Kwangmin Jung, Donggyu Kim, Seunghyeon Yu
arXiv 25 Feb 2021 · Finance — Risk Management · publishedJournal of Risk & Insurance (2022) · 17 citations (OpenAlex)
arXiv:2102.12783 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a dynamic process of portfolio risk measurement to address potential information loss. The proposed model takes advantage of financial big data to incorporate out-of-target-portfolio information that may be missed when one considers the Value at Risk (VaR) measures only from certain assets of the portfolio. We investigate how the curse of dimensionality can be overcome in the use of financial big data and discuss where and when benefits occur from a large number of assets. In this regard, the proposed approach is the first to suggest the use of financial big data to improve the accuracy of risk analysis. We compare the proposed model with benchmark approaches and empirically show that the use of financial big data improves small portfolio risk analysis. Our findings are useful for portfolio managers and financial regulators, who may seek for an innovation to improve the accuracy of portfolio risk estimation.
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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 | Engle, R. F. and Kroner, K. F (1995) Multivariate simultaneous generalized arch | 1.000 | 5 | 3 | 100% |
| 2 | Fan, J., Wang, W., and Zhong, Y (2019) Robust covariance estimation for approximate factor models | 0.941 | 6 | 4 | 83% |
| 3 | Bickel, P. J. and Levina, E (2008) Covariance regularization by thresholding | 0.928 | 4 | 3 | 100% |
| 4 | Fan, J., Liao, Y., and Mincheva, M (2013) Large covariance estimation by thresholding principal orthogonal complements | 0.923 | 14 | 8 | 79% |
| 5 | Ait-Sahalia, Y. and Xiu, D (2017) Using principal component analysis to estimate a high dimensional factor model with high-frequency data | 0.909 | 8 | 5 | 75% |
| 6 | Kim, D. and Fan, J (2019) Factor garch-itô models for high-frequency data with application to large volatility matrix prediction self | 0.843 | 4 | 3 | 75% |
| 7 | Li, Q., Cheng, G., Fan, J., and Wang, Y (2018) Embracing the blessing of dimensionality in factor models | 0.811 | 4 | 2 | 100% |
| 8 | Bollerslev, T (1986) Generalized autoregressive conditional heteroskedasticity | 0.737 | 3 | 2 | 100% |
| 9 | Bollerslev, T (1990) Modelling the coherence in short-run nominal exchange rates: a multivariate generalized arch model | 0.737 | 3 | 2 | 100% |
| 10 | Jorion, P (2000) Value at risk: The New Benchmark for Managing Financial Risk | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 88 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Large Volatility Matrix Analysis Using Global and National Factor Models | 0.405 | 1 | 1 |
| 2 | Large Global Volatility Matrix Analysis Based on Observation Structural Information | 0.405 | 1 | 1 |