Monetary policy, exchange rates and stock prices in the Middle East region

A structural vector autoregressive model is employed to investigate the impact of monetary policy and real exchange rate shocks on the stock market performance of Kuwait, Oman, Saudi Arabia, Egypt and Jordan. In order to identify the structural shocks both short run and long run restrictions are applied. Unlike previous literature the contemporaneous interdependence between the financial variables are left unrestricted to give a more accurate depiction of the relationships. The heterogeneity of the results reflect the different monetary policy frameworks and stock market characteristics of these countries. Mainly, monetary policy and the real exchange rate shocks have a significant short run impact on the stock prices of the countries that apply a relatively more independent monetary policy and flexible exchange rates.

Consumer confidence indices and stock markets’ meltdowns

Consumer confidence indices (CCIs) are a closely monitored barometer of countries’ economic health and an informative forecasting tool. Using European and US data, we provide a case study of the two recent stock market meltdowns (the post-dotcom bubble correction of 2000–2002 and the 2007–2009 decline at the beginning of the financial crisis) to contribute to the discussion on their appropriateness as proxies for stock markets’ investor sentiment. Investor sentiment should positively covary with stock market movements (DeLong, Shleifer, Summers, and Waldmann 1990); however, we find that the CCI–stock market relationship is not universally positive.We also do not find support for the information effect documented in the previous literature, but identify a more subtle relationship between consumer expectations about future household finances and stock market fluctuations.

Volatility and VaR Forecasting in the Madrid Stock Exchange

This paper provides an empirical study to assess the forecasting performance of a wide range of models for predicting volatility and VaR in the Madrid Stock Exchange. The models performance was measured by using different loss functions and criteria. The results show that FIAPARCH processes capture and forecast more accurately the dynamics of IBEX-35 returns volatility. It is also observed that assuming a heavy-tailed distribution does not improve models ability for predicting volatility. However, when the aim is forecasting VaR, we find evidence of that the Student’s t FIAPARCH outperforms the models it nests the lower the target quantile.