in rough LSV models.
problem Pricing European options in non-Markovian local stochastic volatility models.
method Conditional LSV dynamics, rough path theory, rough partial differential equations (RPDEs).
result Established a PDE pricing method for non-Markovian models.
New algorithm calibrates stochastic volatility models without errors.
problem Calibration errors in stochastic volatility models.
method Monte Carlo based LSV calibration algorithm for all models.
result Closed-form and exact calibration method with variance reduction.
The paper calibrates LSV models using optimal transport and convex optimisation.
problem Calibrating Local-Stochastic Volatility (LSV) models with European option prices.
method Optimal transport problem, convex optimisation, PDE formulation, Hamilton-Jacobi-Bellman equation.
result Numerical solution of dual problem yields calibrated LSV model parameters.
New neural operator calibrates LSV models faster and more accurately.
problem Calibrating LSV models is slow, noisy, and sequential.
method Developed a projection-consistent neural operator.
result Calibration latency reduced from 98.5 to 0.6 ms.
Derives new equations for stochastic volatility models.
problem Modeling local-stochastic-volatility models and their derivatives.
method Conditional forward equation, Dupire stochastic PDE, rolling expiry vanilla option SPDE.
result New equations for LSV models and their derivatives.
Derives new equations for volatility models and option pricing.
problem Modeling and pricing options in local-stochastic-volatility models.
method Develops conditional forward equations and Dupire stochastic PDEs.
result Derives new SPDE for vanilla options.
Paper studies particle method for LSV model calibration, proving convergence and error bounds.
problem Calibration of local-stochastic volatility models with open well-posedness question.
method Regularized Euler--Maruyama scheme for particle approximation of McKean--Vlasov dynamics.
result Strong convergence of the Euler--Maruyama scheme with rate 1/2 in step-size.
New method reduces Monte Carlo error in option pricing and Greeks estimation.
problem Reducing Monte Carlo error in option pricing and Greeks estimation.
method Denoised Monte Carlo technique for LSV models.
result Reduces Monte Carlo error by an order of magnitude.
The paper proposes a neural network method to calibrate LSV models without interpolation.
problem Calibrating LSV models with market option prices using neural networks.
method Parametrizing leverage function with neural networks and learning parameters from market prices; using deep hedging for variance reduction.
result The method accurately calibrates LSV models and outperforms interpolation methods.
Study short-maturity Asian option pricing in LSV models using large deviations theory.
problem Derive short-maturity asymptotics for Asian option prices in LSV models.
method Large deviations theory and novel expansion method.
result Explicit series expansions for the solution of the variational problem around the ATM point.
Proves existence and uniqueness of calibrated LSV model.
problem Calibrating a local stochastic volatility model to market data.
method Proves strong existence and uniqueness of solution to a McKean-Vlasov SDE.
result Establishes well-posedness of a calibrated two-factor 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…
This paper solves the inversion problem for jump processes using Markovian projections.
problem Calibrating jump-diffusion models with both local and stochastic features.
method Inverting Markovian projections for pure jump processes.
result Constructs calibrated local stochastic intensity (LSI) models for credit risk applications.
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…
Discussing hybrid models in Bayesian networks.
problem Improving accuracy in network modeling.
method Hybrid semiparametric Bayesian approach.
result Enhanced model performance in complex networks.
Expert augmentation improves hybrid model generalization.
problem Limited generalization of hybrid models outside training distribution.
method Introducing expert augmentation to improve hybrid model performance.
result Expert augmentation improves generalization of hybrid models.
Hybrid models forecast EPEC energy spot prices.
problem Forecasting energy spot prices in EPEC markets.
method Combining Naive, Fourier, ARMA/GARCH, mean-reversion, jump-diffusion, and RNN models.
result Improved accuracy in forecasting compared to individual models.
Hybrid models combine interpretable and complex models for better performance and control.
problem Improving model performance and user transparency in machine learning.
method Investigates hybrid models from theory, taxonomy, and methodological perspectives.
result Hybrid models can outperform standalone black boxes and provide precise control over transparency.
New method sparsifies hybrid neural ODEs for better performance and stability.
problem Excessive latent states and interactions from mechanistic models lead to training inefficiency and over-fitting.
method Automatic state selection and structure optimization combining domain-informed graph modifications with data-driven regularization.
result Improved predictive performance and robustness with desired sparsity.
Paper proposes a method to estimate scientific parameters in hybrid models without relying on model architecture.
problem Estimating unknown parameters in hybrid models combining machine learning and scientific models.
method Sharpness-aware minimization adapted for hybrid modeling, focusing on model simplicity.
result Demonstrates effectiveness of SAM-based hybrid model learning for scientific parameter estimation.
Hybrid model combines deep features with invertible transformations for accurate predictions and feature modeling.
problem Accurate prediction and feature modeling using deep and invertible transformations.
method Neural hybrid model with a linear model on features from a deep invertible transformation.
result Hybrid model achieves similar accuracy to purely predictive models while maintaining generative capabilities.
Investors optimize their portfolios to maximize utility under drawdown constraints and stochastic volatility.
problem Maximizing utility relative to maximum performance under drawdown constraints and stochastic volatility.
method Approximations through coefficient expansion and nonlinear transformations, numerically computed.
result Investors need a different portfolio strategy in stochastic volatility compared to constant volatility.
In the present paper, a fuzzy logic based method is combined with wavelet decomposition to develop a step-by-step dynamic hybrid model for the estimation of financial time series. Empirical tests on fuzzy regression, wavelet decomposition as well as the new hybrid model are conducted on the well known SP500 index fin…
Hybrid bootstrap improves model performance over dropout.
problem Improving predictive model performance through regularization.
method Resamples features from other training points instead of replacing them with zeros.
result Offers superior performance compared to dropout.
A hybrid ASR system using conformer architecture improves word-error-rate and training speed.
problem Improving word-error-rate and training efficiency for hybrid ASR systems.
method Used conformer architecture, applied time downsampling, and transposed convolutions.
result Conformer-based hybrid model achieves competitive results and significantly outperforms BLSTM-based hybrid model.
Bayesian hybrid models correct for missing physics in machine learning.
problem Systematic bias in machine learning models.
method Fusing physics-based insights with machine learning constructs, using Bayesian calibration and stochastic programming.
result Bayesian hybrid models outperform pure machine learning approaches with less data.
Hybrid model improves music source separation by 1.4 dB.
problem Improving music source separation accuracy.
method End-to-end hybrid spectrogram and waveform model, using model decision for domain choice.
result 1.4 dB improvement in Signal-to-Distortion (SDR) on MusDB HQ dataset.
This paper explores data science applications in economics using a taxonomy of models and hybrid models showing higher accuracy.
problem Investigating data science applications in economics.
method Systematic literature review using Prisma method.
result Hybrid models showed higher prediction accuracy than other algorithms.
A hybrid model for Bayesian optimization handles mixed variables using MCTS for categorical and GP for continuous.
problem Optimizing functions with mixed variable types (continuous, integer, categorical).
method Merges MCTS for categorical and GP for continuous variables, integrates UCTS search strategy, and dynamically selects kernels.
result Hybrid models outperform traditional methods in Bayesian optimization.
Hybrid models are reinterpreted as Neuro-Symbolic AI designs to quantify uncertainty and variability.
problem Limited semantic interface for comparing hybrid models across domains.
method Reinterpret hybrid models as Neuro-Symbolic AI, translating them into explicit inference function and logic-belief decomposition.
result Metrics SVR and BD quantify uncertainty and variability in hybrid models.
The paper introduces FMCI and hybrid decoding for hidden Markov models.
problem Computing distributions and decoding hidden state sequences in HMMs.
method Finite Markov chain imbedding (FMCI) and hybrid decoding.
result Hybrid decoding improves performance over traditional methods.
Study compares forecasting models for European financial markets and cryptocurrencies, finding hybrid ETS-ANN model best.
problem Challenges in predicting financial market fluctuations and cryptocurrency prices.
method Comparative analysis of ARIMA, hybrid ETS-ANN, and kNN models on European financial markets and cryptocurrency data.
result Hybrid ETS-ANN model performs best over extended periods, with moderate accuracy.
Hybrid model forecasts Bitcoin prices better than standard LSTM.
problem Forecasting Bitcoin price fluctuations.
method VMD for decomposition, LSTM for modeling IMFs, final prediction aggregation.
result Hybrid model outperforms standard LSTM in various metrics.
Hybrid models improve groundwater level prediction and uncertainty analysis.
problem Predicting and analyzing uncertainty of monthly groundwater levels.
method Six evolutionary optimization algorithms (GOA, CSO, WA, GA, KA, PSO) hybridized with ANFIS, ANN, and SVM.
result ANFIS-GOA outperformed other models in predicting groundwater levels.
Hybrid model combines VAR and neural network for OFI prediction.
problem Accurate prediction of Order Flow Imbalance (OFI) in high frequency trading.
method Combines Vector Auto Regression (VAR) and a simple feedforward neural network (FNN).
result Hybrid model achieves superior predictive accuracy compared to standalone models.
A new method for DPMM inference without truncation.
problem Inference in DPMM requires truncation, limiting model flexibility.
method Hybrid inference combining MCMC and variational methods.
result Efficient variational updates without increased model complexity.
Optimizes hybrid dividend strategies in dual models with periodic and continuous payments.
problem Determining the best dividend strategy in a dual model with periodic and continuous payments.
method Generalizes results from a Brownian model to a dual (spectrally positive Lévy) model, using the scale function.
result The optimal strategy is of the hybrid-barrier type and can be expressed using the scale function.
Study compares quantum and classical ML in crypto trading, finding hybrid models outperform.
problem Comparing quantum and classical machine learning in crypto trading strategies.
method Backtesting 10 models across multiple crypto assets using classical ML, quantum ML, hybrid models, and transformer models.
result Hybrid quantum models achieve superior performance with 13.99% return and 1.76 Sharpe ratio.
Hybrid model combines interpretable and black-box models for better transparency and performance.
problem Balancing interpretability and predictive performance in machine learning models.
method Proposes a Hybrid Predictive Model (HPM) integrating an interpretable model with a black-box model, using principled objective functions and customized training algorithms.
result Hybrid models achieve an efficient trade-off between transparency and predictive performance.
MES-LSTM hybrid method improves multivariate time series forecasting and mortality modeling.
problem Challenges in applying hybrid forecast methods to multivariate data.
method Generalized multivariate extension of ES-RNN, utilizing vectorized implementation.
result MES-LSTM shows significant improvement over pure statistical and deep learning methods in forecast accuracy and prediction interval construction.
New hybrid method combines ARIMA and ANN for better time series forecasting.
problem Improving forecasting accuracy of time series data.
method ARIMA-ANN hybrid method with empirical mode decomposition strategies.
result Our hybrid method outperforms traditional methods in forecasting accuracy.
Hybrid ASR systems can model graphemes effectively using chenones, outperforming traditional methods.
problem Traditional hybrid ASR systems struggle with English's poor grapheme-phoneme correspondence.
method Leveraging tied context-dependent graphemes (chenones) to model graphemes directly.
result Chenone-based systems significantly outperform senone baselines by 4.5% to 11.1% on English datasets.
Hybrid regularization avoids double descent in random feature models.
problem Avoiding the double descent phenomenon in random feature models.
method Combines early stopping and weight decay, using GCV for hyperparameter selection.
result Hybrid method successfully avoids double descent and achieves comparable generalization.
HyBO optimizes hybrid structures using diffusion kernels.
problem Optimizing complex interactions between discrete and continuous variables.
method HyBO uses diffusion kernels over hybrid spaces with additive kernel formulation.
result HyBO significantly outperforms state-of-the-art methods on real-world benchmarks.