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Volatility Spillovers in New Member States: A Bayesian Model
Janhuba, Radek ; Horváth, Roman (advisor) ; Červinka, Michal (referee)
Volatility spillovers in stock markets have become an important phenomenon, especially in times of crises. Mechanisms of shock transmission from one mar- ket to another are important for the international portfolio diversification. Our thesis examines impulse responses and variance decomposition of main stock in- dices in emerging Central European markets (Czech Republic, Poland, Slovakia and Hungary) in the period of January 2007 to August 2009. Two models are used: A vector autoregression (VAR) model with constant variance of resid- uals and a time varying parameter vector autoregression (TVP-VAR) model with a stochastic volatility. Opposingly of other comparable studies, Bayesian methods are used in both models. Our results confirm the presence of volatility spillovers among all markets. Interestingly, we find significant opposite trans- mission of shocks from Czech Republic to Poland and Hungary, suggesting that investors see the Central European exchanges as separate markets. Bibliographic Record Janhuba, R. (2012): Volatility Spillovers in New Member States: A Bayesian Model. Master thesis, Charles University in Prague, Faculty of Social Sciences, Institute of Economic Studies. Supervisor: doc. Roman Horváth Ph.D. JEL Classification C11, C32, C58, G01, G11, G14 Keywords Volatility spillovers,...
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Transmission of uncertainty shocks: learning from heterogeneous responses on a panel of EU countries
Claeys, Peter ; Vašíček, Bořek
Numerous recent studies, starting with Bloom (2009), highlight the impact of varying uncertainty levels on economic activity. These studies mostly focus on individual countries, and cross-country evidence is scarce. In this paper, we use a set of (panel) BVAR models to study the effect of uncertainty shocks on economic developments in EU Member States. We explicitly distinguish between domestic, common and global uncertainty shocks and employ new proxies of uncertainty. The domestic uncertainty indicators are derived from the Business and Consumer Surveys administered by the European Commission. The common EU-wide uncertainty is subsequently derived by means of a factor model. Finally, the global uncertainty indicator – inspired by Jurado et al. (2015) – is extracted as a common factor from a broad set of forecast indicators that are not driven by the business cycle. The results suggest that real output in EU countries drops after spikes in uncertainty, mainly as a result of lower investment. Unlike for the U.S., there is little evidence of activity overshooting following this initial fall. The responses to uncertainty shocks vary across Member States. These differences can be attributed not mainly to different shock sizes, but rather to cross-country structural characteristics. Member States with more flexible labour markets and product markets seem to weather uncertainty shocks better. Likewise, a higher manufacturing share and higher economic diversification help dampen the impact of uncertainty shocks. The role of economic openness is more ambiguous.
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Volatility Spillovers in New Member States: A Bayesian Model
Janhuba, Radek ; Horváth, Roman (advisor) ; Červinka, Michal (referee)
Volatility spillovers in stock markets have become an important phenomenon, especially in times of crises. Mechanisms of shock transmission from one market to another are important for the international portfolio diversification. Our thesis examines impulse responses and variance decomposition of main stock indices in emerging Central European markets (Czech Republic, Poland, Slovakia and Hungary) in the period of January 2007 to August 2009. Two models are used: A vector autoregression (VAR) model with constant variance of residuals and a time varying parameter vector autoregression (TVP-VAR) model with a stochastic volatility. Opposingly of other comparable studies, Bayesian methods are used in both models. Our results confirm the presence of volatility spillovers among all markets. Interestingly, we find significant opposite transmission of shocks from Czech Republic to Poland and Hungary, suggesting that investors see the Central European exchanges as separate markets. Powered by TCPDF (www.tcpdf.org)
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Volatility Spillovers in New Member States: A Bayesian Model
Janhuba, Radek ; Horváth, Roman (advisor) ; Červinka, Michal (referee)
Volatility spillovers in stock markets have become an important phenomenon, especially in times of crises. Mechanisms of shock transmission from one mar- ket to another are important for the international portfolio diversification. Our thesis examines impulse responses and variance decomposition of main stock in- dices in emerging Central European markets (Czech Republic, Poland, Slovakia and Hungary) in the period of January 2007 to August 2009. Two models are used: A vector autoregression (VAR) model with constant variance of resid- uals and a time varying parameter vector autoregression (TVP-VAR) model with a stochastic volatility. Opposingly of other comparable studies, Bayesian methods are used in both models. Our results confirm the presence of volatility spillovers among all markets. Interestingly, we find significant opposite trans- mission of shocks from Czech Republic to Poland and Hungary, suggesting that investors see the Central European exchanges as separate markets. Bibliographic Record Janhuba, R. (2012): Volatility Spillovers in New Member States: A Bayesian Model. Master thesis, Charles University in Prague, Faculty of Social Sciences, Institute of Economic Studies. Supervisor: doc. Roman Horváth Ph.D. JEL Classification C11, C32, C58, G01, G11, G14 Keywords Volatility spillovers,...
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Rare Shocks vs. Non-linearities: What Drives Extreme Events in the Economy? Some Empirical Evidence
Franta, Michal
A small-scale vector autoregression (VAR) is used to shed some light on the roles of extreme shocks and non-linearities during stress events observed in the economy. The model focuses on the link between credit/financial markets and the real economy and is estimated on US quarterly data for the period 1984–2013. Extreme shocks are accounted for by assuming t-distributed reduced-form shocks. Non-linearity is allowed by the possibility of regime switch in the shock propagation mechanism. Strong evidence for fat tails in error distributions is found. Moreover, the results suggest that accounting for extreme shocks rather than explicit modeling of non-linearity contributes to the explanatory power of the model. Finally, it is shown that the accuracy of density forecasts improves if non-linearities and shock distributions with fat tails are considered.
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