Abstract
Research background: Financial cycles are behind many deep financial crises and it closely connects them with the business cycles, showing long memory properties and effects. Being closely connected with the business cycles, we must first explore the true nature of the financial cycles to understand the nature of the business cycles. Financial cycles are real, they have long memory properties and long-lasting effects on the economy.
 Purpose of the article: This study investigates the use of (SSA) in tracking and monitoring financial cycles focusing on ten (10) transitional economies 2005?2018.
 Methods: Singular spectrum analysis isolate significant oscillatory patterns (cycles) on housing markets with an average 4-years length. We isolate credit cycles just for Bulgaria, implying long memory properties of the cycles since this study investigated medium term (2?5 years) oscillations.
 Findings & Value added: The results prove the importance and advantages of using (SSA) in the study of financial cycles attempting to reveal the true nature of financial cycles as the principal component behind business cycles. Financial cycles show longer oscillations in the credit and property price series, which can explain 37.7%?49.9% of the variance of the total financial cycle fluctuations. Study results are of practical importance, particularly to policy-makers and practitioners in former transitional economies being vulnerable to adverse shocks on the financial markets. The results should assist policy-makers and financial practitioners in building and maintaining a sound financial policy needed to avoid future financial ?bubbles?.
Highlights
Business cycles have always attracted a large interest in the body of literature on economics
Bulgaria, Croatia, Hungary, Poland, Russia and Slovakia we find no evidence of business fluctuations under the embedded dimension
Housing markets in transitional economies follow a clear, statistically significant 4-years oscillatory path which is visible from the Figure 5
Summary
Business cycles have always attracted a large interest in the body of literature on economics. This is because of large negative effects that volatility in economic activity can cause. Volatility in real economic activity is a significant headwind to economic growth. Financial cycles have always been in the shadow of business ones. The crisis of 2008 brought them to light, but after the recovery the world forgot about financial cycles once again. Financial cycles are studied randomly and mostly for most developed economies. Like transitional economies, lack a body of literature on financial cycles. We track financial cycles in ten (10) transitional economies using singular spectrum analysis (SSA) as a potential tool for tracking business cycles efficiently
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