Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Graphical Models in Heavy-Tailed Markets
Authors: Jose Vinicius de Miranda Cardoso, Jiaxi Ying, Daniel Palomar
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 16,674 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,208 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed methods outperform state-of-the-art benchmarks in an extensive series of practical experiments with publicly available data from the S&P500 index, foreign exchanges, and cryptocurrencies. |
| Researcher Affiliation | Academia | José Vinícius de M. Cardoso, Jiaxi Ying, Daniel P. Palomar Department of Electronic and Computer Engineering Department of Industrial Engineering and Decision Analytics The Hong Kong University of Science and Technology Clear Water Bay, Hong Kong SAR China |
| Pseudocode | Yes | Algorithm 1: Student-t Graph Learning; Algorithm 2: k-component Student-t graph learning |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We perform experiments using historical daily price time series data, available in Yahoo! Finance TM, from financial instruments in three scenarios: (i) stocks belonging to the S&P500 index, (ii) foreign exchange markets, and (iii) cryptocurrencies. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions "cvxpy" but does not provide specific version numbers for it or any other software component used in the experiments. |
| Experiment Setup | Yes | In our ADMM algorithms, we set the penalty parameter to ρ = 1 and the hyperparameter η in (16) is adaptively increased until the rank constraint is satisfied. For GLE and NGL, we use grid search on the sparsity hyperparameter such that the resulting graph yields the highest modularity value. The graph weights in Algorithm 1 and 2 are initialized using the same procedure as in [8]. |