Reconstructing heavy-tailed distributions by splicing with maximum entropy in the mean

IF 0.4 4区 经济学 Q4 BUSINESS, FINANCE
Santiago Carrillo, H. Gzyl, A. Tagliani
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引用次数: 9

Abstract

Sometimes it is not possible to obtain a single parametric density with the desired tail behavior to fit a given data set. Splicing two different parametric densities is a useful process in such cases. Since the two parts depend on local data, a question arises over how best to assemble the two parts so that the properties of the whole data set are taken into account. We propose an application of the method of maximum entropy in the mean to splice the two parts together in such a way that the resulting global density has the first two moments of the full data set.
用最大均值熵拼接法重建重尾分布
有时不可能获得具有所需尾行为的单参数密度来拟合给定的数据集。在这种情况下,拼接两个不同的参数密度是一个有用的过程。由于这两个部分依赖于本地数据,因此出现了一个问题,即如何最好地组合这两个部分,以便考虑到整个数据集的属性。我们提出了一种应用均值最大熵的方法,将这两部分拼接在一起,使所得到的全局密度具有完整数据集的前两个矩。
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来源期刊
Journal of Operational Risk
Journal of Operational Risk BUSINESS, FINANCE-
CiteScore
1.00
自引率
40.00%
发文量
6
期刊介绍: In December 2017, the Basel Committee published the final version of its standardized measurement approach (SMA) methodology, which will replace the approaches set out in Basel II (ie, the simpler standardized approaches and advanced measurement approach (AMA) that allowed use of internal models) from January 1, 2022. Independently of the Basel III rules, in order to manage and mitigate risks, they still need to be measurable by anyone. The operational risk industry needs to keep that in mind. While the purpose of the now defunct AMA was to find out the level of regulatory capital to protect a firm against operational risks, we still can – and should – use models to estimate operational risk economic capital. Without these, the task of managing and mitigating capital would be incredibly difficult. These internal models are now unshackled from regulatory requirements and can be optimized for managing the daily risks to which financial institutions are exposed. In addition, operational risk models can and should be used for stress tests and Comprehensive Capital Analysis and Review (CCAR). The Journal of Operational Risk also welcomes papers on nonfinancial risks as well as topics including, but not limited to, the following. The modeling and management of operational risk. Recent advances in techniques used to model operational risk, eg, copulas, correlation, aggregate loss distributions, Bayesian methods and extreme value theory. The pricing and hedging of operational risk and/or any risk transfer techniques. Data modeling external loss data, business control factors and scenario analysis. Models used to aggregate different types of data. Causal models that link key risk indicators and macroeconomic factors to operational losses. Regulatory issues, such as Basel II or any other local regulatory issue. Enterprise risk management. Cyber risk. Big data.
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