Quant GANs: Deep Generation of Financial Time Series (1907.06673v2)

Abstract: Modeling financial time series by stochastic processes is a challenging task and a central area of research in financial mathematics. As an alternative, we introduce Quant GANs, a data-driven model which is inspired by the recent success of generative adversarial networks (GANs). Quant GANs consist of a generator and discriminator function, which utilize temporal convolutional networks (TCNs) and thereby achieve to capture long-range dependencies such as the presence of volatility clusters. The generator function is explicitly constructed such that the induced stochastic process allows a transition to its risk-neutral distribution. Our numerical results highlight that distributional properties for small and large lags are in an excellent agreement and dependence properties such as volatility clusters, leverage effects, and serial autocorrelations can be generated by the generator function of Quant GANs, demonstrably in high fidelity.

• The paper introduces a novel framework using TCN-based GANs to generate synthetic financial time series that capture realistic market phenomena.
• It employs Temporal Convolutional Networks for improved long-range dependency modeling and ensures risk-neutral valuation for option pricing.
• Empirical results show Quant GANs outperform GARCH models in capturing volatility clustering and autocorrelations, enhancing trading strategy robustness.

An Expert Overview of "Quant GANs: Deep Generation of Financial Time Series"

The paper "Quant GANs: Deep Generation of Financial Time Series" explores the intersection of deep learning methodologies, particularly Generative Adversarial Networks (GANs), and financial time series modeling. The authors propose Quant GANs, a novel approach designed to simulate realistic asset price paths that could aid in extending limited datasets and refining financial trading strategies.

Core Idea and Methodology

The work introduces a robust framework where GANs are employed to generate synthetic financial time series that exhibit properties naturally observed in real financial markets. The model builds on Temporal Convolutional Networks (TCNs) rather than traditional Recurrent Neural Networks (RNNs) to address the limitations related to long-range dependency modeling. The use of TCNs within the GAN architecture is presented as a means to generate time series that demonstrate realistic features such as volatility clustering, leverage effects, and serial autocorrelations.

The generator in their GAN framework, termed as Stochastic Volatility Neural Networks (SVNNs), is meticulously designed to transition to a risk-neutral distribution, allowing for application in financial contexts such as option pricing under different market conditions. This makes each generated path compatible with financial theories that rely on risk-neutral valuation.

Numerical Results and Implications

Empirically, the Quant GANs exhibit remarkable alignment with historical data, both in terms of distributional and dependence properties. The paper includes a quantitative comparison using metrics such as Earth Mover's Distance (EMD) and the DY metric to validate the generated paths against real-world data. The results illustrate that Quant GANs outperform traditional GARCH models, especially in capturing complex financial phenomena over various time horizons.

Theoretical and Practical Contributions

Theoretically, this work makes significant contributions by providing a rigorous mathematical foundation for SVNNs, enhancing the understanding of their projection capabilities in financial time series modeling. Additionally, the usage of the Lambert W function to manage the heavy-tailed nature of financial time series is a noteworthy feature that contributes to the realistic representation of financial data dynamics.

Practically, Quant GANs open new possibilities for quantitative finance, particularly in areas where data sufficiency and authenticity are challenging. The ability to generate rich, synthetic datasets can be pivotal for stress-testing trading strategies that rely on extensive backtesting.

Future Directions

While the paper sets a precedent in integrating GANs with financial models, it also opens several avenues for future research. Refining the tail behavior modeling of generated financial time series and establishing a unified evaluation metric across models are potential areas for further exploration. Moreover, the adaptability of Quant GANs in high-frequency trading environments or other financial derivatives could be investigated, which would push the boundaries of current financial modeling practices.

Overall, "Quant GANs: Deep Generation of Financial Time Series" contributes substantially to both the theoretical and practical understanding of financial time series generation using advanced deep learning techniques. It lays a comprehensive foundation for further research and application in financial domains where realistic data simulation is critical.