Notice: 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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Cross-sectional Learning of Extremal Dependence among Financial Assets
Authors: Xing Yan, Qi Wu, Wen Zhang
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show its flexible tail dependence structure through simulation. Coupled with a GARCH model to eliminate serial dependence of each individual asset return series, we use this novel method to model and forecast multivariate conditional distribution of stock returns, and obtain notable performance improvements in multi-dimensional coverage tests. |
| Researcher Affiliation | Collaboration | Xing Yan School of Data Science City University of Hong Kong EMAIL Qi Wu School of Data Science City University of Hong Kong EMAIL Wen Zhang JD Digits EMAIL |
| Pseudocode | Yes | Algorithm 1 Algorithm for learning parameters of our proposed tail dependence model with data. |
| Open Source Code | No | The paper does not provide any information about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper mentions using stock return data (Apple, IBM, Microsoft, JP Morgan, Walmart, Dow-Jones stocks, SP500) and specifies the date ranges and number of observations for each. However, it does not provide concrete access information (e.g., links, DOIs, specific repositories, or formal citations to publicly accessible versions) for the exact datasets used. |
| Dataset Splits | No | The paper specifies a 'testing set' but does not explicitly mention the use of a separate 'validation' set or its split details. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions various software components and models like 'GARCH model', 'AR(1)-GARCH(1,1)', 'quantile regression', 't-distribution innovation', 'multivariate normal', 'multivariate t', 'Clayton copula', and 'Gumbel copula'. However, it does not specify any version numbers for these software dependencies, which is necessary for reproducibility. |
| Experiment Setup | Yes | A is a positive constant satisfying A 3 (see [Wu and Yan, 2019]). We set A = 4 in this paper. Now we have totally n location parameters ยต1, . . . , ยตn, (n2+n)/2 usual correlation/scale parameters ฯ11, ฯ21, ฯ22, . . . , ฯnn, (n2+n)/2 right-tail parameters u11, u21, u22, . . . , unn, and (n2 + n)/2 left-tail parameters v11, v21, v22, . . . , vnn. The total number of parameters is n + 3(n2 + n)/2. ... we set ฮจ = {0.01, 0.02, . . . , 0.98, 0.99} with 99 probability levels. Other smaller set that covers the interval (0, 1) sufficiently is also acceptable, e.g., {0.01, 0.05, 0.1, . . . , 0.9, 0.95, 0.99} with 21 levels. |