We study the economic sources of stock-bond return comovements and its time variation using a dynamic factor model. We identify the economic factors employing a semi-structural regime-switching model for state variables such as interest rates, inflation, the output gap, and cash flow growth. We also view risk aversion, uncertainty about inflation and output, and liquidity proxies as additional potential factors. We find that macroeconomic fundamentals contribute little to explaining stock and bond return correlations, but that other factors, especially liquidity proxies, play a more important role. The macro factors are still important in fitting bond return volatility; whereas the "variance premium" is critical in explaining stock return volatility. However, the factor model primarily fails in fitting covariances.

Using only daily data on bond and stock returns, we identify and characterize flight to safety (FTS) episodes for 23 countries. On average, FTS days comprise less than 3% of the sample, and bond returns exceed equity returns by 2.5 to 4%. The majority of FTS events are country-specific not global. FTS episodes coincide with increases in the VIX and the Ted spread, decreases in consumer sentiment indicators and appreciations of the Yen, Swiss franc, and US dollar. The financial, basic materials and industrial industries under-perform in FTS episodes, but the telecom industry outperforms. Money market instruments, corporate bonds, and commodity prices (with the exception of metals, including gold) face abnormal negative returns in FTS episodes. Hedge funds, especially those belonging to the "event-driven" styles, display negative FTS betas, after controlling for standard risk factors. Liquidity deteriorates on FTS days both in the bond and equity markets. Both economic growth and inflation decline right after and up to a year following a FTS spell.

We study the economic sources of stock-bond return comovements and its time variation using a dynamic factor model. We identify the economic factors employing a semi-structural regime-switching model for state variables such as interest rates, inflation, the output gap, and cash flow growth. We also view risk aversion, uncertainty about inflation and output, and liquidity proxies as additional potential factors. We find that macroeconomic fundamentals contribute little to explaining stock and bond return correlations, but that other factors, especially liquidity proxies, play a more important role. The macro factors are still important in fitting bond return volatility; whereas the "variance premium" is critical in explaining stock return volatility. However, the factor model primarily fails in fitting covariances.

Abstract:We investigate the valuation and the pricing of initial public offerings (IPOs) by investment banks for a unique dataset of 49 IPOs on Euronext Brussels in the 1993-2001 period. We find that for each IPO several valuation methods are used, of which Discounted Free Cash Flow (DFCF) is the most popular. The offer price is mainly based on DFCF valuation, to which a discount is applied. Our results suggest that DDM tends to underestimate value, while DFCF produces unbiased value estimates. When using multiples, investment banks rely mostly on future earnings and cash flows. Multiples based on post-IPO forecasted earnings and cash flows result in more accurate valuations.