Do the foreign exchange rates really follow a random walk?: An empirical question revisited

The random walk behavior of nominal exchange rates using daily, weekly and monthly data and of real exchange rates using monthly data is investigated for seven major currencies and fails to be confirmed, but fails less frequently for the post-1979 period.     

Is political popularity a random walk?

This paper tets for nonstationarity in the political popularity of UK parties and governments within the context of an economic model which predicts that party support is an ARIMA (0, 1, 1) process. The hypothesis is rejected for the government's popularity but the results for individual party support are less clear out.     

Canadian economic growth: random walk or just a walk?

This paper analyses the role of shocks in Canadian economic growth since 1870. It uses a nonparametric technique to evaluate the degree of presistence of an innnovation in long-run GNP. It is found that a one percent shock to Canadian GNP changes the long-run forecast of this variable by appproximately the same amount, which is characteristic of a random walk process. It is also shown that in important periods of Canadian economic growth its GNP evolved as a random walk with constant drift. With the exception of the period 1929–42, no evidence of business cycles is found. These results lead to the conclusion that movements and oscillations in the GNP of Canada since 1870 have been primarily driven by the accumulation of shocks rather than by cyclical movements.     

Do Malaysian house prices follow a random walk? Evidence from univariate and panel LM unit root tests with one and two structural breaks

In this article we apply univariate and panel Lagrange Multiplier (LM) unit root tests with one and twostructural breaks proposed by Lee and Strazicich (2003, 2004) and Im et al. (2005) to examine housing prices for five different housing price indices (all housing, detached housing, semi-detached housing, terrace housing and high-rise housing) in 14 states of Malaysia to test whether housing prices exhibit a random walk. Our main finding from the univariate LM unit root tests is that for the vast majority of states housing prices follow a stationary process about a segmented trend. The results of the panel LM unit root tests provide overwhelming evidence that house prices are segmented trend reverting.     

The random walk as a forecasting benchmark: drift or no drift?

We examine the proposition that the random walk without drift is more powerful in predicting exchange rates than the random walk with drift. It is demonstrated that there is no theoretical reason why the random walk without drift always outperforms the random walk with drift and that this is an empirical issue. The results show that while the random walk without drift can outperform the random walk with drift in terms of the RMSE, it fails to do so in terms of the ability to predict the direction of change, measures that take into account magnitude and direction, and in terms of profitability. If the drift factor is allowed to change over time by estimating the model in time-varying parameter terms, the random walk with drift performs even better.     

Does God practice a random walk? The 'financial physics' of a nineteenth-century forerunner,Jules Regnault

We analyse the work of a neglected French economist, Jules Regnault, whose Calcul des Chances et Philosophie de la Bourse (1863) laid the basis of modern stochastic models of price behaviour and contains an anticipation of econometrics. At a time when short-term speculation was denounced as immoral, he approached this question ‘scientifically’ and constructed two models. The first one was relative to short-term speculation and took the shape of a random walk - a model used by Bachelier (1900). The second one deals with long-term speculation and aims at evaluating the mean value of the French 3 per cent bond.     

Why is it so difficult to outperform the random walk in exchange rate forecasting?

A simulation exercise is used to demonstrate the difficulty to outperform the random walk in exchange rate forecasting if forecasting accuracy is judged by the Root Mean Square Error (RMSE) or similar criteria that depend on the magnitude of the forecasting error. It is shown that, as the exchange rate volatility rises, the RMSE of the model rises faster than that of the random walk. While the literature considers this finding to be a puzzle that casts a big shadow of doubt on the soundness of international monetary economics, the results show that failure to outperform the random walk, in both in-sample and out-of-sample forecasting, should be the rule rather than the exception. However, the results do not imply that the random walk is unbeatable, because it can be easily outperformed if forecasting accuracy is judged according to criteria such as direction accuracy and profitability.     

The random walk versus unbiased efficiency: can we separate the wheat from the chaff?

Abstract:

In the issue under investigation the wheat cannot be separated from the chaff because all we have is chaff. The random walk and unbiased efficiency are equally bad forecasters that should not be used as benchmarks for measuring forecasting accuracy. The simulation and econometric results show that when two forecasters are not independent they produce forecasts of similar quality, in which case it is erroneous to use the forward rate as a forecaster and the spot rate as a benchmark. Theoretical and intuitive explanations are presented for why the random walk and unbiased efficiency produce poor-quality forecasts that are almost identical. The failure of unbiased efficiency is explained primarily in terms of the post Keynesian view of the forward exchange rate.     

The real exchange rate in Brazil Mean reversion or random walk in the long run?

This paper studies the behavior of the real exchange rate in Brazil over the longest possible period for which data are available: 1855–1990. Does the real exchange rate follow a random walk or does it revert to its mean, possibly nonstationary, level? The evidence is mixed. Formal tests can not reject the hypothesis of nonstationary behavior, although the judgement is borderline. However, time-series identification favors a stationary interpretation, and simple autoregressive processes for the real exchange rate yield extremely robust and satisfactory estimates.     

Do former athletes make better managers? Evidence from a partially adaptive grouped-data regression model

The goal of this paper is to introduce a partially adaptive estimator for the grouped-data regression model based on an error structure described by a mixture of two normal distributions. The model we introduce is easily estimated by maximum likelihood using the EM algorithm adapted from the work of Bartolucci and Scaccia (Comput Stat Data Anal 48:821–834, 2005). The partially adaptive estimator is applied to data used by Long and Caudill (Rev Econ Stat 73:525–531, 1991) to examine the impact of intercollegiate athletics on income. We estimate a variation of the original regression model and find that there is a considerable financial advantage for those male athletes now working in business management. This finding is consistent with the idea that athletes acquire team-building and organizational skills that are helpful in business.     

Do we need a global VAR model to forecast inflation and output in South Africa?

This study determines whether the global vector autoregressive (GVAR) approach provides better forecasts of key South African variables than a vector error correction model (VECM) and a Bayesian vector autoregressive (BVAR) model augmented with foreign variables. The article considers both a small GVAR model and a large GVAR model in determining the most appropriate model for forecasting South African variables. We compare the recursive out-of-sample forecasts for South African GDP and inflation from six types of models: a general 33 country (large) GVAR, a customized small GVAR for South Africa, a VECM for South Africa with weakly exogenous foreign variables, a BVAR model, autoregressive (AR) models and random walk models. The results show that the forecast performance of the large GVAR is generally superior to the performance of the customized small GVAR for South Africa. The forecasts of both the GVAR models tend to be better than the forecasts of the augmented VECM, especially at longer forecast horizons. Importantly, however, on average, the BVAR model performs the best when it comes to forecasting output, while the AR(1) model outperforms all the other models in predicting inflation. We also conduct ex ante forecasts from the BVAR and AR(1) models over 2010:Q1–2013:Q4 to highlight their ability to track turning points in output and inflation, respectively.     

Do stock market returns predict changes to output? Evidence from a nonlinear panel data model

Recent empirical work suggests a predictive relationship between stock returns and output growth. We employ quarterly data from a panel of 27 countries to test whether stock returns as useful in predicting growth. Unlike previous research, our approach allows for the possible non-linear effect of recessions on the growth-return relationship. There is strong evidence to suggest that a linear model would be misspecified and provide potentially misleading inference. Using a switching regression approach, we find evidence that returns are most useful in predicting growth when the economy is in recession.First version received: November 2002/Final version received: April 2003This paper benefited greatly from discussions with Kalvinder Shields, Mark Harris, Pete Summers, and Vance Martin. Two anonymous referees provided useful comments on an earlier version of the paper for which we are grateful. The usual disclaimer applies to any errors and omissions. Funding from The University of Melbourne greatly assisted in the completion of this paper.     

Do real exchange rates really follow threshold autoregressive or exponential smooth transition autoregressive models?

Nonlinear models, especially threshold autoregressive [TAR] and exponential smooth transition autoregressive [ESTAR] classes, are widely applied for modeling real exchange rates in order to examine the validity of purchasing power parity [PPP]. Even though the nonlinear models are theoretically well-motivated, some of the recent findings cast doubts on their relevance for real exchange rates. In particular, the nonlinear models do not necessarily yield improved out-of-sample forecasts over linear models and add little value in resolving the well-documented PPP puzzle. Utilizing a nonparametric entropy measure of dependence proposed by Granger et al. (2004), we show, in this study, that the real exchange rates from four major countries had exhibited quite strong nonlinear serial dependence, which linear autoregressive models fail to replicate. Furthermore, the nonlinear TAR and ESTAR models estimated for the real exchange rates also have some difficulty in generating significant serial dependence structure actually observed in the data. Overall, other nonlinear models than the currently entertained TAR and ESTAR should be considered to study the dynamics of the real exchange rates.     

Do cash flow imbalances facilitate leverage adjustments of Chinese listed firms? Evidence from a dynamic panel threshold model

This paper investigates the asymmetric impact of cash flow on firms’ leverage adjustments. We use a dynamic panel threshold model and estimate it with a newly-developed first-difference GMM approach, which allows endogeneity in both threshold variable and regressors. Employing data of 1054 Chinese listed firms during 2004–2016, we show that Chinese listed firms have leverage targets, towards which they adjust at an average speed of 25.9%. Moreover, firms with larger absolute cash flow adjust towards their leverage targets significantly faster than those with smaller absolute cash flow. This finding is robust to a post-financial crisis sample period, alternative measures of leverage and cash flow, and the incorporation of an additional control variable. We find evidence that cash flow imbalances facilitate leverage adjustments by reducing adjustment costs, and Chinese listed firms rely more on debt issues. These findings provide new insights on firms’ leverage adjustment and cash flow management.     

Why is it so difficult to outperform the random walk? An application of the Meese–Rogoff puzzle to stock prices

Some economists suggest that the Meese–Rogoff puzzle is equally applicable to the stock market, in the sense that no model of stock prices can outperform the random walk in out-of-sample forecasting. We argue that this is not a puzzle and that we should expect nothing, but this result if forecasting accuracy is measured by the root mean square error (RMSE) and similar metrics that take into account the magnitude of the forecasting error only. We demonstrate by using two models for dividend-paying and nondividend-paying stocks that as price volatility rises, the RMSE of the random walk rises, but the RMSE of the model rises even more rapidly, making it unlikely for the model to outperform the random walk.