Generates financial time series with stylized facts using diffusion models.
problem Generating realistic synthetic financial time series with statistical properties like fat tails, volatility clustering, and seasonality.
method Utilizes denoising diffusion probabilistic models (DDPMs) with wavelet transformation to convert and generate financial time series.
result Demonstrates that the proposed approach satisfies stylized financial time series properties.
Unified framework for generating synthetic financial time series that accurately capture both marginal distributions and temporal dynamics.
problem Generating synthetic financial time series that reproduce both marginal distributions and temporal dynamics.
method SBBTS: A unified Schrödinger-Bass framework for synthetic financial time series.
result SBBTS accurately recovers stochastic volatility and correlation parameters that prior methods fail to capture.
This paper uses deep generative models to create synthetic financial data for portfolio and risk modeling.
problem Challenges in empirical research due to privacy, accessibility, and reproducibility issues in financial data.
method Investigates the use of Time-series Generative Adversarial Networks (TimeGAN) and Variational Autoencoders (VAEs) to generate synthetic financial return series.
result Synthetic data from TimeGAN closely mimics real financial data in distributional shapes, volatility, and autocorrelation.
A TTA framework improves forecasting accuracy in non-stationary time series.
problem Improving forecasting accuracy in non-stationary time series.
method Normalization-based test-time adaptation for causal timeseries forecasting and direction classification.
result Normalization-based TTA improves forecasting error in synthetic gradual drift and can even hurt in aggressive norm-only adaptation in financial markets.
We discuss the origin of multiscaling in financial time-series and investigate how to best quantify it. Our methodology consists in separating the different sources of measured multifractality by analysing the multi/uni-scaling behaviour of synthetic time-series with known properties. We use the results from the synthe…
Proposes TNCM-VAE for generating causal financial time series.
problem Lack of causal reasoning in market generators.
method Combines VAE with structural causal models, enforcing causal constraints through DAGs and using causal Wasserstein distance.
result Superior performance in counterfactual probability estimation, L1 distances as low as 0.03-0.10.
CoFinDiff generates synthetic financial data capturing stylized facts and meeting specified conditions.
problem Limited data availability and difficulty in controlling synthetic financial data generation.
method Conditional diffusion model with cross-attention to incorporate conditions derived from price data.
result Synthetic data generated by CoFinDiff accurately meets specified conditions for trends and volatility.
Transformer models predict financial time series movements accurately.
problem Applying transformer models to financial time series prediction.
method Transformer architecture applied to synthetic and real S&P500 data.
result Transformer models predict financial time series movements accurately.
Generative model for financial time series using structured noise and signature learning.
problem Creating synthetic financial data to reflect real-world market dynamics.
method Structured noise, moving average model, signature transform, reinforcement learning.
result Model effectively captures key financial characteristics and outperforms existing methods.
Generative Adversarial Graph Neural Network (Sig-Graph GAN) models financial time series data.
problem Challenges in generating synthetic data for non-stationary financial time series.
method Integrates time-series signature, LSTM, and GNNs with visibility graph algorithm.
result Sig-Graph GAN outperforms baseline methods in replicating time series data distributions.
Synthetic data improves financial models without real data.
problem Lack of real financial data due to privacy and regulation.
method Application of synthetic data across various financial data types.
result Synthetic data enhances financial model accuracy and fairness.
TSFMs improve financial forecasting from diverse datasets.
problem Challenges in forecasting financial time series due to noisy, non-stationary, and heterogeneous data.
method Empirical study of TSFMs in global financial markets, evaluating zero-shot inference, fine-tuning, and pre-training from scratch.
result Pre-trained TSFMs on financial data achieve substantial forecasting and economic improvements, highlighting the value of domain-specific adaptation.
This study compares deep generative models to traditional methods for generating financial time series.
problem Generating realistic multivariate financial time series for risk management and portfolio optimization.
method Systematic comparison of deep generative models (DGMs) against state-of-the-art parametric models on synthetic and empirical data.
result Deep generative models outperform traditional parametric models in generating financial time series.
Quantum models generate financial time series with desired properties.
problem Generating synthetic financial data with temporal correlations.
method Quantum generative adversarial networks (QGANs) with quantum and classical components.
result QGANs can generate financial time series with matching distribution and temporal correlations.
Kronos improves financial time series analysis with a pre-trained model.
problem Limited application of large-scale models to financial candlestick data.
method Unified, scalable pre-training framework for financial K-line modeling.
result Kronos excels in financial tasks like price forecasting and volatility prediction.
ReGEN-TAD detects anomalies in financial time series with interpretable models.
problem Detecting anomalies in complex financial time series with high-dimensional data.
method Integrates machine learning with econometric diagnostics in a refined convolutional--transformer architecture.
result Unified anomaly score without labeled data, robust to structured deviations.
TC-VAE generates robust financial time series data with causal constraints.
problem Generating realistic financial time series data with causal relationships.
method TC-VAE with causality constraint, RealNVP prior, and Wasserstein distance.
result TC-VAE loss controls discrepancy between market and generated distributions.
CTBench benchmarks cryptocurrency time series generation for trading applications.
problem Lack of comprehensive benchmarks for cryptocurrency time series generation.
method Developed a comprehensive benchmark extsf{CTBench} with 13 metrics across 5 dimensions.
result Uncovered trade-offs between statistical fidelity and real-world profitability.
SFAG generates realistic financial data that passes trading tests.
problem Financial generative models often produce unrealistic and unstable trading outcomes.
method Introduces SFAG, a GAN variant that aligns stylized facts and optimizes with adversarial loss.
result SFAG generates synthetic data that preserves stylized facts and supports robust trading strategies.
sWk-means clusters multidimensional financial time series into distinct market regimes.
problem Classifying distinct market regimes in multidimensional financial time series.
method Approximated multidimensional Wasserstein distance as sliced Wasserstein distance for clustering.
result sWk-means successfully identifies distinct market regimes in real financial data.
We present a simple and general result that the sign of the variations or increments of uncorrelated times series are predictable with a remarkably high success probability of 75% for symmetric sign distributions. The origin of this paradoxical result is explained in details. We also present some tests on synthetic, fi…
WGAN-GP generates Bitcoin-like financial time series.
problem Challenges in modeling financial time series due to volatility and overfitting.
method Adversarial training using LSTM architecture for deep generation.
result Generated Bitcoin-like time series are close to real data distribution but distinguishable.
Proposes a method to detect anomalies in financial time series using PCA and neural networks.
problem Anomalies in financial time series lead to miscalibrated risk models.
method Extract features using PCA, define anomaly score with neural network, calibrate cutoff value.
result The proposed PCA NN approach outperforms other anomaly detection methods.
New method uses randomised signatures for generating financial time series data.
problem Generating synthetic financial time series data accurately.
method Introduced a Wasserstein-type distance based on discrete-time randomised signatures.
result Demonstrated universal approximation for randomised signatures on continuous functions.
This paper reviews and compares deep generative models for financial time series and VaR.
problem Forecasting risk factor distribution in financial markets.
method Apply multiple deep generative models (CGAN, CWGAN, Diffusion, Signature WGAN) and propose new methods for conditional time series generation.
result Top performing models are Historical Simulation, GARCH, and CWGAN.
Hybrid model combines deep learning and agent-based methods for synthetic LOB generation.
problem Generating realistic financial time series data for model training.
method Combining TABL model with Chiarella model for intraday trading activity simulation.
result Hybrid model generates realistic price dynamics but fails to accurately recreate market microstructure.
R package otsfeatures analyzes ordinal time series data.
problem Lack of tools for analyzing ordinal time series data.
method Provides functions for extracting statistical features and performing inferential tasks.
result Package enables traditional machine learning tasks on ordinal time series.
Unified framework evaluates synthetic financial data models.
problem Data scarcity and confidentiality in finance hinder model development and testing.
method Multi-criteria evaluation of three generative paradigms: ARIMA-GARCH, VAEs, and TimeGAN.
result TimeGAN achieves the best balance between realism and temporal coherence.
Training deep learning models that generalize well to live deployment is a challenging problem in the financial markets. The challenge arises because of high dimensionality, limited observations, changing data distributions, and a low signal-to-noise ratio. High dimensionality can be dealt with using robust feature sel…
In this work, we propose a model for estimating volatility from financial time series, extending the non-Gaussian family of space-state models with exact marginal likelihood proposed by Gamerman, Santos and Franco (2013). On the literature there are models focused on estimating financial assets risk, however, most of t…
A new method is proposed to compute connectivity measures on multivariate time series with gaps. Rather than removing or filling the gaps, the rows of the joint data matrix containing empty entries are removed and the calculations are done on the remainder matrix. The method, called measure adapted gap removal (MAGR), …
Investigates chaotic financial time series with monthly contributions and devaluation.
problem Analyzing chaotic behavior in financial processes with piecewise contributions and negative interest rates.
method Examines a financial process with monthly contributions and devaluation, showing dichotomy in behavior.
result Financial time series exhibit either periodic sequences or Cantor set of ω-limit points, with chaotic behavior at points of a Cantor attractor.
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.
Detects lead-lag clusters in US equity market time series.
problem Identifying lead-lag relationships in multivariate time series.
method Directed network clustering of lead-lag relationships.
result Validated on US equity market data, detects statistically significant lead-lag clusters.
FinTSBridge evaluates financial time series models for asset pricing.
problem Lack of effective evaluation methods for financial time series models.
method Developed FinTSBridge suite with new metrics and tasks.
result Showcased new metrics for financial time series models.
Develops a flexible model for regime transitions in time series data.
problem Nonlinear and context-dependent regime transitions in time series data.
method Semi-parametric state-space model with learned transition functions.
result Improved recovery of nonlinear transition dynamics and earlier detection of regime changes.
Model financial time series with MOGP for imputation and prediction.
problem Impute missing financial data due to dependencies among multiple series.
method Use a multi-output Gaussian process (MOGP) with expressive covariance functions.
result The model outperforms other MOGPs and independent Gaussian process on real financial data.
This paper studies concept drift detectors for financial time series.
problem Improving accuracy on financial time series with concept drifts.
method Three simple concept drift detectors tailored to financial time series.
result Two of the detectors are as effective as state-of-the-art detectors.
GANs can learn stylized facts of financial time series, but performance varies by architecture.
problem Capturing stylized facts of financial time series using GANs.
method Examination of GANs' ability to learn stylized facts of financial time series, focusing on univariate and multivariate data.
result GANs can capture stylized facts of financial time series, but performance varies by architecture.
Paper proposes a new algorithm for clustering financial market regimes.
problem Rapid and automated detection of distinct market regimes.
method Wasserstein k-means algorithm for clustering financial time-series.
result Wasserstein k-means algorithm outperforms traditional clustering methods.
Quantum model generates financial data with fewer parameters.
problem Generating financial data with fewer parameters.
method Applied time-series quantum generative model to financial data.
result Fewer parameters required compared to classical methods.
Delphyne improves financial time series models with pre-trained language models.
problem Lack of financial data and negative transfer effect in existing time-series pre-trained models.
method Delphyne is a pre-trained model for financial time series that addresses the lack of financial data and negative transfer effect.
result Delphyne achieves competitive performance and superior performances on various financial tasks.
We investigate the local fractal properties of the financial time series based on the evolution of the Warsaw Stock Exchange Index (WIG) connected with the largest developing financial market in Europe. Calculating the local Hurst exponent for the WIG time series we find an interesting dependence between the behavior o…
ProteuS generates synthetic financial data with regime changes for testing drift detection.
problem Simulating concept drift in financial markets for model evaluation.
method ARMA-GARCH models fitted to ETF data, generating synthetic time series with predefined regime changes.
result Generated datasets reveal the complexity of detecting and adapting to market regime changes.
An analysis of the stylized facts in financial time series is carried out. We find that, instead of the heavy tails in asset return distributions, the slow decay behaviour in autocorrelation functions of absolute returns is actually directly related to the degree of clustering of large fluctuations within the financial…
Modeling financial time series with LSTM and trainable initial states.
problem Extracting patterns and information from financial time series.
method Long Short-Term Memory (LSTM) network with trainable initial hidden states.
result Model captures relative similarity and predicts future stock trends.
Develops a kernel for financial time series analysis.
problem Measuring similarity between evolving financial networks.
method Commute time matrix, dynamic time warping, Shannon entropy.
result Proposes a kernel for financial time series analysis.
Fine-tuning a time series model improves financial price prediction accuracy.
problem Improving accuracy in predicting financial market prices using large models.
method Continual pre-training of a time series foundation model on financial data to fine-tune its performance for price prediction.
result The fine-tuned model outperforms the baseline in various financial metrics.