Intrinsic Bubbles and Fat Tails in Stock Prices

We study the constant discount rate present value model for stock pricing in a stochastic setting where the exogenous dividend stream is modeled as a random walk with innovations drawn from the family of stable distributions. We derive an exact analytical solution for the fundamental stock price. We evaluate the ability of the model fundamentals and the dividends-driven intrinsic bubbles to explain the observed variation in annual US stock prices. We compare results obtained in this setting with those from the traditional model where all stochastic processes are driven by Gaussian shocks.Stock prices, present-value model, intrinsic bubbles, fat tails, normal distributions, stable distributions

Comparison of Two Alternative Approaches to Modeling Level Shifts in the Presence of Outliers

We study alternative models for capturing abrupt structural changes (level shifts) in a times series. The problem is confounded by the presence of transient outliers. We compare the performance of non-Gaussian time-varying parameter models and multiprocess mixture models within a Monte Carlo experimental setup. Our findings suggest that once we incorporate shocks with thick-tailed probability distributions, the superiority of the multiprocess mixture models over the time-varying parameter models, reported in an earlier study, disappears. The behavior of the two models, both in adapting to level shifts and in reacting to transient outliers, is very similar.time-varying parameter (TVP) models, non-Gaussian state space models, multiprocess mixture models, level shifts, outliers

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

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

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

On Business Cycle Asymmetries in G7 Countries

We investigate whether business cycle dynamics in seven industrialized countries (the G7) are characterized by asymmetries in conditional mean. We provide evidence on this issue using a variety of time series models. Our approach is fully parametric. Our testing strategy is robust to any conditional heteroskedasticity, outliers, and / or long memory that may be present. Our results indicate fairly strong evidence of nonlinearities in the conditional mean dynamics of the GDP growth rates for Canada, Germany, Italy, Japan, and the US. For France and the UK, the conditional mean dynamics appear to be largely linear. Our study shows that while the existence of conditional heteroskedasticity and long memory does not have much affect on testing for linearity in the conditional mean, accounting for outliers does reduce the evidence against linearity.business cycles, asymmetries, nonlinearities, conditional heteroskedasticity, long memory, outliers, real GDP, stable distributions

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

News or Noise? Signal Extraction Can Generate Volatility Clusters From IID Shocks

We develop a framework in which information about firm value is noisily observed. Investors are then faced with a signal extraction problem. Solving this would enable them to probabilistically infer the fundamental value of the firm and, hence, price its stocks. If the innovations driving the fundamental value of the firm and the noise that obscures this fundamental value in observed data come from non-Gaussian thick-tailed probability distributions, then the implied stock returns could exhibit volatility clustering. We demonstrate the validity of this effect with a simulation study.stock returns, volatility clusters, GARCH processes, signal extraction, thick-tailed distributions, simulations

On the Economic Impact of Modeling Non-Linearities: The Asset Pricing Example

We investigate the economic importance of modeling non-linearities in the dynamics of exogenous processes on the implied moments of endogenous variables in the context of the consumption-based asset pricing model. For this purpose, we model the endowment process alternatively as a linear autoregression and as a non-linear threshold autoregression. The asset pricing model with non-linear endowment is solved using quadrature techniques. A comparison of the moments of the model-implied rates of return in the two cases suggests that the economic impact of modeling non-linearities is less than 0.01 percent per annum.asset pricing, rates of return, non-linearities, threshold autoregressions, numerical solutions

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