To address the Great Recession, with interest rates close to the zero lower bound, the main central banks, among which the European Central Bank (ECB), resorted to unconventional monetary policy (UMP) measures. These policies mainly consist of asset purchases programmes, which, in the case of the ECB, caused an increase of the Central Bank’s balance sheet of about 185% in the period 2009-2019. Since the main aim pursued by ECB through the UMP implementation was to give new stimulus to the real economy, most of the literature analyses their impact on output, inflation as well as on financial stability; however, only a narrow branch of the literature has volatility as a key research objective. Within this PhD thesis, an extended literature review is discussed in Chapter 1, whereas in Chapter 2 we present a new model that, differently to the current literature, allow us to analyse the UMP effects on stock market volatility, by distinguishing the volatility component depending directly on these new monetary policy tools (which accounts for about the 1.4% of the general volatility process). From an empirical application of our model - which focus on four Eurozone stock indexes (CAC40, DAX30, FTSEMIB and IBEX35) - it emerges clearly the UMP effectiveness in stabilising financial markets: therefore, whereas we observe a volatility jump on announcement days, we find a remarkable volatility reduction due to the UMP implementation. Finally, in Chapter 3, we extend our analysis within the Markov switching (MS) framework, proving the ECB ability to keep the volatility component depending on UMP within a low volatility regime for a maximum of 53 business days. Moreover, the Model Confidence Set procedure finds out how the forecasting power of our unconventional policy proxies improves after the EAPP announcement, giving also evidence in favour of the better out-of-sample performance of the MS models, at the same time.

Stock Market Volatility and ECB’s Unconventional Monetary Policies

Lacava, Demetrio
2020-12-21

Abstract

To address the Great Recession, with interest rates close to the zero lower bound, the main central banks, among which the European Central Bank (ECB), resorted to unconventional monetary policy (UMP) measures. These policies mainly consist of asset purchases programmes, which, in the case of the ECB, caused an increase of the Central Bank’s balance sheet of about 185% in the period 2009-2019. Since the main aim pursued by ECB through the UMP implementation was to give new stimulus to the real economy, most of the literature analyses their impact on output, inflation as well as on financial stability; however, only a narrow branch of the literature has volatility as a key research objective. Within this PhD thesis, an extended literature review is discussed in Chapter 1, whereas in Chapter 2 we present a new model that, differently to the current literature, allow us to analyse the UMP effects on stock market volatility, by distinguishing the volatility component depending directly on these new monetary policy tools (which accounts for about the 1.4% of the general volatility process). From an empirical application of our model - which focus on four Eurozone stock indexes (CAC40, DAX30, FTSEMIB and IBEX35) - it emerges clearly the UMP effectiveness in stabilising financial markets: therefore, whereas we observe a volatility jump on announcement days, we find a remarkable volatility reduction due to the UMP implementation. Finally, in Chapter 3, we extend our analysis within the Markov switching (MS) framework, proving the ECB ability to keep the volatility component depending on UMP within a low volatility regime for a maximum of 53 business days. Moreover, the Model Confidence Set procedure finds out how the forecasting power of our unconventional policy proxies improves after the EAPP announcement, giving also evidence in favour of the better out-of-sample performance of the MS models, at the same time.
21-dic-2020
Unconventional monetary policies, financial market, volatility models, Realized Volatility, Multiplicative Error Model, Model Confidence Set, Markov switching model.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3182149
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