switching volatility in a nonliner open economySwitching Volatility in a Nonlinear Open Economy*
bSergey IvashchenkoandaJonathan Benchimol
Discussion Paper 2020.04
Bank of Israel - http://www.boi.org.il
* This paper does not necessarily reflect the views of the Bank of Israel. We thank the referees,
Robert Kollmann, John B. Taylor, Yossi Yakhin, and the participants at the Bank of Israel
Research Department seminar; the 3rd CEPR MMCN Annual Conference; the 15th Dynare
Annual Conference; the 5th Henan University and INFER Applied Macroeconomics
Workshop; and the 49th Money, Macro, and Finance Research Group conferences for their
valuable comments. a Bank of Israel, Jerusalem, Israel. Corresponding author.
Email: jonathan.benchimol@boi.org.il b Russian Academy of Sciences (IREP), Financial Research Institute, and Saint-Petersburg
State University, Saint Petersburg, Russia.
Any views expressed in the Discussion Paper Series are those of the authors
and do not necessarily reflect those of the Bank of Israel
Research Department, Bank of Israel. POB 780, 91007 Jerusalem, Israel
Switching Volatility in a Nonlinear Open Economy
Jonathan Benchimol and Sergey Ivashchenko
Abstract
Uncertainty about a regime's economy can change drastically around a crisis. An imported crisis such
as the global financial crisis in the euro area highlights the effect of foreign shocks. Estimating an
open-economy nonlinear dynamic stochastic general equilibrium model for the euro area and the
United States including Markov-switching volatility shocks, we show that these shocks were
significant during the global financial crisis compared with periods of calm. We describe how US
shocks from both the real economy and financial markets affected the euro area economy and how
bond reallocation occurred between short- and long-term maturities during the global financial crisis.
Importantly, the estimated nonlinearities when domestic and foreign financial markets influence the
economy, should not be neglected. The nonlinear behavior of market-related variables highlights the
importance of higher-order estimation for providing additional interpretations to policymakers.






































0
-6
IRF +1 std. shock IRF +2 std. shock IRF -2 std. shock
10 20 30 40
0
0.05
0
0.05
0.1
IRF +1 std. shock IRF +2 std. shock IRF -2 std. shock
10 20 30 40
10 -3
-0.02
-0.01
0
0.01
0
1
0.05
-0.02
-0.01
0
-1
5
0.1
0.1
5
0.05
0.1
2
4
6
0.05
0.005
0.01
2
-2
-1
0.05
2
1
2
0.5
5
5
1
10 -5
IRF +1 std. shock IRF -3 std. shock IRF +3 std. shock
10 20 30 40
0.05
0.1
0.05
5
0.05
0.01
0
0.1
-3
IRF +1 std. shock IRF -3 std. shock IRF +3 std. shock
10 20 30 40
-0.005
0
-3
IRF +1 std. shock IRF -3 std. shock IRF +3 std. shock
10 20 30 40
-0.1
-0.05
0
0
0.5
1