سنجش ارزش در معرض ریسک شرطی با استفاده از ترکیب مدل FIGARCH و نظریه ارزش فرین
محورهای موضوعی : دانش سرمایه‌گذاری
سعید فلاح پور
1
(استادیار دکتری مدیریت مالی، دانشگاه تهران، تهران، ایران)
رضا راعی
2
(استاد دانشگاه تهران، دکتری مدیریت مالی، تهران، ایران)
سعید میرزامحمدی
3
(استادیار گروه سیستم‌های اقتصادی و اجتماعی دانشگاه علم و صنعت ایران، تهران، ایران)
سیدمحمد هاشمی نژاد
4
(دانشجوی دکتری مدیریت مالی دانشگاه تهران، تهران، ایران (نویسنده مسئول))
کلید واژه: تئوری مقدار فرین, تابع زیان لوپز, حافظه بلندمدت, FIGARCH,
چکیده مقاله :
تلاش در جهت شناسایی مدل مناسب و بالا بردن دقت اندازه گیری با استفاده از سنجه ارزش در معرض ریسک از اهمیت ویژه ای برخوردار است. ارزش در معرض ریسک شرطی (CVaR) با نداشتن برخی نواقص ارزش در معرض ریسک، سنجه قابل اعتماد تری می باشد. در این پژوهش با مطالعه در خصوص ویژگی های داده های شاخص کل بورس اوراق بهادار تهران وکاربرد مدل FIGARCH-EVT در محاسبه ارزش در معرض ریسک شرطی، تصریح دقیق تری حاصل شده است. ابتدا مدل ترکیبی GARCH-EVT پیاده سازی شد و با توسعه آن، به مدل FIGARCH-EVT رسیدیم که خاصیت خوشه ای بودن، پویا بودن و حافظه بلندمدت را در مدل سازی لحاظ نموده است. استفاده از مدل FIGARCH برای داده های بازده لگاریتمی شاخص کل، موجب لحاظ کردن خواص فوق در مدل سازی خواهد شد. بعلاوه، خاصیت دنباله پهن بودن داده های بازده شاخص با استفاده از تئوری مقدار فرین (EVT) برای پسماندهای مدل FIGARCH بکار برده می شود. برای مقایسه نتایج، مدل های NORMAL-GARCH و t-Student-GARCH، شبیه سازی تاریخی و GARCH-EVT نیز برای داده ها بازده شاخص بکار برده شده است. نتایج حاصل از مدل ها با استفاده از آزمون های پس‌آزمون مورد بررسی و مقایسه قرار گرفته اند. نتایج حاصل از این پژوهش نشان می دهد که توزیع داده ها بازدهی شاخص نامتقارن دارای چولگی بوده و از توزیع نرمال تبعیت نمی کند. بر اساس چهار آزمون جزء اخلال مازاد استانداردشده، فرآیند نقض تجمعی، پس آزمایی ریزش مورد انتظار و تابع زیان لوپز مدل FIGARCH-EVT نسبت به سایر مدل ها از دقت بالاتری برخوردار می باشد.
Trying to identify an appropriate model to enhance measurement accuracy by using value at risk measures is of particular importance. Conditional Value at Risk (CVaR) with having some of the shortcomings of VaR, is a more reliable measure. In this study, the characteristics of the Tehran Stock Exchange index data usage FIGARCH-EVT model to calculate value at risk if states have been more accurate. GARCH-EVT hybrid implementation model and its development, FIGARCH-EVT model, we found that the effect of clustering, dynamic and long-term memory has been included in the modeling. FIGARCH model for log data output index, which will be modeled in terms of the above properties. In addition, the wide trail property index return data using extreme value theory (EVT) is used for residual FIGARCH model. To compare the results, NORMAL-GARCH models and t-Student-GARCH, historical simulation and GARCH-EVT indicator is used for data output. The results of the model using retrospective tests were evaluated. The results of this study indicate that the data distribution is skewed and asymmetrical index returns do not follow a normal distribution. The tests Standardized Exceedance Residuals and The Cumulative Violation Process and Expected shortfall backtesting and loss function Lopez FIGARCH-EVT model over other models is more accurate.
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