Are consumers' financial needs and values common across cultures? Evidence from six countries

Are consumers' financial needs, and financial values, the same or different across cultures? Two studies, with student (Study 1; n = 988) and non‐student (Study 2; n = 959) participants, explore the extent of equivalence, across six countries (Brazil, Russia, China, Taiwan, Tunisia and US), in financial need belief, and financial value, measurement models. The financial need beliefs, derived from self‐determination theory (SDT) principles, include financial self‐efficacy, financial autonomy, financial community trust and support; the financial values include materialism and financial altruism. Both the financial need and financial value constructs evidence configural invariance (similar factor structure), and factor invariance among student but not non‐student samples. The financial need constructs evidence full, and the financial value constructs evidence partial, metric (factor loading) invariance. Factor covariance invariance obtains for the financial need beliefs constructs but not the financial value constructs. Finally, neither financial need nor financial value constructs evidence scalar (intercept) invariance. These results provide partial support for extending SDT's hypothesis of universal human needs to the financial domain. In contrast, the financial value constructs of altruism and materialism are largely instable across cultures, suggesting that consumer views of giving, and the role of wealth in social status, differ between countries.     

Detection of Information Flow in Major International Financial Markets by Interactivity Network Analysis

This paper is an empirical assessment of various aspects regarding interconnections among the largest world stock markets. The study investigates the dynamic connection structure of ten major stock markets using partial directed coherence (PDC) and graphical models. Recently, PDC has been introduced for a frequency domain analysis of linear Granger-causality based on modeling the underlying dynamics by vector autoregressive processes. It constitutes a powerful tool to explore the causal influence between complex multivariate processes in neuroscience area. The paper proposes using this tool to study the information flow and causal influence between international financial markets. The novel proposed approach offers many advantages and superiorities compared to the conventional methodologies for investigating financial links across stock markets. Applying PDC to ten opening and closing major stock index data allows the discrimination between directed and induced causality connections between financial markets. The graphical model summarizing the PDC results and instantaneous causality analysis shows the US, UK, Germany and Hong Kong have a consistently strong causality impact on price movements in major financial markets. The strong connection between the ten major financial markets supported by the empirical results tends to reduce the gain of international diversification and intensifies the risk of the international financial system instability.     

A Nonlinear Approach for Modeling and Forecasting US Business Cycles

The purpose of this paper is to provide a complete evaluation of four regime-switching models by checking their performance in detecting US business cycle turning points, in replicating US business cycle features and in forecasting US GDP growth rate. Both individual and combined forecasts are considered. Results indicate that while the Markov-switching model succeeded in replicating all the NBER peak and trough dates without an extra-cycle detection, it seems to be outperformed by the Bounce-back model in term of the delay time to a correct alarm. Concerning business cycle features characterization, none of the competing models dominates over all the features. The performance of the Markov-switching and bounce back models in detecting turning points was not translated into an improved business cycle feature characterization since they are outperformed by the Floor and Ceiling model. The forecast performance of the considered models varies across regimes and across forecast horizons. That is, the model performing best in an expansion period is not necessarily the same in a recession period and similarly for the forecast horizons. Finally, combining such individual forecasts generally leads to increased forecast accuracy especially for h=1.     

On the post-financial crisis performance of Islamic mutual funds: the case of Riyad funds

Using Riyad Capital mutual funds as a proxy for Saudi Arabian mutual funds, this paper empirically compares the risk-adjusted performance and investment style of Islamic mutual funds with that of conventional funds in the wake of the recent global financial crisis of 2009–2014. Absolute and relative risk-adjusted measures with single factor (Jensen) and multifactor (Carhart) models are applied. Our findings suggest that Islamic funds outperformed conventional funds domestically, given similar risk exposure, and produced comparable results under lower market risk globally. The results show that Islamic funds are a relatively big cap from the strong statistical significance registered on the global side as evidenced by the difference portfolio outcomes. In addition, the difference portfolios provide statistical evidence that Islamic funds are more value-oriented compared to conventional funds on both fronts. Furthermore, Islamic funds tend to slightly favour a contrarian trading investment strategy as suggested by statistically significant local portfolio value and global difference portfolios results. The results of home bias test show stronger ties by local Islamic funds to local market relative to the global proxy suggesting that domestic investors and managers favour Islamic funds over conventional funds, thus confirming a local preference for Shari’ah-compliant investments.     

Sterilised Interventions within a Heterogeneous Expectation Exchange Rate Model: Evidence from the Reserve Bank of Australia

In this article, we study the effectiveness of central bank intervention within a heterogeneous expectation exchange rate model for the Reserve Bank of Australia. The empirical evidence is gathered by applying a Markov‐switching approach to daily A$/US$ exchange data from December 1983 to April 2008. Our results support both chartist and fundamentalist regimes. It is shown that the two regimes are persistent and that the fundamentalist regime is riskier. Moreover, interventions when the chartist regime prevails increase the proportion of fundamentalists and thus exert a stabilising effect on the foreign exchange market.     

Empirical evaluation of nominal convergence in Czech Republic, Poland and Hungary (CPH)

We estimate a four variable structural vector auto regression (SVAR) model of the Czech Republic, Poland and Hungary economies in order to evaluate the links between the instruments of monetary policy and inflation outcomes. We find that the linkages between the interest rates and price levels are weak. However, the exchange rate constitutes the most important channel of monetary policy transmission for Poland and Hungary. For the Czech Republic, the link between interest rate rise and price level is rather indirect.     

Modeling preference evolution in discrete choice models: A Bayesian state-space approach

We develop discrete choice models that account for parameter driven preference dynamics. Choice model parameters may change over time because of shifting market conditions or due to changes in attribute levels over time or because of consumer learning. In this paper we show how such preference evolution can be modeled using hierarchial Bayesian state space models of discrete choice. The main feature of our approach is that it allows for the simultaneous incorporation of multiple sources of preference and choice dynamics. We show how the state space approach can include state dependence, unobserved heterogeneity, and more importantly, temporal variability in preferences using a correlated sequence of population distributions. The proposed model is very general and nests commonly used choice models in the literature as special cases. We use Markov chain monte carlo methods for estimating model parameters and apply our methodology to a scanner data set containing household brand choices over an eight-year period. Our analysis indicates that preferences exhibit significant variation over the time-span of the data and that incorporating time-variation in parameters is crucial for appropriate inferences regarding the magnitude and evolution of choice elasticities. We also find that models that ignore time variation in parameters can yield misleading inferences about the impact of causal variables. This paper is based on the first author's doctoral dissertation.