Expected Returns and Expected Dividend Growth

We investigate a consumption-based present value relation that is a function of future dividend growth. Using data on aggregate consumption and measures of the dividend payments from aggregate wealth, we show that changing forecasts of dividend growth make an important contribution to fluctuations in the U.S. stock market, despite the failure of the dividend-price ratio to uncover such variation. In addition, these dividend forecasts are found to covary with changing forecasts of excess stock returns. The variation in expected dividend growth we uncover is positively correlated with changing forecasts of excess returns and occurs at business cycle frequencies, those ranging from one to six years. Because positively correlated fluctuations in expected dividend growth and expected returns have offsetting affects on the log dividend-price ratio, the results imply that both the market risk-premium and expected dividend growth vary considerably more than what can be revealed using the log dividend-price ratio alone as a predictive variable.

TESTING MARKET EFFICIENCY VIA DECOMPOSITION OF STOCK RETURN. APPLICATION TO ROMANIAN CAPITAL MARKET

In this paper we are investigating the market efficiency using a model which decomposes the stock return into two components: a stochastic trend and a white noise component. This model is tested for the Romanian Capital Market, considering the time series of BET (Bucharest Exchange Trade) Index. The conclusion is that for our data sample we cannot reject the efficient market hypothesis for Romanian Capital Market. Classificaefficient market hypothesis, random walk, stochastic trend, ARIMA models, Romanian Capital Market, BET.tion-JEL: C42, G14

Are property prices non-linear? An investigation of the behaviour of US REITs and UK property company shares

Linear models of market performance may be misspecified if the market is subdivided into distinct regimes exhibiting different behaviour. Price movements in the US Real Estate Investment Trusts and UK Property Companies Markets are explored using a Threshold Autoregressive (TAR) model with regimes defined by the real rate of interest. In both US and UK markets, distinctive behaviour emerges, with the TAR model offering better predictive power than a more conventional linear autoregressive model. The research points to the possibility of developing trading rules to exploit the systematically different behaviour across regimes

Behavioral Heterogeneity in Stock Prices

We estimate a dynamic asset pricing model characterized by heterogeneous boundedly rational agents. The fundamental value of the risky asset is publicly available to all agents, but they have different beliefs about the persistence of deviations of stock prices from the fundamental benchmark. An evolutionary selection mechanism based on relative past profits governs the dynamics of the fractions and switching of agents between different beliefs or forecasting strategies. A strategy attracts more agents if it performed relatively well in the recent past compared to other strategies. We estimate the model to annual US stock price data from 1871 until 2003. The estimation results support the existence of two expectation regimes, and a bootstrap F-test rejects linearity in favor of our nonlinear two-type heterogeneous agent model. One regime can be characterized as a fundamentalists regime, because agents believe in mean reversion of stock prices toward the benchmark fundamental value. The second regime can be characterized as a chartist, trend following regime because agents expect the deviations from the fundamental to trend. The fractions of agents using the fundamentalists and trend following forecasting rules show substantial time variation and switching between predictors. The model offers an explanation for the recent stock prices run-up. Before the 90s the trend following regime was active only occasionally. However, in the late 90s the trend following regime persisted and created an extraordinary deviation of stock prices from the fundamentals. Recently, the activation of the mean reversion regime has contributed to drive stock prices back closer to their fundamental valuation.

Complexity, Evolution and Learning: a simple story of heterogeneous expectations and some empirical and experimental validation.

This note discusses complexity models in economics. A key feature of these models is that agents have heterogeneous expectations, disciplined by adaptive learning and evolutionary selection. Agents adapt their rules based upon past observations and switch between different forecasting heuristics based upon strategy performance. We discuss how these models match empirical facts as well as laboratory experiments with human subjects and how this approach may tame the ``wilderness of bounded rationality''.

Volatility forecasts: a continuous time model versus discrete time models

This paper compares empirically the forecasting performance of a continuous time stochastic volatility model with two volatility factors (SV2F) to a set of alternative models (GARCH, FIGARCH, HYGARCH, FIEGARCH and Component GARCH). We use two loss functions and two out-of-sample periods in the forecasting evaluation. The two out-of-sample periods are characterized by different patterns of volatility. The volatility is rather low and constant over the first period but shows a significant increase over the second out-of-sample period. The empirical results evidence that the performance of the alternative models depends on the characteristics of the out-ofsample periods and on the forecasting horizons. Contrarily, the SV2F forecasting performance seems to be unaffected by these two facts, since the model provides the most accurate volatility forecasts according to the loss functions we consider

The Italian Treasury Econometric Model (ITEM)

In this paper, we provide a description of the Italian Treasury Econometric Model (ITEM). We illustrate its general structure and model properties, especially with regard to the economy's response to changes in policy and in other dimensions of the economic environment. The model has a quarterly frequency and includes 371 variables. Out of these, 124 are exogenous and 247 endogenous. The model structure features 36 behavioral equations and 211 identities. One of the key features of the model is the joint representation of the economic environment on both the demand and the supply side. Since it is designed for the needs of a Treasury Department, its public finance section is developed in great detail, both on the expenditure and revenue side. It also features a complete modeling of financial assets and liabilities of each institutional sector. After documenting the model structure and the estimation results, we turn to the outcomes of model simulation and ascertain the model properties. In ITEM the shocks that generate permanent effects on output are associated with: a) variation of variables that affect the tax wedge in the labor market and the user cost of capital; b) labor supply change; c) variation in the trend component of TFP (technical progress). By contrast, variables that exert their effects on the demand side have only temporary effects on output. We also perform in-sample dynamic simulation of the model. This allows us to derive simulated values of all the endogenous variables which can be compared with the corresponding actual values. This allows us to appraise, for each aggregate, whether the simulated values track the observed data.Macroeconometric models; Economic Policy

Forecasting the equity risk premium: The role of technical indicators

Ministry of Education, Singapore under its Academic Research Funding Tier

Dispersion and Volatility in Stock Returns: An Empirical Investigation

This paper studies three different measures of monthly stock market volatility: the time-series volatility of daily market returns within the month; the cross-sectional volatility or 'dispersion' of daily returns on industry portfolios, relative to the market, within the month; and the dispersion of daily returns on individual firms, relative to their industries, within the month. Over the period 1962-97 there has been a noticeable increase in firm-level volatility relative to market volatility. All the volatility measures move together in a countercyclical fashion. While market volatility tends to lead the other volatility series, industry-level volatility is a particularly important leading indicator for the business cycle.