The nonlinear dynamic relationship of exchange rates: Parametric and nonparametric causality testing

The present study investigates the linear and nonlinear causal linkages among six currencies denoted relative to United States dollar (USD), namely Euro (EUR), Great Britain Pound (GBP), Japanese Yen (JPY), Swiss Frank (CHF), Australian Dollar (AUD) and Canadian Dollar (CAD). The data spans two periods between 3/20/1991 and 3/20/2007. We apply a new nonparametric test for Granger non-causality by Diks and Panchenko [Diks, C., Panchenko, V., 2005. A note on the Hiemstra–Jones test for Granger noncausality. Studies in Nonlinear Dynamics and Econometrics 9 (art. 4); Diks, C., Panchenko, V., 2006. A new statistic and practical guidelines for nonparametric Granger causality testing. Journal of Economic Dynamics & Control 30, 1647–1669] and the linear Granger test on the return time series. To detect strictly nonlinear causality, we examine the pairwise VAR-filtered residuals as well as in a six-variate formulation. We find remaining significant bi- and uni-directional causal nonlinear relationships in the series. Finally, we investigate causality after controlling for conditional heteroskedasticity using a GARCH–BEKK model. Whilst the nonparametric test statistics are smaller in some cases, significant nonlinear causal linkages persisted even after GARCH filtering during both periods. This indicates that currency returns may exhibit asymmetries and statistically significant higher-order moments.     

Does inflation cause gold market price changes? evidence on the G7 countries from the tests of nonparametric quantile causality in mean and variance

This article utilizes the newly proposed nonparametric causality-in-quantiles test to examine the predictability of mean and variance of changes in gold prices based on inflation for G7 countries. The causality-in-quantiles approach permits us to test for not only causality in mean but also causality in variance. We start our investigation by utilizing tests for nonlinearity. These tests identify nonlinearity, showing that the linear Granger causality tests are subject to misspecification error. Unlike tests of misspecified linear models, our nonparametric causality-in-quantiles tests find causality in mean and variance from inflation to gold market price changes between the 0.20 quantile and the 0.70 quantile, implying that very low- and high-price changes in gold markets are not related to inflation. These changes should be related to other sources, such as financial shocks and exchange market shocks. We find support that gold serves as a hedge against inflation, but only in the mid-quantile ranges, i.e. quantiles from 0.20 to 0.70. Our results show that gold does not serve as a hedge against inflation during periods when gold market price changes are very low or very high, which are respectively quiet and highly volatile periods.     

Testing misspecified non-nested factor demand systems: Some Monte Carlo results

Empirical factor demand analysis is a topic in which a choice must be made among several competing non-nested functional forms. Each of the commonly used factor demand systems, such as Translog, Generalized Leontief, Quadratic, and Generalized McFadden, exhibits statistical inadequacy when tested for the absence of residual autocorrelation, homoskedasticity and normality. This does not necessarily imply that the whole system is invalid, especially if misspecification affects only a subset of the equations forming the entire system. Since there is no theoretical guidance on how to select the model which is most able to capture the relevant features of the data, formal testing procedures can be useful. In the literature, paired and joint univariate non-nested tests (e.g. Davidson-MacKinnon's J and P tests, Bera-McAleer test and Barten-McAleer test) have been discussed at length, whereas virtually no attention has been paid to multivariate non-nested tests. In this paper we show how multivariate non-nested tests can be derived from their univariate counterparts, and we apply these tests to compare alternative factor demand systems. Since the outcome of a non-nested test is likely to be influenced by the type of misspecification affecting the competing models, we investigate the empirical performance of a multivariate non-nested test using new Monte Carlo experiments. The competing models are compared indirectly via a statistically adequate model which is considered as if it were the DGP. Under such circumstances, the distribution of the non-nested test of an incorrect null, when it is evaluated at the DGP, tends to be closer to the distribution of the test under the correct null, at least in small samples. A non-nested test is expected to select the model which is closest to the DGP. Moreover, we investigate the empirical behaviour of a non-nested test when the DGP has, in turn, autoregressive, heteroskedastic and non-normal errors. Finally, we provide some suggestions for the applied researcher. First version received: November 1999/Final version received: May 2001     

Dynamic relationships between crude oil prices and socially responsible investing in Brazil: evidence for linear and non-linear causality

Recent years have witnessed an increasing interest in socially responsible investing (SRI), reflecting investors’ growing awareness of social, environmental, ethical and corporate governance issues. At the same time, the effect of oil price shocks on stock price returns has become a prominent issue due to surges in energy prices. Using the Brazilian corporate sustainability index (ISE) as a benchmark for socially responsible investments in the Brazilian stock market, the present study extends the understandings on the impact of oil prices on stock price behaviour, focusing on a new class of assets: those from socially responsible firms. To this end, apart from conventional linear causality approaches, we apply a nonparametric test by Diks and Panchenko (DP) on daily data spanning from January 2008 to December 2015 to test for non-linear causality, before and after controlling for conditional heteroscedasticity. Our findings show that, in spite of their efforts to become more socially responsible, firms that have adhered to the ISE in recent years are influenced by crude oil spot prices, especially the WTI crude. In line with previous studies, we also provide consistent evidence that the Brazilian stock market, as a whole, is associated with the international crude oil market.     

On the Causality Between Exchange Rates and Stock Prices: A Note

This study uses a new Granger non–causality testing procedure developed by Toda and Yamamoto (1995) to contribute to the debate on exchange rates and stock prices in Sweden. It examines a possible causal relation between these variables in a vector autoregression (VAR) model. The results show that Granger causality is unidirectional running from stock prices to effective exchange rates. The results also reveal that an increase in Swedish stock prices is associated with an appreciation of the Swedish krona. Special attention is given to the estimation methodology and the lag choosing process.     

PREDICTING STOCK RETURNS AND VOLATILITY WITH INVESTOR SENTIMENT INDICES: A RECONSIDERATION USING A NONPARAMETRIC CAUSALITY‐IN‐QUANTILES TEST

Evidence of monthly stock returns predictability based on popular investor sentiment indices, namely S^BW and S^PLS as introduced by Baker and Wurgler (2006, 2007) and Huang et al. (2015) respectively are mixed. While, linear predictive models show that only S^PLS can predict excess stock returns, nonparametric models (which accounts for misspecification of the linear frameworks due to nonlinearity and regime changes) finds no evidence of predictability based on either of these two indices for not only stock returns, but also its volatility. However, in this paper, we show that when we use a more general nonparametric causality‐in‐quantiles model of Balcilar et al., (forthcoming), in fact, both S^BW and S^PLS can predict stock returns and its volatility, with S^PLS being a relatively stronger predictor of excess returns during bear and bull regimes, and S^BW being a relatively powerful predictor of volatility of excess stock returns, barring the median of the conditional distribution.     

A Pearson's test for symmetry with an application to the Spanish business cycle

In this paper, we look for asymmetries in the Spanish business cycle. To that end, we firstly propose an easy nonparametric testing procedure to test for symmetry based on a Pearson's chi-squared statistic, which we call P-test. Then, we test for two popular forms of asymmetry, deepness and steepness, using a battery of nonparametric tests. In addition, we analyse possible complementarities between the tests used in this paper, and we compute p-value adjustments for multiple tests.     

A sneeze in the U.S., a cough in Japan,but pneumonia in Taiwan? An application of the Markov-Switching vector autoregressive model

This paper examines the lead–lag relationships among the output of Taiwan, Japan and the U.S. Three testing methods are employed: the traditional linear Granger causality test, Hiemstra and Jones' [Hiemstra, C., Jones, J.D., 1994. Testing for linear and nonlinear Granger causality in the stock price-volume relation. Journal of Finance 49, 1639–1664] nonlinear Granger causality test and Warne's [Warne, A., 2000. Causality and regime inference in a Markov-S switching VAR, Working Paper no. 118, Sveriges Riksbank, Stockholm.] Granger causality test under the Markov-Switching model. We find that the causal ordering is unclear and depends on the model we used. Because Markov-Switching model imposes few restrictions in estimation, we tend to use its estimated results but bear in mind that the evidence is sensitive. First, the common shock hypothesis is found that most probably exists between Taiwan and the U.S. Next, we conclude that Japan tends to lead Taiwan's output, to a certain extent. Last, there is no causal ordering between the U.S. and Japan economies.     

The price–volume relationship in the crude oil futures market Some results based on linear and nonlinear causality testing

This article presents some evidence for the presence of a causal relationship between price and volume in the crude oil futures market. The results of linear causality testing reveal the presence of causality running from volume to price but not vice versa. While the results of testing for nonlinear causality are inconsistent, most of the evidence shows that causality runs in both directions. In general, there is evidence for the sequential information arrival hypothesis and the noise trading model, but not for market efficiency. There is also some evidence for the presence of a maturity or a liquidity effect. Finally, there is some variation in the results, depending on the sample period.     

Energy prices and exchange rates of the U.S. dollar: Further evidence from linear and nonlinear causality analysis

The causality relationships between energy prices and exchange rates have been investigated in many existing studies. Previous investigations ignore the possible nonlinear behaviors which may be caused by asymmetry, persistence or structural breaks. To fill this gap, we apply both linear and nonlinear causality tests to examine the causal relationships between energy prices and exchange rates of the U.S. dollar. Our results show that in the period before recent financial crisis, unidirectional linear causality running from petroleum prices to exchange rates and unidirectional nonlinear causality running from exchange rates to natural gas prices are revealed. In the period after the financial crisis, the bidirectional nonlinear causality relationships between petroleum prices and exchange rates can be found and there are no causality between exchange rates and natural gas prices. Moreover, we examine the source of nonlinear behaviors of causality relationships. Our evidence indicates that both volatility spillover and regime shift contribute to nonlinear causality and the explanation power of the former one is much stronger.     

Does Financial Development 'Lead' Economic Growth?

We use the Toda & Yamamoto (1995) causality testing procedure to investigate the relationship, if any, between financial development and economic growth.We use quarterly data from 19 OECD countries and China, and use total credit and interest spread as indicators of financial development. We also consider the impact of financial development on investment and productivity. We find meagre evidence that financial development 'leads' economic growth, either directly or indirectly. This casts further doubt on claims that financial development is a necessary and perhaps sufficient precursor to economic growth.     

A non-linear approach for predicting stock returns and volatility with the use of investor sentiment indices

The popular sentiment-based investor index S^BW introduced by Baker and Wurgler (2006, 2007) is shown to have no predictive ability for stock returns. However, Huang et al. (2015) developed a new investor sentiment index, S^PLS, which can predict monthly stock returns based on a linear framework. However, the linear model may lead to misspecification and lack of robustness. We provide statistical evidence that the relationship between stock returns, S^BW and S^PLS is characterized by structural instability and inherent nonlinearity. Given this, using a nonparametric causality approach, we show that neither S^BW nor S^PLS predicts stock market returns or even its volatility, as opposed to previous empirical evidence.     

Exogeneity, causality, and co-breaking in economic policy analysis of a small econometric model of money in the UK

Since the objective of economic policy is to change target variables in the DGP, when economic policy analysis uses an econometric model, it is important that the model delivers reliable inferences about policy responses in the DGP. This requires that the model be congruent and encompassing, and hence exogeneity, causality, cointegration, co-breaking, and invariance all play major roles. We discuss these roles in linear cointegrated VARs, prior to illustrating their importance in a bivariate model of money and interest rates in the UK over the last century.Financial support from the UK Economic and Social Research Council under grant L116251015, and the EUI Research Council grantEconometric Modelling of Economic Time Series, is gratefully acknowledged. Early research for the paper was done whilst Mizon was Visiting Fellow in the Economics Program of the RSSS at ANU, where he benefited from the excellent research environment and discussions with Adrian Pagan. We are grateful to Hans-Martin Krolzig for helpful discussions on the topic. We also thank members of the Research Department, Norges Bank, Oslo, the particpants at theWorkshop on Money Demand in Europe, Humboldt University, October 1997, two referees, and the editors Helmut Lütkepohl and Jürgen Wolters for their valuable comments. The data may be obtained from the internet, http://wotan.wiwi.huberlin.de/oekonometric./engl/data.html     

Did Baltic stock markets offer diversification benefits during the recent financial turmoil? Novel evidence from a nonparametric causality-in-quantiles test

Motivated by financial liberalization investors seek for new investment opportunities through international portfolio diversification. To this end we explore any asymmetric causal relationship between developed European stock markets (Germany, France and UK) and emerging Baltic markets namely; Estonia, Latvia and Lithuania. Our analysis focuses on the period before and after countries’ EU accession and pre- and post the global financial crisis. For this purpose, both the standard parametric test for causality and a novel nonparametric test for causality-in-quantiles are employed. The results of both the parametric and nonparametric Granger causality test support a causal relationship in mean that runs from all of the major markets to the Baltic markets across both samples. The results imply the existence of significant nonlinear return and volatility spillover from European markets to Baltic markets. Policy implications for international investors are also discussed.     

Does fine wine price contain useful information to forecast GDP? Evidence from major developed countries

This study provides the first attempt to examine the ability of the price of fine wine to forecast the Gross Domestic Product (GDP) for the major developed countries. Considering the limitation of a linear Granger causality test in detecting nonlinear causal relationships, a nonlinear Granger causality test is also employed. The results from our nonlinear causality test show that this new variable contains useful information to forecast GDP for the US, the UK, and Australia, suggesting that we may include it as a forecasting variable in GDP forecasting models, especially nonlinear models, for these three countries.     

中国粮食价格与CPI的关系(1996—2008)——基于非线性关联积分的因果检验

当前,中国特色的市场化道路步入了发展的快车道,粮食市场与宏观经济的相互关系也表现得更为复杂。本文尝试引入一种不基于任何函数关系假定前提的非线性检验模型,对1996—2008年间中国粮食价格与CPI的关系进行检验和解释,以期更为准确地理解和阐释两者之间的关系,为制定宏观经济政策,实现国家宏观调控目标,提供可资借鉴与参考的理论解释。本文的检验结果表明,中国粮食价格和CPI之间存在双向因果关系,但在时效和强度上存在较大差异。CPI对粮食价格有半年左右的显著影响,粮食价格对CPI仅表现为滞后1个月的短期效应。最后在给出理论解释的基础上,本文提出了相关政策建议。     

Nonparametric neural network modeling of hedonic prices in the housing market

This article addresses the contribution to hedonic modeling of a nonparametric approach based on artificial neural network (ANN) regressions. ANNs provide consistent estimates for the hedonic price of each attribute and permit a number of hypotheses on the hedonic price relationship to be tested nonparametrically. In particular, we exploit results by Stinchcombe and White (Econom Theory 14:295?C324, 1998) in order to carry out misspecification testing in linear and semiloglinear hedonic models. The same approach directly applies to testing misspecification of any parametric specification for the hedonic relationship. A nonparametric significance test for the variables in the hedonic model is also proposed. The test extends the approach developed by Racine (J Bus Econ Stat 15(3):369?C378, 1997) in kernel-based nonparametric testing to ANN-based inference. The finite sample performance of the proposed tests is analyzed through Monte Carlo experiments, and simulation-based algorithms for computation of the null distribution of the tests are proposed. Then, the performance of three classes of regression models??linear, semi-log, and ANNs??applied to hedonic price modeling in a Spanish regional housing market is compared. Our results indicate the presence of nonlinear behavior, as predicted by economic theory, with the ANN-based tests detecting statistically significant evidence of misspecification??both in the linear and the semilog specifications??and ANN regressions providing moderate improvement of predictive performance.     

The role of fiscal policy in the St. Louis model: Nonparametric estimates for a small open economy

This paper reevaluates the efficacy of monetary and fiscal policies and bidirectional causality between income and each of the policy instruments used in the St. Louis model for aggregate demand using nonparametric (or infinite parametric) spectral methods. We proceed by estimating the strength of the correlations (or partial coherences) between income and each of the policy instruments over various frequencies. Then we obtain the corresponding band regression and Hannan's efficient estimates of both the lead and lag coefficients in the St. Louis model. The analysis is carried out with seasonally adjusted quarterly data and is divided into the flexible, fixed, and managed flexible exchange rate regimes. We find that while estimates from parametric regressions yield the standard conclusions for the St. Louis model, results from the nonparametric analysis are quite different. Specifically, the results of our analysis reveal that (i) both monetary and fiscal instruments are strongly correlated with income over cycles of 10 quarters or longer for the most recent period of the managed flexible exchange rate regime, and (ii) bidirectional causality exists between income and the fiscal policy instrument. These results suggest that both monetary and fiscal policy have a long-lasting effect on aggregate demand and that bidirectional causality exists between income and policy instruments. An explanation for the existence of bidirectional causality might be that the Canadian government generally pursued a purposeful discretionary fiscal policy during the post-World War II period. Furthermore, it appears that discretionary policy action may have been anticipated by rational, farsighted, and forward-looking economic agents. Finally, our results for the flexible exchange rate and fixed rate regimes are in agreement with the Mundell-Fleming view of the role of monetary fiscal policy in an open economy.     

Asymmetric causality tests with an application

This article argues that there are several logical reasons for the existence of asymmetric causal effects that need to be taken into account but usually are neglected in the literature. It suggests allowing for asymmetry in the causality testing by using the cumulative sums of positive and negative shocks. A bootstrap simulation approach with leverage adjustment is used to generate critical values that are robust to non-normality and time-varying volatility. An application to the efficient market hypothesis in the UAE is provided. The results show that the equity market is informationally efficient with regard to the oil shocks regardless if these shocks are positive or negative.     

Nonlinear dynamics and the exports–output growth nexus

The untested assumption of a linear relationship between exports and output growth in previous empirical investigations may lead to invalid inference if the actual relationship is nonlinear. This paper re-examines the relationship between exports and economic growth in five industrialized economies (Canada, Italy, Japan, UK, and the US) with emphasis on the effect of nonlinearities on the causal relationships. Results from linearity tests show that nonlinearities do exist in the dynamic relationship between exports and GDP growth. Nonlinear smooth transition autoregressive (STAR) model results suggest that nonlinear Granger causality flows from exports to output growth and vice versa. Predictive accuracy tests further confirm the appropriateness of the nonlinear models over the linear model specification.