Macroeconomic variables and the stock market : an empirical comparison of the US and Japan
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In this thesis, extensive research regarding the relationship between macroeconomic variables and the stock market is carried out. For this purpose the two largest stock markets in the world, namely the US and Japan, are chosen. As a proxy for the US stock market we use the S&P500 and for Japan the Nikkei225. Although there are many empirical investigations of the US stock market, Japan has lagged behind. Especially the severe boom and bust sequence in Japan is unique in the developed world in recent economic history and it is important to shed more light on the causes of this development. First, we investigate the long-run relationship between selected macroeconomic variables and the stock market in a cointegration framework. As expected, we can support existing findings in the US, whereas Japan does not follow the same relationships as the US. Further econometric analysis reveals a structural break in Japan in the early 1990s. Before that break, the long-run relationship is comparable to the US, whereas after the break this relationship breaks down. We believe that a liquidity trap in a deflationary environment might have caused the normal relationship to break down. Secondly, we increase the variable set and apply a non-linear estimation technique to investigate non-linear behaviour between macroeconomic variables and the stock market. We find the non-linear models to have better in and out of sample performance than the appropriate linear models. Thirdly, we test a particular non-linear model of noise traders that interact with arbitrage traders in the dividend yield for the US and Japanese stock market. A two-regime switching model is supported with an inner random or momentum regime and an outer mean reversion regime. Overall, we recommend investors and policymakers to be aware that a liquidity trap in a deflationary environment could also cause severe downturn in the US if appropriate measures are not implemented accordingly.
Thesis, PhD Doctor of Philosophy
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