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Paper Information

Journal:   JOURNAL OF FINANCIAL ECONOMICS (FINANCIAL ECONOMICS AND DEVELOPMENT)   winter 2019 , Volume 12 , Number 45 #p00531; Page(s) 93 To 126.

Asset-Liability Dynamic GAP Forecasting applying Adaptive Neuro-Fuzzy Inference System(ANFIS) and Auto Regressive Fractional Integral Moving Average (arfima): Case Study of a Private Bank in Iran

Author(s):  Ghasemi Abdolrasool*, BAHRAMI JAVID, Shabani Jafroodi Sorayya
* Faculty of Economics, Allameh Tabatabaei University, Tehran, Iran
The proper management of liquidity, the ability to raise funds and timely fulfillment of obligations, is a prerequisite for the survival of banks. Proper liquidity management can reduce the likelihood of serious banking problems. Indeed, given that liquidity shortage in a bank can result in widespread systemic consequences, the importance of liquidity for each bank is beyond any other issue. In addition, banks should always monitor their assets and liabilities strictly in order to increase the profitability of the banks and manage the liquidity from banking operations in the best possible way as well. Estimated Maturity of asset-liability gap in future periods is one of the key measures in the direction of optimal liquidity management and identifying the potential of the bank against any deficits in the leading one. In this paper, the asset-liability gap is calculated based on two adaptive-neuro-fuzzy models and long-term memory modeling (ARFIMA) modeling. The results of the research show that, the accuracy of both models in the prediction of the dynamic gap has been high. However, the results of modeling applying a long-term memory pattern show higher accuracy in this regard. Thus banks can assess the long-term position of the asset-liability gap and identify the amount of their surplus liquidity resources using this template.
Keyword(s): Banks liquidity management,anfis method,arfima method,private banks
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