Introduction of New Risk Metric using Kernel Density Estimation Via Linear Diffusion

Advances in Mathematical Finance and Applications

مقاله 13، دوره 7، شماره 2، تیر 2022، صفحه 467-476   

نوع مقاله: َApplied-Research Paper

شناسه دیجیتال (DOI): 10.22034/amfa.2020.1896210.1397

نویسندگان

Ahmad Darestani Farahani¹؛ Mohammadreza Miri Lavasani²؛ Hamidreza Kordlouie^{* 3}؛ Ghodratallah Talebnia⁴

¹Department of Finance, Science and Research Branch, Islamic Azad University, Tehran, Iran

²Department of HSE, Science and Research Branch, Islamic Azad University, Tehran, Iran

³Department of Financial Management, Eslamshahr Branch, Islamic Azad University, Tehran, Iran

⁴Department of Accounting, Science and Research Branch, Islamic Azad University, Tehran, Iran.

چکیده

Any investor in stock markets around the world has a deep concern about the shortfalls of allocation wealth to any stock without accurate estimation of related risks. As we review the literature of risk management methods, one of the main pillars for the risk management framework in defining risk measurement approach using historical data is the estimation of the probability distribution function. In this paper, we propose a new measure by using kernel density estimation via diffusion as a nonparametric approach in probability distribution estimation to enhance the accuracy of estimation and consider some distribution characteristics, investor risk aversion and target return which will make it more accurate, compre-hensive and consistent with stock historical performance and investor concerns.

کلیدواژه‌ها

Risk Measurement؛ Generalized Co-Lower Partial Moment؛ Portfolio Optimization؛ Nonparametric estimation؛ Stock Market

مراجع

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