Forecasting with preliminary data

This paper examines several methods to forecast revised US trade balance figures by incorporating preliminary data. Two benchmark forecasts are considered: one ignoring the preliminary data and the other applying a combination approach; with the second outperforming the first. Competing models include a bivariate AR error-correction model and a bivariate AR error-correction model with GARCH effects. The forecasts from the latter model outperforms the combination benchmark for the one-step forecast case only. A restricted AR error-correction model with GARCH effects is discovered to provide the best forecasts. © 1997 John Wiley & Sons, Ltd.     

Daily FX Volatility Forecasts: Can the GARCH(1,1) Model be Beaten using High‐Frequency Data?

Volatility forecasting remains an active area of research with no current consensus as to the model that provides the most accurate forecasts, though Hansen and Lunde (2005) have argued that in the context of daily exchange rate returns nothing can beat a GARCH(1,1) model. This paper extends that line of research by utilizing intra‐day data and obtaining daily volatility forecasts from a range of models based upon the higher‐frequency data. The volatility forecasts are appraised using four different measures of ‘true’ volatility and further evaluated using regression tests of predictive power, forecast encompassing and forecast combination. Our results show that the daily GARCH(1,1) model is largely inferior to all other models, whereas the intra‐day unadjusted‐data GARCH(1,1) model generally provides superior forecasts compared to all other models. Hence, while it appears that a daily GARCH(1,1) model can be beaten in obtaining accurate daily volatility forecasts, an intra‐day GARCH(1,1) model cannot be. Copyright © 2011 John Wiley & Sons, Ltd.     

Google Trends and the forecasting performance of exchange rate models

In this paper, we use Google Trends data for exchange rate forecasting in the context of a broad literature review that ties the exchange rate movements with macroeconomic fundamentals. The sample covers 11 OECD countries’ exchange rates for the period from January 2004 to June 2014. In out‐of‐sample forecasting of monthly returns on exchange rates, our findings indicate that the Google Trends search query data do a better job than the structural models in predicting the true direction of changes in nominal exchange rates. We also observed that Google Trends‐based forecasts are better at picking up the direction of the changes in the monthly nominal exchange rates after the Great Recession era (2008–2009). Based on the Clark and West inference procedure of equal predictive accuracy testing, we found that the relative performance of Google Trends‐based exchange rate predictions against the null of a random walk model is no worse than the purchasing power parity model. On the other hand, although the monetary model fundamentals could beat the random walk null only in one out of 11 currency pairs, with Google Trends predictors we found evidence of better performance for five currency pairs. We believe that these findings necessitate further research in this area to investigate the extravalue one can get from Google search query data.     

Non-linearity and exchange rates

The conditional heteroscedastic models (CHM) are commonly used to describe the dynamics of nominal exchange rates. However, some investigations have already pointed out that the CHMs are not able to fully explain all non-linearities exhibited by the exchange rate series. This paper analyses the performance of univariate CHMs in modelling the non-linearities of nominal exchange rate series vis-à-vis the US dollar. Twelve currencies are examined on a weekly basis: The Belgian, Swiss and French francs; the Canadian dollar; the German mark; the Danish and Norwegian kroners; the British and Irish pounds; the Italian lira; the Japanese yen and the Dutch guilder. The CHMs captured in a satisfactory way the volatility clustering of the series and show volatility peaks in historically nervous periods of the international market. Moreover, the results of the BDS tests for whiteness applied to the standardized residuals show the good specification of the models. Copyright © 1998 John Wiley & Sons, Ltd.     

Forecasting exchange rates using cointegration models and intra‐day data

We present a cointegration analysis on the triangle (USD–DEM, USD–JPY, DEM–JPY) of foreign exchange rates using intra‐day data. A vector autoregressive model is estimated and evaluated in terms of out‐of‐sample forecast accuracy measures. Its economic value is measured on the basis of trading strategies that account for transaction costs. We show that the typical seasonal volatility in high‐frequency data can be accounted for by transforming the underlying time scale. Results are presented for the original and the modified time scales. We find that utilizing the cointegration relation among the exchange rates and the time scale transformation improves forecasting results. Copyright © 2002 John Wiley & Sons, Ltd.     

What can we learn from the fifties?

Economists have increasingly elicited probabilistic expectations from survey respondents. Subjective probabilistic expectations show great promise to improve the estimation of structural models of decision making under uncertainty. However, a robust finding in these surveys is an inappropriate heap of responses at “50%,” suggesting that some of these responses are uninformative. The way these 50s are treated in the subsequent analysis is of major importance. Taking the 50s at face value will bias any aggregate statistics. Conversely, deleting them is not appropriate if some of these answers do convey some information. Furthermore, the attention of researchers is so focused on this heap of 50s that they do not consider the possibility that other answers may be uninformative as well. This paper proposes to take a fresh look at these questions using a new method based on weak assumptions to identify the informativeness of an answer. Applying the method to probabilistic expectations of equity returns in three waves of the Survey of Economic Expectations in 1999–2001, I find that: (i) at least 65% of the 50s convey no information at all; (ii) it is the answer most often provided among the answers identified as uninformative; (iii) but even if the 50s are a major contributor to noise, they represent at best 70% of the identified uninformative answers. These findings have various implications for survey design.     

Why do regime‐switching models forecast so badly?

Most non‐linear techniques give good in‐sample fits to exchange rate data but are usually outperformed by random walks or random walks with drift when used for out‐of‐sample forecasting. In the case of regime‐switching models it is possible to understand why forecasts based on the true model can have higher mean squared error than those of a random walk or random walk with drift. In this paper we provide some analytical results for the case of a simple switching model, the segmented trend model. It requires only a small misclassification, when forecasting which regime the world will be in, to lose any advantage from knowing the correct model specification. To illustrate this we discuss some results for the DM/dollar exchange rate. We conjecture that the forecasting result is more general and describes limitations to the use of switching models for forecasting. This result has two implications. First, it questions the leading role of the random walk hypothesis for the spot exchange rate. Second, it suggests that the mean square error is not an appropriate way to evaluate forecast performance for non‐linear models. Copyright © 1999 John Wiley & Sons, Ltd.