HyperIV: Real-time Implied Volatility Smoothing
Authors: Yongxin Yang, Wenqi Chen, Chao Shu, Timothy Hospedales
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments across 8 index options demonstrate that Hyper IV achieves superior accuracy compared to existing methods while maintaining computational efficiency. |
| Researcher Affiliation | Academia | 1Queen Mary University of London 2University of Edinburgh. Correspondence to: Yongxin Yang <EMAIL>. |
| Pseudocode | Yes | The Py Torch implementation is shown below: iv_network = torch.nn.Sequential( torch.nn.Linear(2, 16), torch.nn.Tanh(), torch.nn.Linear(16, 16), torch.nn.Tanh(), torch.nn.Linear(16, 1), torch.nn.Softplus() ) class Set Embedding Network(nn.Module): def __init__(self, input_dim, output_dim, num_heads=2, num_layers=2, hidden_dim=128): super(Set Embedding Network, self).__init__() self.fc1 = nn.Linear(input_dim, hidden_dim) self.attention_layers = nn.ModuleList( [nn.TransformerEncoderLayer( d_model=hidden_dim, nhead=num_heads, dim_feedforward=hidden_dim, batch_first=True, dropout=0, activation="relu") for _ in range(num_layers)]) self.fc2 = nn.Linear(hidden_dim, output_dim) def forward(self, x): x = self.fc1(x) for layer in self.attention_layers: x = layer(x) x = x.mean(dim=1) x = self.fc2(x) return x |
| Open Source Code | Yes | We make code available at https://github.com/qmfin/hyperiv. |
| Open Datasets | No | Due to licence restrictions, we cannot redistribute the data used for model training. Academic researchers may access the end-of-day data through their institution s subscription to WRDS (which includes Option Metrics). |
| Dataset Splits | Yes | For the one-minute interval data, we train the model using data before 2023-08-01 and test on the subsequent intervals. For the one-day data, we train the model using data before 2023-01-01 and test on the remaining intervals. |
| Hardware Specification | Yes | All training and testing procedures are executed on a NVIDIA A100 GPU with 80G VRAM, with the exception of SSVI, which runs on an Intel Xeon CPU. |
| Software Dependencies | No | The Py Torch implementation is shown below: iv_network = torch.nn.Sequential(...) (This indicates PyTorch, but no specific version number is provided for PyTorch or any other software dependency). |
| Experiment Setup | Yes | The total training duration spans 500 epochs, with each epoch processing all intervals in batches of 128. ... We found that a simple multi-layer perceptron (MLP) with two hidden layers, each containing 16 neurons, performs well after initial experimentation. ... The architectures of gθ( ) and hω( ) are described in detail in Appendix A. |