Abstract
This dissertation examines how macroeconomic variables influence financial market volatility and correlations using mixed frequency time series methods. The modelling framework allows combining highfrequency and lowfrequency data within the same model and thus allows directly relating the economic data to the lowfrequency component of volatility or correlations. The dissertation sheds light on which economic variables influence the lowfrequency component of volatilities and correlations, as well as examines various methods to improve long horizon forecasts for stock market volatility by utilising the information in macroeconomic variables.
The first essay considers the relative and combined importance of macroeconomic fundamentals and surveybased sentiment data for modelling US equity market volatility in a GARCHMIDAS framework. It uses a data set which accurately takes into account realtime data revisions to lags of the macroeconomic data and extends the analysis to include several new variables. Forwardlooking macroeconomic data is important for forecasting volatility, even after the information in sentiment indicators is controlled for. On the other hand, for example, consumer confidence indicators contain information complementary to forwardlooking macroeconomic variables. Overall, models combining macroeconomic and sentiment data tend to improve insample fit and in some cases also outofsample forecast accuracy compared to models only driven by one type of data. The improvements in forecasting performance are, however, not statistically significant, and therefore the results do not strongly advocate using several explanatory variables in the MIDAS polynomial.
In the second essay I assess the timevariation in predictive ability arising from the inclusion of macroeconomic and financial data in a GARCHMIDAS model for US stock market volatility. I compare forecasts from a GARCHMIDAS model to forecasts from a nested GARCH model, and therefore the differences in forecasting performance directly reflect the impact of economic data. While forecasting performance between the two models is similar when considered over the full outofsample period, there is clear timevariation in relative forecasting performance over subsamples. I suggest the variation could arise from the phase of the business cycle or the volatility environment and find particularly strong evidence in favour of economic variables being important for volatility forecasting during lowvolatility periods. Forecast combination methods and a decision rule based on conditional predictive ability produce consistently better forecasts than the GARCH model, although statistical significance of the improvements depend on the loss function considered.
The third essay considers the timevariation in the comovement of equity returns and exchange rate returns in several markets using the DCCMIDAS model. Determining the economic drivers of the lowfrequency correlation aids in differentiating between the various theoretical explanations for the correlation, which predict both a positive and a negative relationship. The essay concentrates on the portfolio rebalancing channel and on two hypotheses suggested in the earlier literature, namely flighttoquality and quantitative easing (QE) related searchforyield, in addition to examining the sensitivity of the correlation to other economic variables related to portfolio rebalancing motives, such as the business cycle. Although there are common elements driving the return correlation in the different markets, for instance, interest rate differentials and quantitative easing measures, their impact on the correlation varies, suggesting the underlying theoretical explanation differs across markets. While the onset of US QE1 had a clear impact on the correlations, overall the results suggest that being in a QE regime is more important than announcement effects for the longterm correlation.
The first essay considers the relative and combined importance of macroeconomic fundamentals and surveybased sentiment data for modelling US equity market volatility in a GARCHMIDAS framework. It uses a data set which accurately takes into account realtime data revisions to lags of the macroeconomic data and extends the analysis to include several new variables. Forwardlooking macroeconomic data is important for forecasting volatility, even after the information in sentiment indicators is controlled for. On the other hand, for example, consumer confidence indicators contain information complementary to forwardlooking macroeconomic variables. Overall, models combining macroeconomic and sentiment data tend to improve insample fit and in some cases also outofsample forecast accuracy compared to models only driven by one type of data. The improvements in forecasting performance are, however, not statistically significant, and therefore the results do not strongly advocate using several explanatory variables in the MIDAS polynomial.
In the second essay I assess the timevariation in predictive ability arising from the inclusion of macroeconomic and financial data in a GARCHMIDAS model for US stock market volatility. I compare forecasts from a GARCHMIDAS model to forecasts from a nested GARCH model, and therefore the differences in forecasting performance directly reflect the impact of economic data. While forecasting performance between the two models is similar when considered over the full outofsample period, there is clear timevariation in relative forecasting performance over subsamples. I suggest the variation could arise from the phase of the business cycle or the volatility environment and find particularly strong evidence in favour of economic variables being important for volatility forecasting during lowvolatility periods. Forecast combination methods and a decision rule based on conditional predictive ability produce consistently better forecasts than the GARCH model, although statistical significance of the improvements depend on the loss function considered.
The third essay considers the timevariation in the comovement of equity returns and exchange rate returns in several markets using the DCCMIDAS model. Determining the economic drivers of the lowfrequency correlation aids in differentiating between the various theoretical explanations for the correlation, which predict both a positive and a negative relationship. The essay concentrates on the portfolio rebalancing channel and on two hypotheses suggested in the earlier literature, namely flighttoquality and quantitative easing (QE) related searchforyield, in addition to examining the sensitivity of the correlation to other economic variables related to portfolio rebalancing motives, such as the business cycle. Although there are common elements driving the return correlation in the different markets, for instance, interest rate differentials and quantitative easing measures, their impact on the correlation varies, suggesting the underlying theoretical explanation differs across markets. While the onset of US QE1 had a clear impact on the correlations, overall the results suggest that being in a QE regime is more important than announcement effects for the longterm correlation.
Original language  English 

Awarding Institution 

Supervisors/Advisors 

Award date  29 Nov 2019 
Place of Publication  Helsinki 
Publisher  
Print ISBNs  9789521087547 
Electronic ISBNs  9789521087554 
Publication status  Published  29 Nov 2019 
MoE publication type  G5 Doctoral dissertation (article) 
Fields of Science
 511 Economics