The Relationship Between the European Countries and the Stock Price

the relationship between the European countries and the stock price

APT model        

Conclusion:

We find two primary factors production and interest rate

Movements in production and interest rates clearly determine stock returns in the European countries.

Stock price anticipate movements in production one year in advance but move simultaneously with interest rates. Future changes in industrial production and current changes in long-term interest rates account for approximately one half of stock returns

When taking the entire 1969-2012 period into account, both variables seem to have the same relative importance in the determination of stock returns. However, over different time periods there are cleat differences; in the first year’s interest rates were the main, if not the only factor, but in recent years the variable is of less importance and future production has become the key factor

All this evidence is surprisingly similar for the three European countries, but differs noticeably from the results obtained for the US, where production seems to be the only factor behind stock returns over the whole period.

stock market volatility and macroeconomic fundamentals

GARCH-MIDAS models

we introduced a new component volatility models combining the insights of spline- GARCH and MIDAS filters. This new class allowed us to distinguish short- and long-run sources of volatility and link them directly to economic variables. The new model specifications also relate to the long-established use of realized volatility yet refines these measures through MIDAS filtering.

To assess the economic content, we suggest a variance ratio measuring the contribution of economic sources to expected volatility. The results reveal that for the full sample, the long-run component typically accounts for roughly half of predicted volatility. For the most recent period, the results show roughly a 30% contribution. When the long-run component is driven by economic variables, the numbers are not so high, except for specific subsamples such as the Great Depression and some of the post–World War II era.

What is most encouraging is our findings regarding long- term forecasting. We find that models with the long-term component driven by inflation and industrial production

The relationship between Chinese stock market and the Chinese GDP

Measure: Granger

Data: 1991-2005 GDP and SHANGZH

Conclusion:

In short term, there are not any obviously relationships between the stock market and the GDP, while in long term there are not any stable relationships between the stock market and the GDP either. From 2001, our GDP increased beyond a speed of 7% every year, but the stock market decreased every year. This phenomenon can support the conclusion.