Forging Financial Futures: A Guide to Synthetic Data

Author: Denis Avetisyan

As demand for financial modeling grows, researchers are increasingly turning to artificially generated data to overcome privacy concerns and data scarcity.

The analysis of both actual and simulated S&P 500 logarithmic returns demonstrates patterns within short-term market fluctuations, providing a basis for understanding and potentially modeling financial time series data using <span class="katex-eq" data-katex-display="false"> \log(returns) </span>. — The analysis of both actual and simulated S&P 500 logarithmic returns demonstrates patterns within short-term market fluctuations, providing a basis for understanding and potentially modeling financial time series data using $\log(returns)$ .

A comprehensive review compares statistical, latent-variable, and deep generative models – including TimeGAN and ARIMA-GARCH – for creating realistic and useful synthetic financial time series.

Despite increasing demand for robust financial models, data scarcity and privacy concerns often hinder development and rigorous testing. This need motivates ‘Synthetic Financial Data Generation for Enhanced Financial Modelling’, which presents a unified evaluation framework comparing statistical, latent-variable, and deep learning approaches to generating realistic financial time series. Empirical results demonstrate that TimeGAN achieves the best balance between fidelity and temporal coherence, outperforming both ARIMA-GARCH and Variational Autoencoders in downstream tasks like portfolio optimization and volatility forecasting. Given the trade-offs between model complexity, realism, and computational cost, how can practitioners best select synthetic data generation techniques tailored to their specific application needs?

Data Scarcity and the Pursuit of Fidelity

Financial modeling, crucial for risk assessment, algorithmic trading, and regulatory compliance, historically depends on granular, real-world transactional data. However, this reliance creates significant obstacles, as such data often contains personally identifiable information and is subject to stringent privacy regulations like GDPR and CCPA. Obtaining access requires navigating complex legal frameworks and establishing robust data usage agreements, a process that is both time-consuming and expensive. Furthermore, even anonymized datasets carry residual risks of re-identification, potentially leading to legal repercussions and reputational damage. These hurdles limit the scope of financial innovation and hinder the development of sophisticated models, particularly for emerging technologies where historical data is scarce or non-existent. The inherent challenges in accessing and utilizing real financial data have therefore spurred interest in alternative approaches, such as the generation of synthetic datasets.

The advancement of innovative financial algorithms and robust stress-testing capabilities is frequently constrained by a fundamental challenge: insufficient data. Developing and validating complex models – whether for fraud detection, algorithmic trading, or risk management – demands extensive datasets that accurately reflect real-world financial conditions. However, access to such data is often limited due to privacy concerns, regulatory restrictions, and the sheer cost of acquisition. This scarcity particularly impacts the testing of models under extreme, but plausible, market conditions – scenarios crucial for ensuring financial stability. Without the ability to simulate a diverse range of events, including rare ‘black swan’ occurrences, developers are left with incomplete assessments of model performance and potential vulnerabilities, ultimately hindering progress and potentially increasing systemic risk.

The promise of synthetic financial data as a workaround for privacy concerns and data scarcity rests heavily on its ability to faithfully replicate the statistical properties and temporal dependencies of real-world financial time series. Recent investigations reveal substantial performance differences between popular generative models in achieving this crucial fidelity. Specifically, TimeGAN, Variational Autoencoders (VAEs), and ARIMA-GARCH models exhibit varying degrees of success in preserving key characteristics such as volatility clustering, skewness, and autocorrelation. While certain models excel at capturing marginal distributions, maintaining realistic temporal dynamics – the way data evolves over time – proves to be a significant challenge. Consequently, the choice of generative model critically impacts the reliability of downstream analyses, including algorithmic backtesting, stress testing, and the development of novel financial instruments, highlighting the need for careful evaluation and model selection.

TimeGAN (red) and VAE (green) successfully replicate the <span class="katex-eq" data-katex-display="false">\sqrt{t}</span> scaling of rolling 30-day volatility observed in real market data (blue), demonstrating their capacity to model conditional heteroskedasticity. — TimeGAN (red) and VAE (green) successfully replicate the $\sqrt{t}$ scaling of rolling 30-day volatility observed in real market data (blue), demonstrating their capacity to model conditional heteroskedasticity.

Constructing Artificial Realities: Generative Approaches

ARIMA-GARCH models represent a traditional approach to time series generation, establishing a foundational benchmark against which newer techniques are measured. The Autoregressive Integrated Moving Average (ARIMA) component requires careful selection of its order – denoted as (p, d, q) – representing the number of autoregressive (AR), integrated (I), and moving average (MA) terms, respectively. This selection is typically informed by analysis of the time series’ autocorrelation and partial autocorrelation functions. To model volatility clustering – a common characteristic of financial time series – a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is frequently integrated, with the GARCH(1,1) configuration proving particularly effective due to its relative simplicity and ability to capture persistent volatility shocks. $\sigma_t^2 = \omega + \alpha \epsilon_{t-1}^2 + \beta \sigma_{t-1}^2$ where $\sigma_t^2$ represents the conditional variance at time t, ω is a constant, and α and β are coefficients representing the impact of past squared errors and past variance, respectively.

Variational Autoencoders (VAE) and Time Generative Adversarial Networks (TimeGAN) represent deep learning methodologies for synthetic data generation, offering advantages over traditional statistical techniques when dealing with complex, high-dimensional datasets. VAEs utilize encoder-decoder architectures to learn a latent representation of the input data, enabling the generation of new samples by decoding from this latent space. TimeGAN, specifically designed for time series data, employs a generative adversarial network where a generator network attempts to create synthetic time series data that mimics the real data distribution, while a discriminator network attempts to distinguish between the real and synthetic data. This adversarial process encourages the generator to produce increasingly realistic synthetic data. Both approaches require substantial computational resources and careful hyperparameter tuning, but can effectively capture intricate dependencies and patterns present in the original data.

Evaluation of deep learning models for synthetic data generation necessitates verification beyond simple plausibility; the generated data must be statistically indistinguishable from the original dataset. Our comparative analysis assessed Variational Autoencoders (VAE), Time Generative Adversarial Networks (TimeGAN), and Autoregressive Integrated Moving Average – Generalized Autoregressive Conditional Heteroskedasticity (ARIMA-GARCH) models using established metrics. Results indicate TimeGAN consistently outperformed both VAE and ARIMA-GARCH in replicating the statistical properties of the real data, demonstrating its superior ability to generate high-fidelity synthetic time series.

QQ-plots reveal that while both TimeGAN and VAE attempt to replicate the distribution of real data's normalized log-returns, deviations from the diagonal suggest discrepancies, particularly in the tails of the distributions. — QQ-plots reveal that while both TimeGAN and VAE attempt to replicate the distribution of real data’s normalized log-returns, deviations from the diagonal suggest discrepancies, particularly in the tails of the distributions.

Measuring the Illusion: Assessing Synthetic Data Quality

Distributional fidelity, a key metric for evaluating synthetic data, assesses the similarity between the distributions of real and synthetic datasets. This is commonly quantified using statistical tests such as the Maximum Mean Discrepancy (MMD) and the Kolmogorov-Smirnov (KS) test. MMD measures the distance between probability distributions in a reproducing kernel Hilbert space, while the KS test determines the maximum distance between the cumulative distribution functions of the two datasets. Dimensionality reduction techniques, specifically Principal Component Analysis (PCA), are often employed prior to these tests to reduce computational complexity and focus on the most significant features when comparing distributions in high-dimensional spaces. By quantifying distributional similarity, these methods help ensure the synthetic data accurately represents the characteristics of the original data.

Temporal coherence in synthetic financial data assesses whether the time-dependent relationships observed in real data are accurately replicated. Financial time series exhibit autocorrelation and other temporal dependencies; therefore, simply matching marginal distributions is insufficient. Verification requires evaluating the synthetic data’s ability to reproduce patterns like volatility clustering, seasonality, and lead-lag relationships between assets. Methods for assessing temporal coherence include analyzing autocorrelation functions, performing Granger causality tests to confirm predictive relationships, and visually inspecting time series plots for similar trends and cyclical behavior. Failure to maintain temporal coherence can lead to inaccurate model training and unreliable downstream analysis, as models may not generalize correctly to real-world market dynamics.

The ultimate validation of synthetic financial data lies in its downstream utility, specifically its capacity to reliably support model training and analytical processes. Evaluation metrics like Minimum Maximum Distance (MMD) and the Kolmogorov-Smirnov (KS) test assess distributional similarity, but practical performance is paramount. Recent results demonstrate that the TimeGAN model achieved the lowest MMD and KS values when compared to real data, indicating a high degree of statistical fidelity. Critically, portfolios constructed using this synthetic data exhibited a Sharpe Ratio most closely aligned with those generated from actual market data, suggesting its suitability for financial modeling and analysis where preserving performance characteristics is essential.

The autocorrelation function reveals both linear and volatility persistence in real and synthetic return sequences, with similar patterns confirming the synthetic data's temporal fidelity. — The autocorrelation function reveals both linear and volatility persistence in real and synthetic return sequences, with similar patterns confirming the synthetic data’s temporal fidelity.

The Promise and Peril of Simulated Realities

While synthetic financial data offers a compelling solution for overcoming data scarcity and privacy concerns, it is crucial to recognize that complete immunity from privacy leakage is not guaranteed. The generative models used to create this data, however sophisticated, can inadvertently memorize and reproduce patterns present in the original, sensitive datasets. Consequently, a thorough evaluation of potential disclosure risks is paramount before deploying synthetic data in real-world applications. This necessitates employing robust validation techniques, such as Membership Inference Attacks, to assess the degree to which individual records from the original data might be inferred from the synthetic counterpart, and proactively mitigating any identified vulnerabilities to ensure responsible and ethical data utilization.

The responsible implementation of synthetic financial data hinges on the diligent application of robust validation techniques and privacy-enhancing technologies. While synthetic datasets offer a compelling solution to data scarcity and privacy concerns, they are not inherently immune to information leakage; therefore, continuous monitoring and rigorous testing are crucial. Advanced methods, such as differential privacy and adversarial training, can be integrated into the data generation process to actively minimize the risk of revealing sensitive information about individuals present in the original datasets. Furthermore, comprehensive validation procedures, extending beyond simple statistical comparisons, are needed to assess the fidelity of the synthetic data and confirm that it accurately reflects the characteristics of the real data without compromising privacy guarantees. This proactive approach ensures that the benefits of synthetic data can be realized while upholding ethical standards and regulatory requirements.

Continued innovation in synthetic financial data relies on advancements across both generative modeling and rigorous evaluation. Current research emphasizes the development of more nuanced models capable of replicating complex financial dynamics while simultaneously safeguarding sensitive information. Notably, the TimeGAN model has emerged as a promising solution, achieving a Membership Inference Attack (MIA) accuracy of 51.1% – effectively indistinguishable from random guessing and indicating strong privacy protection. This performance is coupled with its superior accuracy in volatility forecasting, as measured by the lowest Root Mean Squared Error (RMSE) among tested models. Future efforts will likely concentrate on refining these generative approaches and creating more comprehensive metrics to assess not only data utility, but also the robustness of privacy safeguards, ultimately unlocking new applications of synthetic data in areas like fraud detection, algorithmic trading, and risk management.

Principal component analysis reveals that synthetic data closely aligns with real data when projected onto the subspace defined by the two most significant components <span class="katex-eq" data-katex-display="false"> \mathbb{R}^2 </span>. — Principal component analysis reveals that synthetic data closely aligns with real data when projected onto the subspace defined by the two most significant components $\mathbb{R}^2$ .

The pursuit of realistic synthetic financial data, as detailed in the study, echoes a fundamental tension between complexity and utility. The research highlights TimeGAN’s superior performance in mirroring real-world financial time series, yet acknowledges the increased computational burden. This mirrors the observation of Blaise Pascal: “The eloquence of the body is to move without speaking.” Just as elegant movement requires nuanced mechanics, so too does genuinely useful synthetic data demand sophisticated generative models. The study’s comparison of models-from the simplicity of ARIMA-GARCH to the depth of TimeGAN-demonstrates that increased realism often necessitates embracing complexity, even if it obscures immediate interpretability. The goal isn’t merely to generate data, but to create a simulation so convincing it moves-or, in this case, informs-without needing extensive explanation.

The Road Ahead

The pursuit of synthetic financial data, as demonstrated, inevitably reveals the core tension: fidelity versus parsimony. TimeGAN’s superior performance is not a triumph, but an acknowledgement of the inherent complexity of financial time series – a complexity which, perhaps, should not be celebrated. The model’s success merely postpones the question of what constitutes ‘realism’ and whether perfect replication is even desirable, or possible, given the fundamentally unpredictable nature of markets.

Future work must prioritize not just generating data like the original, but generating data that reveals the underlying structure, however chaotic. Simpler models, such as ARIMA-GARCH, offer interpretability, but at the cost of mirroring reality. The challenge lies in distilling the essence of financial behavior into a form both understandable and predictive. To achieve this, research should focus on identifying the minimal set of parameters needed to capture market dynamics, discarding the noise that masquerades as signal.

Ultimately, the value of synthetic data rests not in its ability to fool a statistical test, but in its capacity to illuminate the unseen. If a model requires more explanation than observation, its utility is questionable. The path forward is not toward greater complexity, but toward ruthless simplification – a search for the fundamental laws governing financial behavior, expressed in the fewest possible terms.

Original article: https://arxiv.org/pdf/2512.21791.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

Data Scarcity and the Pursuit of Fidelity

Constructing Artificial Realities: Generative Approaches

Measuring the Illusion: Assessing Synthetic Data Quality

The Promise and Peril of Simulated Realities

The Road Ahead

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