This is a historical research and backtest case study. It is not a live performance claim, investment forecast or representation that synthetic data is observed market data.
Can synthetic derivatives features improve research where observed options data is sparse?
The source case study focused on the JSE and described a VAE-GAN approach for producing synthetic options-chain information from sparse historical data and correlated market inputs.
The research backtest reported 50.48% versus 42.46%.
Those figures describe the historical research backtest only; they are not current returns, expected returns or live customer performance.
Generate, critique and validate rather than treating synthetic data as observed fact.
The source page described a Variational Autoencoder for probabilistic generation and an adversarial validation layer, with financial constraints such as put-call relationships used as part of the realism checks.
Source-reported portfolio comparison.
The visual below reproduces the two backtest figures stated in the supplied historical case-study page. It is not a forecast or a live-performance claim.
Use the application designed for the work.
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