A Structural Time Series Analysis of the Effect of Quantitative Easing on Stock Prices

Abstract

In this paper, a structural time series model is estimated to analyse the effect of quantitative easing (QE) on stock prices for the US, UK and Japan. The model is estimated by maximum likelihood in a time-varying parametric framework, using the DJIA, S&P500, NASDAQ, FTSE100 and the NIKKEI225 as the dependent variable and the balance sheet of the respective Central Bank as an explanatory variable, along with the unobserved components that account for the behaviour of other explanatory variables that are not explicitly specified in the model. The results show that QE had a significant but not exclusive effect on the DJIA, S&P500 and the NASDAQ, suggesting that these stock prices are also affected by other missing variables and cyclical movements. However, for the UK and Japan, no effect of QE on the FTSE100 and the NIKKEI225 is found, suggesting that variables other than QE are important for the rise in these stock prices. One plausible explanation for this result is that perhaps QE becomes effective only after a certain threshold level is met.

The Impact of Quantitative Easing on Stock Prices in the U.S. Stock Market - Structural VAR Model Analysis

“…According to Tawadros and Moosa, QE may only have an impact once a specific threshold has been reached [25]. This interpretation could potentially clarify hitherto findings in this investigation.…”

“…According to Tawadros and Moosa, stock prices have a strong association with QE [25]. However, the estimated structural time series model revealed that missing variables other than QE influenced stock prices.…”