Developing Asset Correlation Risk Model (ACR) with Asset- Liability Management (ALM) Approach with using of VECM model

Subject Areas : Financial engineering

Mahdi Hemmati Asiabaraki ¹ , Mohammadhasan Gholizadeh ² , Seyed Mozafar Mirbargkar ³

1 - Department of financial management, Rasht Branch, Islamic Azad University, Rasht, Iran.
2 - Department of Management, Faculty of Literature and Humanities, University of Guilan, Rasht, Iran.
3 - Department of Business Management, Rasht Branch, Islamic Azad University, Rasht, Iran

Received: 2020-05-12 Accepted : 2020-05-12 Published : 2020-03-20

Keywords: Granger causality, Asset Relation Risk, Asset- Liability Management, Vector Error Correction Model,

Abstract :

Banks, as levers in macroeconomic policies, by regulating and adjusting the bank's interest rates, enforce monetary policies and controls inflation and unemployment, which is one of the most important macroeconomic goals. One of these tools is asset-debt management. Therefore, the purpose of this research is to develop the Asset Correlation Risk Model (ACR) with the Asset- Liability Management approach (ALM). This research is descriptive in nature and in terms of its purpose. The statistical population of the research is the companies accepted in the Tehran Stock Exchange and the sample of the banks accepted in this collection, which can be extracted from the research data. The research period is from 1391 to 1396, with 20 banks selected as research samples. This research has a theoretical model and a vector error correction model was used to test the hypotheses. According to the t-statistic and the coefficient of estimation of the VECM model, it is determined that the effect of using the debt-asset management approach on the asset-liability correlation risk in a long-term equilibrium is decreasing.

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4) Moskowitz, T. (2003). An analysis of covariance risk and pricing anomalies. Rev. Financ. Stud. 16, 417–457.
5) Po, W.L. & Li, D. (2006) Optimal dynamic portfolio selection: multi period mean–variance formulation. Math. Finance 10, 387–406.
6) Wang, J., Forsyth, P.A. (2011). Continuous time mean variance asset allocation: a time consistent strategy. European J. Oper. Res. 209, 184–201.
7) Zhou, X. Y., Li, D. (2000). Continuous-time mean–variance portfolio selection: a stochastic LQ framework. Appl. Math. Optim. 42, 19–33.