پیش‌بینی نرخ ارز: مقایسه الگوهای رشد لجستیک با الگوهای رقیب

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری اقتصاد، گروه اقتصاد دانشکده اقتصاد و مدیریت دانشگاه ارومیه، ارومیه، ایران

2 دانشیار گروه اقتصاد دانشکده اقتصاد و مدیریت دانشگاه ارومیه، ارومیه، ایران

10.29252/ecoj.10.3.157

چکیده

هدف اصلی این پژوهش، مقایسه الگو‌های رشد لجستیک ‌هاروی، هاروی، شبکه‌عصبی غیرخطی اتورگرسیو و طراحی و یافتن الگوی بهینه پیش‌بینی نرخ ارز بازار آزاد با نوسان زیاد و روند حرکتی غیرخطی است که تاکنون از این نوع الگو‌ها برای پیش‌بینی نرخ ارز در ایران  استفاده نشده است. در این پژوهش، با بکارگیری الگو‌های رشد "لجیستیک­­‌هاروی"، "هاروی" و با افزودن جزء غیرخطی بر اساس بسط سری تیلور توابع مثلثاتی، بر مبنای داده‌های روزانه مربوط به سال‌های 1398:03-1392:01، نوسان‌های نرخ ارز پیش‌بینی و کارآمدی این الگو‌ها بر اساس معیارهای پیش‌بینی و نتایج آن با شبکه‌عصبی غیر‌خطی خودرگرسیونی مورد مقایسه و ارزیابی قرار گرفته است. نتایج آزمون‌های ریشه واحد بیانگر پایایی داده‌ها و رفتار غیرخطی است. در مرحله برآورد، خوبی برازش الگو‌های لجستیک ­‌هاروی و هاروی تایید نگردید. با افزودن جز غیرخطی به الگوی ‌هاروی برازش بسیار مناسبی از نرخ ارز با ضریب تعیین حداقل 95/99 درصد و  حداقل جذر میانگین مربعات خطا حتی در مقایسه آن با شبکه ‌عصبی غیر خطی اتورگرسیو بدست آمد. بنابراین، نتایج نشان می‌دهد که ترکیب الگوی هاروی با جزء غیرخطی یکی از مزیت‌های اساسی به شمار آمده و  بهتر از الگو‌های دیگر نرخ ارز  را پیش‌بینی می‌کند.

کلیدواژه‌ها

عنوان مقاله [English]

Predicting the Exchange Rate: Comparing Logistic Growth and Competing Models

نویسندگان [English]

  • Haqmed Mansoori-Gargary 1
  • Hassan Khodavaisi 2
1 Ph.D Candidate in Economics, Faculty of Economics and Management, Urmia University, Urmia, Iran
2 Associate Professor of Economics, Faculty of Economics and Management, Urmia University, Urmia, Iran
چکیده [English]

The main objective of this study is to compare the Harvey Logistic Growth Models, Harvey, Nonlinear Autoregressive Neural Network, and to design an optimal model with better predictive accuracy for the exchange rate with high volatility and a nonlinear motion patterns which has been neglected for predicting the exchange rate in Iran.  In this study, we use the "Harvey Logistic" Growth Models, Harvey and adding a nonlinear component based on the Taylor series expansion for trigonometric functions, and using the daily data during 2013:03-2019:05, the fluctuations of the exchange rate and the accuracy and prediction of these models are compared its results with the nonlinear autoregressive neuronal network. The results of unit root tests represent that the data is stationary and has nonlinear property. In the estimation stage, the goodness of fit for the Logistic and Harvey models are not confirmed. By adding nonlinear parts to the Harvey model, a good fit was obtained for the exchange rate with a coefficient of determination about 99.95 percent and a minimum mean square error, even when compared with the nonlinear autoregressive neural network. The results show that combining the Harvey model with the nonlinear component is considered as one of the best models which predicts the exchange rate better than other models.

کلیدواژه‌ها [English]

  • Logistic Harvey
  • Harvey
  • Nonlinear Autoregressive Neural Networks
  • Exchange Rate

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