João Nicolau, Paulo M. M. Rodrigues
arXiv 20 Sep 2024 · Econometrics
arXiv:2409.13531 · PDF · DOI · OpenAlex · Extracted main text
This paper introduces a flexible framework for the estimation of the conditional tail index of heavy tailed distributions. In this framework, the tail index is computed from an auxiliary linear regression model that facilitates estimation and inference based on established econometric methods, such as ordinary least squares (OLS), least absolute deviations, or M-estimation. We show theoretically and via simulations that OLS provides interesting results. Our Monte Carlo results highlight the adequate finite sample properties of the OLS tail index estimator computed from the proposed new framework and contrast its behavior to that of tail index estimates obtained by maximum likelihood estimation of exponential regression models, which is one of the approaches currently in use in the literature. An empirical analysis of the impact of determinants of the conditional left- and right-tail indexes of commodities' return distributions highlights the empirical relevance of our proposed approach. The novel framework's flexibility allows for extensions and generalizations in various directions, empowering researchers and practitioners to straightforwardly explore a wide range of research questions.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Wang, H. and C. Tsai (2009) Tail index regression | 1.000 | 7 | 3 | 100% |
| 2 | Nicolau, J., P. M. M. Rodrigues, and M. Z. Stoykov (2023) Tail index estimation in the presence of covariates: Stock returns' tail risk dynamics self | 0.874 | 6 | 2 | 100% |
| 3 | Beirlant, J. and Y. Goegebeur (2003) Regression with response distributions of pareto-type | 0.811 | 4 | 2 | 100% |
| 4 | Gabaix, X. and R. Ibragimov (2012) Log(rank-1/2): A simple way to improve the ols estimation of tail exponents | 0.644 | 2 | 2 | 100% |
| 5 | Hill, B. M (1975) A simple general approach to inference about the tail of a distribution | 0.644 | 2 | 2 | 100% |
| 6 | Nicolau, J. and P. M. M. Rodrigues (2019) A new regression-based tail index estimator self | 0.644 | 2 | 2 | 100% |
| 7 | Alquist, R., S. Bhattarai, and O. Coibion (2020) Commodity-price comovement and global economic activity | 0.405 | 1 | 1 | 100% |
| 8 | Ammann, M., M. Moerke, M. Prokopczuk, and C. M. Wursig (2023) Commodity tail risks | 0.405 | 1 | 1 | 100% |
| 9 | Beirlant, J. and Y. Goegebeur (2004) Local polynomial maximum likelihood estimation for pareto-type distributions | 0.405 | 1 | 1 | 100% |
| 10 | Beirlant, J., Y. Goegebeur, J. Segers, J. Teugels, D. Waal, and C. F… (2004) Statistics of extremes: Theory and applications | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 34 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | High-Dimensional Tail Index Regression | 0.000 | 3 | 1 |