This paper presents a simple rational expectations model of intertemporal asset pricing. It shows that heterogeneous risk aversion of investors is likely to generate declining aggregate relative risk aversion. This leads to predictability of asset returns and high and persistent volatility....

We use intraday data to compute weekly realized variance, skewness, and kurtosis for equity returns and study the realized moments' time-series and cross-sectional properties. We investigate if this week's realized moments are informative for the cross-section of next week's stock returns. We...

This paper argues that Blomberg and Hess's (Journal of International Economics 1997) finding that political variables can be used to predict exchange rate movements better than the random walk model must be seen in the context of the decade and half of previous research which failed to beat this...

We show that log-periodic power-law (LPPL) functions are intrinsically very hard to fit to time series. This comes from their sloppiness, the squared residuals depending very much on some combinations of parameters and very little on other ones. The time of singularity that is supposed to give...

In this paper we examine the validity of using one-year-ahead cash flows prediction tests as a substitute for the value relevance test of earnings. We show theoretically that the R2 of the cash flows prediction regression is contaminated by the presence of (1) noise in the cash flows and (2)...

Bitcoin has been described as a decentralized, partially anonymous, virtual currency, not backed by any government or other legal entity. Bitcoins are highly liquid, have low transaction costs, and are very volatile. This paper will look at the behavior of the value of Bitcoins, forecast the...

The purpose of this paper is to contribute to the debate on the relevance of non-linear predictors of high-frequency data in foreign exchange markets. To that end, we apply nearest-neighbour (NN) predictors, inspired by the literature on forecasting in non-linear dynamical systems, to...

The evolution of financial data shows a high degree of volatility of the series, coupled with increasing difficulties of forecasting the shorter is the time horizon, when using standard (i.e., based on linear models) forecasting methods. Some alternative forecasting methods for non-linear time...

This paper develops a simulation-based solution method to solve large state space macrofinance models using machine learning. We use a neural network (NN) to approximate the expectations in the optimality conditions in the spirit of the stochastic parameterized expectations algorithm (PEA)....