Henri Nyberg

Risk-Return Tradeoff in U.S. Stock Returns over the Business Cycle

In the empirical finance literature, findings on the risk-return tradeoff in excess stock market returns are ambiguous. In this study, I develop a new qualitative response (QR)-generalized autoregressive conditional heteroskedasticity-in-mean (GARCH-M) model combining a probit model for a binary business cycle indicator and a regime-switching GARCH-M model for excess stock market return with the business cycle indicator defining the regime. Estimation results show that there is statistically significant variation in the U.S. excess stock returns over the business cycle. However, consistent with the conditional intertemporal capital asset pricing model (ICAPM), there is a positive risk-return relationship between volatility and expected return independent of the state of the economy.

Forecasting with a noncausal VAR model

Simulation-based forecasting methods for a non-Gaussian noncausal vector autoregressive (VAR) model are proposed. In noncausal autoregressions the assumption of non-Gaussianity is needed for reasons of identifiability. Unlike in conventional causal autoregressions the prediction problem in noncausal autoregressions is generally nonlinear, implying that its analytical solution is unfeasible and, therefore, simulation or numerical methods are required in computing forecasts. It turns out that different special cases of the model call for different simulation procedures. Monte Carlo simulations demonstrate that gains in forecasting accuracy are achieved by using the correct noncausal VAR model instead of its conventional causal counterpart. In an empirical application, a noncausal VAR model comprised of U.S. inflation and marginal cost turns out superior to the best-fitting conventional causal VAR model in forecasting inflation.

A Bivariate Autoregressive Probit Model: Business Cycle Linkages And Transmission Of Recession Probabilities

I propose a new binary bivariate autoregressive probit model of the state of the business cycle. This model nests various special cases, such as two separate univariate probit models used extensively in the previous literature. The parameters are estimated by the method of maximum likelihood and forecasts can be computed by explicit formulae. The model is applied to predict the U.S. and German business cycle recession and expansion periods. Evidence of in-sample and out-of-sample predictability of recession periods by financial variables is obtained. The proposed bivariate autoregressive probit model allowing links between the recession probabilities in the United States and Germany turns out to outperform two univariate models.

Noncausality and the Commodity Currency Hypothesis

This paper provides new evidence on the role of exchange rates in forecasting commodity prices. Consistent with previous studies, we find that commodity currencies hold out-of-sample predictive power for commodity prices when using standard linear predictive regressions. After we reconsider the evidence using noncausal autoregressions, which provide a better fit to the data and are able to accommodate the effects of nonlinearities and omitted variables, the predictive power of exchange rates disappears.

The Risk of Financial Crises: Does it Involve Real or Financial Factors?

The recent financial crisis appears to point to credit booms as the most important driver of crises. However, could macroeconomic factors such as income inequality potentially be the real root cause of financial crises? We explore a broad variety of financial and macroeconomic variables and employ a general-to-specific model selection process to find the most reliable predictors of financial crises in 14 developed countries over a period of more than 100 years. Our in-sample results indicate that income inequality has predictive power in addition to and above loan growth and several other financial variables. Out-of-sample forecasts for individual predictors in different time periods show that their predictive power tends to vary considerably over time, but income inequality yields individual predictive power in each forecasting period.