Automatic Outlier Rectification via Optimal Transport
Authors: Jose Blanchet, Jiajin Li, Markus Pelger, Greg Zanotti
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our approach over conventional approaches in simulations and empirical analyses for mean estimation, least absolute regression, and the fitting of option implied volatility surfaces. In this section, we demonstrate the effectiveness of the proposed statistically robust estimator through various tasks: mean estimation, LAD regression, and two applications to volatility surface modeling. |
| Researcher Affiliation | Academia | Jose Blanchet Dept. of Management Science & Engineering Stanford University jose.blanchet@stanford.edu Jiajin Li Sauder School of Business University of British Columbia jiajin.li@sauder.ubc.ca Markus Pelger Dept. of Management Science & Engineering Stanford University mpelger@stanford.edu Greg Zanotti Dept. of Management Science & Engineering Stanford University gzanotti@stanford.edu |
| Pseudocode | Yes | Algorithm 1: Statistically Robust Estimator and Algorithm 2: Statistically Robust Optimization Procedure |
| Open Source Code | No | The work in this paper was conducted with an industry partner, and the code and data are proprietary. |
| Open Datasets | Yes | We select the data set from Chataigner et al. [2020] consisting of (options chain, surface) pairs from the German DAX index. |
| Dataset Splits | Yes | To perform cross-validation, we split the train day into a training and validation sample, which we use to obtain estimates of MAPE and ˆS as a function of δ. for each day, we sample 80% of the training set without replacement as a cross-validation (CV) training set, and use the remaining 20% of the training set as a CV validation set. |
| Hardware Specification | Yes | Experiments are run on a server with a Xeon E5-2398 v3 processor and 756GB of RAM. |
| Software Dependencies | No | The paper mentions 'Py Torch', 'Tensorflow', and 'JAX' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | The hyperparameters for our estimator, namely δ = 0.5 and r = 0.5, remained constant across all corrupted levels. For our estimator, we estimate the surface by subgradient descent with learning rate α = 10 1 and r = 0.5, terminating when the relative change in loss reaches 10 5. In each trial, we run our optimization procedure for with a learning rate of 10 2. We stop when the number of iterations reaches 2000 or the change in the loss function between successive iterations is below a tolerance of 10 6. We initialize θ to the median of the data set. |