BALANCING MONETARY POLICY IN DEFENCE ECONOMICS IN UKRAINE

被引:0
|
作者
Versal, Nataliia [1 ,2 ]
Krasota, Olena [3 ]
Lialkin, Oleksandr [4 ]
Khytryi, Oleksandr [4 ]
Rybak, Ilona [4 ]
Sydorenko, Daryna [4 ]
机构
[1] Taras Shevchenko Natl Univ Kyiv, Dept Insurance Banking & Risk Management, Kiev, Ukraine
[2] Mendel Univ Brno, Dept Accounting & Taxes, Brno, Czech Republic
[3] Taras Shevchenko Natl Univ Kyiv, Dept Econ Theory Macro & Microecon, Kiev, Ukraine
[4] Taras Shevchenko Natl Univ Kyiv, Kiev, Ukraine
关键词
macroeconomic instability; monetary policy; inflation; devaluation; monetary transmission mechanism; central banking; impulse response analysis;
D O I
10.55643/fcaptp.2.49.2023.3991
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
The russian federation's launch of a full-scale war against independent Ukraine on Feb-ruary 24, 2022, has presented unprecedented challenges to the country. In addition to resistance on the battlefield, Ukraine must implement adaptive macroeconomic policies to address the situation. This combination of military and economic efforts not only prevents economic collapse but also maintains fragile macroeconomic stability during wartime. Monetary stability becomes especially important, highlighting the absolute ne-cessity for effective implementation of monetary policy. This article aims to identify the key characteristics of Ukraine's defence economy and forecast key policy rates and exchange rates during the war. The prerequisite for forecasting was the analysis of endogenous and exogenous factors determining the current state of the Ukrainian economy: index of business expectations in Ukraine and partner countries, state of international trade and balance of payments, disparities in the labour market, reorientation of the state budget to military needs, devaluation of the national currency, high inflation, increase of financial capital price. Modelling is based on consumer price index (CPI), household inflation expectations, key policy rate of the National Bank of Ukraine, real and nominal effective exchange rates hryvnia to USA dollar, gross and net international reserves, gross and net foreign ex-change market interventions, the UK CPI, the USA CPI, EU CPI, and the weighted aver-age yield of domestic government bonds. The methodology involved the use of the VECM model (Vector Error Correlation Model) and the Bagging machine learning method, adapted to time series. Using this methodology enabled an accurate forecast of the key policy rate. In determining the optimal exchange rate, a modified formula was used that takes into account the monetary base, total bank deposits, foreign currency deposits in banks, exchange rate in the black market, and international reserves. This modification enabled the prediction of an exchange rate that closely approximates the official exchange rate.
引用
收藏
页码:24 / 42
页数:19
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