Risk management in general and market risk analysis in particular has been the focus of extensive research in the past several years. The volatility and correlations of securities are the major components of market risk and is crucial for asset allocation and risk management. It can have a wide repercussion on the economy as a whole. The incidents of financial crises such as the recent one in of 2007-2008, have caused great turmoil in financial markets on several continents. For this reason, policy makers often rely on market estimates of volatility as a barometer for the vulnerability of financial markets and the economy.
Market returns are quoted at high frequency levels such as, daily or intra-daily basis while very few of the macroeconomic variables are sampled at daily frequency such as short-term interest rates, exchange rates, while the majority of the rest are quoted on low frequencies. Example such as these includes inflation rate, unemployment rate and GDP. The overall price movement of stock progresses carrying information from such multi-scaled economic variables.
This project in its core aims at investigating the effects of multi-scale macroeconomic variables on the volatility and dependence of global equity returns, which includes, at one hand, understanding statistical inference of such models, and at the other hand, understanding the convergence/divergence pattern of financial markets and predictive ability of volatility models. We also aim to extend the multi-scale model beyond the Gaussian paradigm on distributions of error terms. In this way we allow them to interact with the returns via parsimonious specification of model and look for the possible differential impacts of macroeconomic factors on the evolution of asset values.