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

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.asset pricing, 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

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

The information content of currency option-implied volatilities: implications for ex-ante forecasts of global equity correlations

We use existing currency models, global capital flows, international parity, the Taylor rule, and some simplifying assumptions to derive and empirically test a link between the information contained in currency option-implied volatilities and future global equity correlations. Using data from January 1999 to May 2020, we test our hypothesis and find that exchange rate option-implied volatilities — coupled with one-period ex-post correlations — more accurately predict subsequent world equity market correlations than other models. Our findings have implications for portfolio diversification, forecasts of overall equity portfolio volatility, and portfolio optimization