Search results

The purpose of this paper is to develop a new framework to test the hypothesis that portfolio model predicts a negative correlation between stock prices and exchange rates in a trivariate transmission channel for foreign portfolio equity investment.

This paper utilizes panel data for eight economies to extend the Dumitrescu and Hurlin (2012) Granger non-causality test of heterogeneous panels to a trivariate model by integrating the Toda and Yamamoto (1995) approach to Granger causality.

The evidence suggests that stock prices Granger-cause exchange rates and portfolio equity flows Granger-cause exchange rates. However, the overall panel evidence casts doubt on the explicit trivariate model of portfolio balance model. The study shows that Indonesia may be the only case where stock prices affect exchange rates through portfolio equity flows.

The proposed test does not account for potential asymmetries or structural shifts associated with the crisis period. To isolate the impact of the Asian Financial crisis, this paper rather splits the sample period into two sub-periods: pre- and post-crises. The sample period and countries are also limited due to the use of the balance of payment statistics.

The study casts doubt on the maintained hypothesis of a trivariate transmission channel, as posited by the portfolio model. Policy makers of an economy may integrate capital market and fiscal policies in order to maintain stable exchange rate.

This paper integrates a portfolio equity inflow variable into a single framework with stock price and exchange rate variables. It extends the Dumitrescu and Hurlin’s (2012) bivariate stationary Granger non-causality test in heterogeneous panels to a trivariate setting in the framework of Toda and Yamamoto (1995).

This study aims to investigate the cointegration and causality relationships between Hong Kong’s residential property price and stock price, using quarterly data, from the 1st quarter of 1980 to the 3rd quarter of 2015.

In contrast to other studies, the cointegration test used is the autoregressive distributed lag (ARDL) cointegration (bounds testing) approach of Pesaran et al. (2001) that based on the estimation of an unrestricted error correction model and the causality test is based on non-causality test of Granger et al. (2000). Moreover, this research employs recursive least square procedures and Chow (1960) breakpoint test to detect unknown structural break and variation of relationships between residential property and stock price over the whole sample period.

The results of ARDL cointegration tests running from stock to residential property markets provide strong evidence to support the hypothesis that the stock and residential properties are cointegrated. The results of Granger et al. (2000) non-causality test support the view of wealth effect that stock price has an important causal effect on residential property price in Hong Kong but not vice versa. In addition, the results of recursive ordinary least squares coefficients estimates and Chow (1960) test (breakpoint test) for structural instability confirm the variation of the relationships between stock and residential property markets over the sample period.

The empirical results from cointegration and causality tests suggest that the residential asset returns are better predicted by including the lagged difference values of stock price.

This is the pioneering study to examine the cointegration and causality study of stock and residential property price in Hong Kong by employing Pesaran ARDL cointegration approach and Granger non-causality approach. Investors are able to perform an effective evaluation to assist in allocating investment funds, and the government bodies can implement supplement housing policy in response to the public needs.

The purpose of this paper is to investigate the relationship between the energy consumption and the economic growth in the USA and in a sectoral level by using monthly data from January 1991 to May 2016.

While assessing the relationship at a country level, the authors also examine five sectors by using quantile causality.

The findings indicate the existence of a causality at the sectoral level in tails. More specifically, industrial and electric sectors cause the growth at the lower and higher levels. Residential, commercial and transportation sectors do not cause the growth in all levels. Total consumption causes the growth in the middle and low levels but not in the high level. Finally, the empirical evidence signifies an asymmetric relationship between the covariates.

The results imply that when the consumption deals conditions with fluctuation, it is likely to be affected by growth. In such a case, energy policies gear toward reducing or increasing energy intensity, improving energy efficiency, encouraging the use of alternative sources and investing in the development of technology.

The authors use, for the first time, the quantile causality for the case of energy consumption and economic growth. The quantile test is useful for a thorough comprehension of the causal relationship for this area. Compared to the OLS, which is used for the majority of causality tests, the quantile investigates the causality at the sectoral level in the tails.

The purpose of the present study is to directly examine the relationship between bilateral exchange rate and stock market index in a bivariate framework during the period of the floating exchange rate regime in Thailand.

The monthly data used in this study are the stock market index or stock prices from the Stock Exchange of Thailand, and the nominal bilateral exchange rate in terms of baht per US dollar from the Bank of Thailand. The period covers July 1997 to June 2010 with 156 observations. This is the period that the country switched from fixed to floating exchange rate regime. The stock market return is calculated by the percentage change of stock market index (or stock prices) while the exchange rate return is the percentage change of the nominal bilateral exchange rate. Three estimation methods are used to capture the interaction between stock and foreign exchange markets: bounds testing for cointegration, non‐causality test, and the two‐step approach with a bivariate GARCH model and Granger causality test.

The results of the present study show that bounds testing for cointegration does not detect the long‐run relationship between stock prices and exchange rate. In addition, the non‐causality test fails the diagnostic test for multivariate normality in the residuals of the estimated VAR model. However, the two‐step approach adequately detects the linkages between the stock and foreign exchange markets. It is found that there exists positive unidirectional causality running from stock market return to exchange rate return. The exchange rate risk causes stock return to fall as expected. Moreover, there are bidirectional causal relations between stock market risk and exchange rate risk, but in different directions.

Since a rising trend in the risk in the foreign exchange market causes stock return to fall, both domestic and foreign investors should be aware of the risk or uncertainty in the foreign exchange market because it can cause their portfolio return to fall. For policymakers, reducing exchange rate risk cannot be done without the associated costs from a rising risk in the stock market.

This study provides an evidence of volatility (or risk) spillovers in stock and foreign exchange markets. In addition, the risk in foreign exchange market that adversely affects return in the stock market is an expected phenomenon under the floating exchange rate regime.

The purpose of this paper is to investigate whether the Australian equity market is informationally efficient in the semi‐strong form with regard to interest rates and the exchange rate shocks during the period 1994‐2006.

There is evidence that the data are non‐normal and that autoregressive conditional heteroskedasticity (ARCH) effects exist and in such circumstances, standard estimation methods are not reliable. A new method introduced by Hacker and Hatemi‐J which is robust to non‐normality and the presence of ARCH is applied.

The results show the Australian equity market is not informationally efficient with regard to either the interest rate or the exchange rate.

The empirical findings, in contrast to several previous studies, imply that the possibility for arbitrage profits in the equity market might exist.

This study aims to explain how delinquency shocks in one type of debt contaminate the others. That is, the authors aim to shed light on the time pattern of delinquencies in different debt types.

This study analyzes the interdependencies between mortgage, credit card and auto loans delinquency rates in the USA from 2003 to 2019, using a panel VAR-X, the panel Granger causality tests and the Geweke linear dependence measures. The authors also compute the impulse response functions of a shock to one kind of debt on the others and decompose the variance of the forecast errors.

The authors find a statistically significant bidirectional Granger causality between the delinquencies. The Geweke measures of linear dependence and the Dumitrescu and Hurlin Granger non-causality tests support that mortgage predominantly causes credit card and auto loan delinquencies. Auto loans also cause credit card delinquencies. The impulse response functions confirm this pattern. This scenario aligns with a sequence where debtors consider rational first to default on credit cards, second on auto loans and only on mortgages in the last instance. Indeed, credit card delinquencies Granger-cause delinquencies in other debts when it occurs.

To the best of the authors’ knowledge, this is the first study to focus on the temporal pattern of delinquency rates for all the US states, using panel data. Furthermore, the results call for policymakers to design regulations to break the transmission channel from debt delinquencies.

The purpose of this paper is to examine the interaction of enforcement and adjudication for general deterrence of drunk-driving. The authors present a triangular feedback model between three domains: police, courts and drunk-driving events. The authors’ deductive approach imposes no structural assumptions beyond the core of general deterrence theory.

Using a largely untapped data set for California’s 58 counties from 1990 to 2010, the authors estimate a series of heterogeneous panel Granger non-causality tests. This empirically based evidence is re-organized per the proposed triangular feedback model to objectively categorize local criminal justice systems as active, responsive or reactive (with respect to drunk-driving).

Our results suggest that state-level analyses obscure useful variations that empirical panel methods can now handle. The authors provide evidence that research based on empirically derived groupings, rather than inductively based preconceptions, is key to understanding enforcement and compliance. The authors provide a less confounded picture of the relationship between drunk-driving enforcement and adjudication.

Our study addresses one offense for a particular state in the USA. It is an exploratory analysis. This analytical and empirical approach is new.

Our approach imposes very few a priori assumptions and requires a minimum of data series to be executed. The method can be broadly applied to a range of topics and observational units.

The authors aim to expand identification of local systems’ effectiveness (or not) and mechanisms of for general deterrence of drunk-driving. The offense is one that can be committed easily and unintentionally; it does not presume anomie. The authors address general communities, not anomalies. Knowing how enforcement and compliance operate is essential to an array of behavioral externalities.

This is a new empirically based approach for analyzing social systems. It is a marriage of new macroeconomic time-series techniques with an old question, most often addressed by microeconomic research. This study uses an underutilized data source to construct a unique panel data set.

This paper examines the short and long‐term comovements among UK regional property markets over the period 1976‐2001. The markets examined are London, Outer South East, East Anglia, South West, East Midlands, West Midlands, Yorkshire and Humberside, North and North West. Multivariate cointegration procedures, Granger non‐causality tests, level VAR and generalised variance decomposition analyses based on error‐correction and vector autoregressive models are conducted to analyse relationships among these markets. The results indicate that there is a stationary, long‐term relationship and a number of long‐term causal linkages between the various UK property markets. In terms of the percentage of variance explained, other regional markets are generally more important than innovations in a given region, though this is not the case for the Outer South East. The Outer South East market is segmented from the other regional markets, though also extremely influential in explaining forecast variance in these markets. The overall suggestion is that opportunities exist for portfolio diversification in the UK regional property market, and the Outer South East market should be seen as containing valuable information for forecasting performance in the regional markets.

The purpose of this paper is to examine the relationship between house price affordability (HPA) and rental price affordability (RPA) in New Zealand. The cointegration of HPA and RPA is of particular focus given rising house prices and rising rents.

The study examines the lead-lad correlation between HPA and RPA. The method uses a generalised least square technique and the development of an ordinary least squares model.

The study shows that there is an existence of cointegration and unidirectional statistical causality effects between HPA and RPA across 11 regions in New Zealand. Furthermore, Auckland, Wellington and Canterbury are the three regions in which the results detect the most extreme effects amongst HPA and RPA compared to other places in the country. Extended empirical work shows interesting results that there are lead-lag effects of HPA and RPA on each other and on mortgage rates at the national scale. These effects are consistent for both methods but are changed at individual lead-lag variables and amongst different regions.

The study empirically provides useful insight for both academia and practitioners. Particularly in examining the long-run effects, cointegration and forecasting of the volatile interactions between HPA and RPA.

The purpose of this paper is to examine the relationship between financial development indicators and human development in India using annual data from 1980-2012.

The Ng-Perron unit root test is used to check for the order of integration of the variables. The long run relationship and short run dynamics are examined by implementing the ARDL bounds testing approach to co-integration. Granger’s non-causality test and variance decomposition techniques are also used to examine the impact of financial development indicators on human development.

The results confirm a long run relationship among the variables. The results of granger non causality indicate that unidirectional causality runs from financial development indicators to human development index (HDI). The variance decomposition analysis shows that among all the financial indicators, broad money supply (M3) has the largest contribution to changes in human development in India.

The present study recommends for appropriate reforms in financial market to attain sustainable human development in India. The findings will be useful for India’s policy makers, in order to maintain the parallel expansion of financial development and human development.

This paper is first of its kind to empirically examine the casual relationship between financial development indicators and human capital development proxied by HDI in India by using modern econometric techniques.