An empirical examination of heterogeneity and switching in foreign exchange markets

© 2013 Elsevier B.V. In order to study the expectation formation of financial institutions in the foreign exchange market we develop and apply a recursive selection and estimation algorithm to a dataset of surveyed foreign exchange market expectations. Responses are classified into two groups and forecasting models are endogenously determined within the groups. Estimation results reveal that a fundamentalist-chartist model is capable of explaining a large portion of foreign exchange market expectations. Fundamentalists are found to have mean-reverting expectations whereas chartists have contrarian expectations. Allowing panelists to switch between models significantly improves the fit of the model, especially at the relatively shorter forecast horizons. We find that the fundamentalist model is increasingly used as the forecast horizon extends. Finally, results indicate that model choice is based on a combination of period-specific and individual-specific determinants

Institutional difference and outward FDI: Evidence from China

This paper investigates the impact of institutional difference on China’s outward foreign direct investment (OFDI) through a gravity model. Our estimations are based on a large panel of 150 countries over the period 2003-2015. The results show that the institutional differences of government effectiveness and control of corruption between China and a host country have a statistically significant negative effect on China’s OFDI. In addition, our empirical evidence suggests that the ‘One Belt One Road’ policy does not have the expected positive effect on China’s OFDI. Consistent results are obtained from a set of robustness tests. Our findings provide a reasonable guideline for countries aiming to attract Chinese OFDI or seeking factors to boost it

Non-standard errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Heterogeneous expectations in asset pricing: Empirical evidence from the S&P500

This paper empirically assesses heterogeneous expectations in asset pricing. We use a maximum likelihood approach on S&P500 data to estimate a structural model. Our empirical results are consistent with a market populated with fundamentalists and chartists. In addition, agents switch between these groups conditional on their previous performance. The results imply that the model can explain the inflation and deflation of bubbles. Finally, the model is shown to be in the deterministically stable region, but produces stochastic bubbles of similar length and magnitude as empirically observed. © 2014 Elsevier B.V

Volatility expectations and disagreement

This paper examines the use of survey-based measures in volatility forecasting. We argue that an aggregate volatility forecast built up from individual forecasts should be the sum of individual expected volatilities and the dispersion in mean return forecasts. We use data coming from a repeated survey to capture volatility expectations and mean returns of investors, and to produce aggregate volatility forecasts. Our survey-based volatility forecasts are consistent and quantitatively similar with forecasts based on GARCH and implied volatility models. This result is robust to both in-sample and out-of-sample comparisons and in response to news.</p