G12 Asset Pricing; Trading volume; Bond Interest Rates

January 1, 2010

We show how low-frequency boom and bust cycles in asset prices can emerge from Bayesian learning by investors. Investors rationally maximize infinite horizon utility but hold subjective priors about the asset return process that we allow to differ infinitesimally from the rational expectations prior. Bayesian updating of return beliefs then gives rise to self-reinforcing return optimism that results in an asset price boom. The boom endogenously comes to an end because return optimism causes investors to make optimistic plans about future consumption. The latter reduces the demand for assets that allow to intertemporally transfer resources. Once returns fall short of expectations, investors revise return expectations downward and set in motion a self-reinforcing price bust. In line with available survey data, the learning model predicts return optimism to comove positively with market valuation. In addition, the learning model replicates the low frequency behavior of the U.S. price dividend ratio over the period 1926-2006.

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January 6, 2011

return on a well-diversified portfolio of corporate bonds is close to zero. In contrast, the empirical finance literature documents large and time-varying risk premia in the corporate bond market (the "credit spread puzzle"). This paper introduces a parsimonious real business cycle model where firms issue defaultable debt and equity to finance investment. The mix between debt and equity is determined by a trade-off between tax savings and bankruptcy costs. By their very nature, corporate bonds, while safe in normal times, are highly exposed to the risk of economic depression. This motivates introducing a small, time-varying risk of large economic disaster. This simple feature generates large, volatile and countercyclical credit spreads as well as novel business cycle implications. An increase in disaster risk makes default more systematic, leading to higher risk premia, and higher expected discounted bankruptcy costs, hence worsening financial frictions. This leads to a reduction in investment, output, and leverage. Financial frictions amplify significantly the effects of disaster risk: the response of investment and output is about three times larger than in the frictionless model.

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January 1, 2009

In many research studies it is argued that it is possible to extract useful information about future economic growth from the performance of financial markets. However, this study goes further and shows that it is not only possible to use expectations derived from financial markets to forecast future economic growth, but that data about the financial system can be used for this purpose as well. The research is conducted for the Polish emerging economy on the basis of monthly data. The results suggest that, based purely on the data from the financial system, it is possible to construct reasonable measures that can, even for an emerging economy, effectively forecast future real economic activity. The outcomes are proved by two different econometric methods, namely, by a time series analysis and by a probit model. All presented models are tested in-sample and out-of-sample.

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