طراحی و تبیین مدل آزمون بحران ریسک اعتباری صنعت بانکداری تحت سناریوهای کلان اقتصادی

محورهای موضوعی : آمار

محسن ضیایی بیدهندی ¹ , مهرزاد مینویی ² , میرفیض فلاح شمس ³

1 - گروه مالی، دانشکده مدیریت، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران.
2 - گروه مالی، دانشکده مدیریت، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران.
3 - گروه مالی، دانشکده مدیریت، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران.

تاریخ دریافت : 1399/12/12 تاریخ پذیرش : 1400/04/03 تاریخ انتشار : 1400/05/01

کلید واژه: Macroeconomics", credit risk, ", Banking Industry", financial crisis",

چکیده مقاله :

دلیل اصلی اجرای پژوهش حاضر، طراحی و تبیین مدل آزمون بحران ریسک اعتباری صنعت بانکداری تحت سناریوهای کلان اقتصادی، است. علاوه بر بهره برداری از مستندات و گزارشهای مربوط به صنعت بانکداری، در ادامه از داده های پانل مربوط به گزارش های مالی و دیتاست های صنعت بانکداری، بهره برداری گردید[1]. در پژوهش حاضر، به منظور انجام تحلیلهای اقتصادسنجی، از نرم افزار E-Views ورژن 10بهره برداری شد. از مهمترین نتایج پژوهش حاضر، میتوان به این مورد اشاره نمود که آماره رگرسیون مربوط به مدل گارچ برای نوسانات بین تابع هدف پژوهش و نرخ رشد GDP(A1)، نرخ بهره(A2)، نرخ بیکاری(A3)، نرخ تورم(A4) و نرخ رشد درامد سرانه (A5)، برابر با 0.926 محاسبه شده و برای مدل GARCH برای نوسانات بین "عامل نرخ رشد نقدینگی(B3)"، عامل نرخ شد درامد نفتی(B1)" ،"عامل نرخ سپرده بانکی(B4)"و "عامل نرخ ارز(B2)"برابر با 0.924 محاسبه گردیده است که نشان دهنده قدرت پیشگویی بسیار بالای مدلهای اقتصادسنجی تحقیق با بهره برداری از نرم افزار اقتصادسنجی E-Views، است

چکیده انگلیسی:

The main reason for conducting the present study is to design and explain the credit crunch risk test model of the banking industry under macroeconomic scenarios. In addition to the use of documents and reports related to the banking industry, the panel data related to the annual reports and datasets of the banking industry were used. In the present study, in order to perform econometric analyzes, E-Views software was used and Matlab artificial intelligence environment was used to design an intelligent system. Then, based on the GARCH method, the regression statistics related to the GARCH model for the fluctuations between the research objective function and GDP growth rate, interest rate, unemployment rate, inflation rate and per capita income growth rate are calculated equal to 0.927, which indicates very high predictive power. The econometric model of research is. One of the most important results of the present study is that according to the calculations performed, the bank's credit portfolio to reduce the probability of default is exactly 91 percent (the fifth level of system output is excellent).

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