Tighter Robust Upper Bounds for Options via No-Regret Learning
Authors: Shan Xue, Ye Du, Liang Xu
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical simulations demonstrate that our bounds significantly outperform the benchmarks for both European and Asian options. |
| Researcher Affiliation | Academia | Shan Xue1, Ye Du2*, Liang Xu1 1 School of Business Administration, Southwestern University of Finance and Economics, Chengdu, China 2 Southwestern University of Finance and Economics, Chengdu, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | No | The paper conducts numerical simulations based on theoretical models and parameter settings (e.g., 'We set σ {0.15, 0.3, 0.45}', 'N-time step binomial tree model'), rather than using a traditional publicly available or open dataset for training purposes. |
| Dataset Splits | No | The paper does not specify training/test/validation dataset splits, as it relies on numerical simulations of financial models rather than traditional datasets. |
| Hardware Specification | No | No specific hardware details (e.g., CPU/GPU models, memory) used for running experiments were provided. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned. |
| Experiment Setup | Yes | The range for m is [0.6, 1.4] with stepsize 0.1 while the range for q2(ϕ) is [0, 0.5] with stepsize 10 4. For European options, the benchmarks we use are the bounds in (De Marzo et al. 2016). Our optimal upper bounds are generated by solving the optimization problems with η numerically, while the benchmarks are calculated by plugging in the parameters directly. ... We set S0 = 100, σ = 0.15, T = 1 year, m [0.8, 1.2], and r = q = 0. ... Consider a N-time step binomial tree model from time 0 to time T with step size t = T N , where r = q = 0. During each step, the gross return of the stock is either u = eσ t or d = 1 u. |