Portfolio Optimization under Varying Market Risk Conditions: Copula Dependence and Marginal Value Approaches

سال انتشار: 1403
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 101

فایل این مقاله در 15 صفحه با فرمت PDF قابل دریافت می باشد

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:

شناسه ملی سند علمی:

JR_AMFA-9-1_018

تاریخ نمایه سازی: 4 دی 1402

چکیده مقاله:

This paper aims to investigate the portfolio optimization under various market risk conditions using copula dependence and extreme value approaches. According to the modern portfolio theory, diversifying investments in assets that are less correlated with one another allows investors to assume less risk. In many models, asset returns are assumed to follow a normal distribution. Consequently, the linear correlation coefficient explains the dependence between financial assets, and the Markowitz mean-variance optimization model is used to calculate efficient asset portfolios. In this regard, monthly data-driven information on the top ۳۰ companies from ۲۰۱۱ to ۲۰۲۱ was the subject to consideration. In addition, extreme value theory was utilized to model the asset return distribution. Using Gumbel’s copula model, the dependence structure of returns has been analyzed. Distribution tails were modeled utilizing extreme value theory. If the weights of the investment portfolio are allocated according to Gumbel’s copula model, a risk of ۲.۸% should be considered to obtain a return of ۳.۲%, according to the obtained results.

نویسندگان

Jila Ahmadi

Department of Management, Kashan Branch, Islamic Azad University, Kashan, Iran

Hasan Ghodrati Ghezaani

Department of Management, Kashan Branch, Islamic Azad University, Kashan, Iran

Mehdi Madanchi Zaj

Department of Financial Management, Electronic Unit, Islamic Azad University, Tehran, Iran

Hossein Jabbari

Department of Accounting and Management, Kashan Branch, Islamic Azad University, Kashan, Iran

Aliakbar Farzinfar

Department of Accounting and Management, Kashan Branch, Islamic Azad University, Kashan, Iran