## 7 Discussion  
Various experiments were designed to explore how variations in RSQGAN topology and hyperparameter settings may impact its performance versus the QGAN benchmark. The general observations that could be made from these experiments were:  
1. **The QGAN and RSQGAN performance can be highly variable between assets and regimes for fixed topology and hyperparameter settings. ** The baseline settings used were the same architectural and hyperparameter settings as the QGAN open-source implementation by [PERSON] et al. (2018). Unsurprisingly, these settings resulted in a mixed performance across all stocks and regimes. Baseline QGAN settings consisted of six hidden layers and skip layer connections of 50 nodes each while using fixed discriminator and generator learning rates of 0.0003 and 0.0001, respectively. On the training stock set, RSQGAN outperformed QGAN on all four crisis-sensitive SF  
Figure 14: SF training losses by QGAN and RSQGAN by stock in crisis regime training over 256 epochs. _In-sample stocks._ Subplot order: WOW, BHP, and WBC. **Top left panel**: Volatility clustering ACF. **Top right panel**: Leverage effect. **Bottom left**: Coarse–fine volatility. Blue dots/traces: QGAN SF test metrics. Orange dots/traces: RSQGAN SF test metrics. Black dots/traces: Real data. **Bottom right**: Cumulative loss stoptime for \(\theta=12.5\). Darkbrown trace: Real stoptime CDF. Gray traces: QGAN. Red traces: RSQGAN. Note that RSQGAN is simultaneously training on two other regimes. Figure 15: SF training losses by QGAN and RSQGAN by stock in crisis regime training over 256 epochs. _Validation stocks._ Subplot order: WES, RIO, and ANZ. **Top left panel**: Volatility clustering ACF. **Top right panel**: Leverage effect. **Bottom left**: Coarse–fine volatility. Blue dots/traces: QGAN SF test metrics. Orange dots/traces: RSQGAN SF test metrics. Black dots/traces: Real data. **Bottom right**: Cumulative loss stoptime for \(\theta=12.5\). Darkbrown trace: Real stoptime CDF. Gray traces: QGAN. Red traces: RSQGAN. Note that RSQGAN is simultaneously training on two other regimes. test metrics. However, on the validation stock set, RSQGAN only outperformed on the loss CDF stopping distribution. By construction, the conditional RSQGAN, which is trained by learning from average class losses, performs similarly to the average of multiple QGANs each trained on single-regime datasets. But due to regime class imbalance, it outperforms the minority crisis regime class at the expense of the performance on the 'normal' regime classes. However, as the performance was observed to be highly variable across stock and regime conditions, this suggests that additional model flexibility is required to deal with different regime lengths or stock-specific behaviour. Choosing an appropriate receptive field size (and hence network depth) is an important hyperparameter for simulating different regime lengths in a single model. Selecting an RFS that is too short could lead to longer-term time independence in simulations than is justifiable. Selecting an RFS that is too long would reduce the effective training dataset while increasing learnable parameters. The receptive field size should be consistent with the intended simulation horizon. Despite selecting training and validation stock sets from the same long-term correlation cluster, the mixed performance between stocks and regimes highlights that the same RSQGAN topology was not sufficiently rich to learn stock-specific features that could be applied from one stock (e.g., BHP) and to expect a similarly strong performance in a highly correlated stock (e.g., RIO). Thus, a richer topology such as a joint drift and volatility GAN, such as the stochastic volatility neural network (SVNN) model by [PERSON] et al. [12], could be extended to the conditional case in the future. A single-drift topology in isolation will underperform in simulating stocks that show strong momentum characteristics (i.e., a negative leverage effect) where high returns predict lower future volatility.