Incomplete Information in a Long Run Risks Model of Asset Pricing

We study the effects of incorporating incomplete information in the recently developed long run risks model of asset pricing. Studying the effects of incomplete information in such a setting is tractable, especially in the homoskedastic case with no fluctuating economic uncertainty. The incomplete information model is solved using approximate analytical methods as in the complete information framework analyzed in the literature. Model implications on moments of endogenous variables of interest including rates of return are compared in the long run risks model with and without incomplete information.asset pricing; long run risks; incomplete information; Kalman filter; equity returns; riskfree returns

The Impact of Fat Tails on Equilibrium Rates of Return and Term Premia

We investigate the impact of ignoring fat tails observed in the empirical distributions of macroeconomic time series on the equilibrium implications of the consumption-based asset-pricing model with habit formation. Fat tails in the empirical distributions of consumption growth rates are modeled as a dampened power law process that nevertheless guarantees finiteness of moments of all orders. This renders model-implied mean equilibrium rates of return and equity and term premia finite. Comparison with a benchmark Gaussian process reveals that accounting for fat tails lowers the model-implied mean risk-free rate by 20 percent, raises the mean equity premium by 80 percent and the term premium by 20 percent, bringing the model implications closer to their empirically observed counterparts.pricing model, habit formation, term premium, equity premium, fat tails, dampened power law

Risk Premia in Forward Foreign Exchange Markets: A Comparison of Signal Extraction and Regression Methods

We investigate time varying risk premia in forward dollar/pound monthly exchange rates over the last two decades. We study this issue using a signal plus noise model and separately using regression techniques. Our models account for time varying volatility and non-normalities in the observed series. Our signal plus noise model fails to isolate a statistically significant risk premium component whereas our regression model does. We attribute the discrepancy in the results from the two methods to the low power of the signal plus noise model in discriminating between a time varying risk premium component and a serially uncorrelated spot exchange rate expectational error. An important reason for the low power of the signal plus noise model is its failure to use information on current period forward rates in extracting the risk premium.spot foreign exchange rates; forward foreign exchange rates; timevarying risk premium; signal extraction; non-normality; volatility persistence

A Long-Run Risks Model of Asset Pricing with Fat Tails

WWe explore the effects of fat tails on the equilibrium implications of the long run risks model of asset pricing by introducing innovations with dampened power law to consumption and dividends growth processes. We estimate the structural parameters of the proposed model by maximum likelihood. We find that the homoskedastic model with fat tails leads to significant increase in implied risk premia and volatility of price-dividend ratio over the benchmark Gaussian model, but similar volatility of market return, expected risk free rate and its volatility.asset pricing, long run risks, equity risk premium, fat tails, Dampened Power Law, Levy process

Signal Extraction can Generate Volatility Clusters

volatility clusters; GARCH processes; signal extraction; heavy-tailed distributions

Asset Pricing with Incomplete Information under Stable Shocks

We study a consumption based asset pricing model with incomplete information and alpha-stable shocks. Incomplete information leads to a non-Gaussian filtering problem. Bayesian updating generates fluctuating confidence in the agents' estimate of the persistent component of the dividends’ growth rate. Similar results are obtained with alternate distributions exhibiting fat tails (Extreme Value distribution, Pearson Type IV distribution) while they are not with a thin-tail distribution (Binomial distribution). This has the potential to generate time variation in the volatility of model-implied returns, without relying on discrete shifts in the drift rate of dividend growth rates. A test of the model using US consumption data indicates strong support in the sense that the implied returns display significant volatility persistence of a magnitude comparable to that in the data.asset pricing, incomplete information, time-varying volatility, fat tails, stable distributions

Asset Pricing with Incomplete Information In a Discrete Time Pure Exchange Economy

Abstract We study the consumption based asset pricing model in a discrete time pure exchange setting with incomplete information. Incomplete information leads to a filtering problem which agents solve using the Kalman filter. We characterize the solution to the asset pricing problem in such a setting. Empirical estimation with US consumption data indicates strong statistical support for the incomplete information model versus the benchmark complete information model. We investigate the ability of the model to replicate some key stylized facts about US equity and riskfree returns