Multivariate statistics in industrial marketing management: A practitioner tool kit

Much published work over the years has pointed to the differences between business-to-consumer (B2C) and business-to-business (B2B) marketing. An undesirable by-product of this sometimes misdirected distinction is that managers working within B2B environments have generally not considered the use of what are seen as B2C techniques, such as multivariate statistical analysis. This article is structured in three parts. First, the argument for the similarities between B2B and B2C marketing is developed; second, three different multivariate statistical techniques are presented and combined to form a practical tool kit for use by B2B managers on strategic, operational, and tactical levels; and third, the results of an application of the techniques in the life science research chemicals industry is reported, demonstrating that the tool kit substantially enhanced managerial understanding of customer decision processes.     

Semiparametric estimation of multi-asset portfolio tail risk

When correlations between assets turn positive, multi-asset portfolios can become riskier than single assets. This article presents the estimation of tail risk at very high quantiles using a semiparametric estimator which is particularly suitable for portfolios with a large number of assets. The estimator captures simultaneously the information contained in each individual asset return that composes the portfolio, and the interrelation between assets. Noticeably, the accuracy of the estimates does not deteriorate when the number of assets in the portfolio increases. The implementation is as easy for a large number of assets as it is for a small number. We estimate the probability distribution of large losses for the American stock market considering portfolios with ten, fifty and one hundred assets of stocks with different market capitalization. In either case, the approximation for the portfolio tail risk is very accurate. We compare our results with well known benchmark models.     

EXTREME VALUE THEORY IN FINANCE: A SURVEY

Extreme value theory is concerned with the study of the asymptotic distribution of extreme events, that is to say events which are rare in frequency and huge in magnitude with respect to the majority of observations. Statistical methods derived from it have been employed increasingly in finance, especially for risk measurement. This paper surveys some of those main applications, namely for testing different distributional assumptions for the data, for Value‐at‐Risk and Expected Shortfall calculations, for asset allocation under safety‐first type constraints, and for the study of contagion and dependence across markets under conditions of stress.     

Islamic equity market integration and volatility spillover between emerging and US stock markets

The purpose of this paper is to study the conditional correlations across the US market and a sample of five Islamic emerging markets, namely Turkey, Indonesia, Pakistan, Qatar, and Malaysia. The empirical design uses MSCI (Morgan Stanley Capital International) Islamic equity index since it applies stringent restrictions to include companies. Indeed, two main restrictions must be met: (i) the business activity must be compliant with Shari’ah (i.e., Islamic law) guidelines and (ii) interest-bearing investments and leverage ratios should not exceed upper limits. Three models are used: multivariate GARCH BEKK, CCC, and DCC. The estimation results of the three models show that the US and Islamic emerging equity markets are weakly correlated over time. No sheer evidence supports that the US market spills over into the Islamic emerging equity markets. Besides interpreting the results in terms of weak market integration, the peculiar specificities of the Islamic finance industry and the admittance conditions to the MSCI Islamic equity index contribute to explaining them. Indeed, Islamic finance bans interest-bearing investments and imposes some rules, such as asset-backing, which has sizeable impacts on volatility spillover and shocks transmissions, alongside with the close linkage between real and financial sectors. These findings suggest that investors should take caution when investing in the Islamic emerging equity markets and diversifying their portfolios in order to minimize risk.     

Local Powers of Optimal One‐sample and Multi‐sample Tests for the Concentration of Fisher‐von Mises‐Langevin Distributions

One‐sample and multi‐sample tests on the concentration parameter of Fisher‐von Mises‐Langevin distributions on (hyper‐)spheres have been well studied in the literature. However, only little is known about their behaviour under local alternatives, which is due to complications inherent to the curved nature of the parameter space. The aim of the present paper therefore consists in filling that gap by having recourse to the Le Cam methodology, which has recently been adapted from the linear to the spherical setup. We obtain explicit expressions of the powers for the most efficient one‐ and multi‐sample tests. As a nice by‐product, we are also able to write down the powers (against local Fisher‐von Mises‐Langevin alternatives) of the celebrated Rayleigh test of uniformity. A Monte Carlo simulation study confirms our theoretical findings and shows the empirical powers of the above‐mentioned procedures.     

Brexit: Tracking and disentangling the sentiment towards leaving the EU

On 23 June, 2016, the UK held a referendum to decide whether to stay in the European Union or leave. The uncertainty surrounding the outcome of this referendum had major consequences for public policy, investment decisions, and currency markets. We discuss some of the subtleties involved in smoothing and disentangling poll data in light of the problem of tracking the dynamics of the intention to Brexit, and propose a multivariate singular spectrum analysis method that produces trendlines on the unit simplex. The trendline yield via multivariate singular spectrum analysis is shown to resemble that of local polynomial smoothing, and singular spectrum analysis presents the nice feature of disentangling the dynamics directly into components that can be interpreted as changes in public opinion or sampling error. The merits and disadvantages of some different approaches for obtaining smooth trendlines on the unit simplex are contrasted, in terms of both local polynomial smoothing and multivariate singular spectrum analysis.     

Time-frequency co-movements between oil prices and interest rates: Evidence from a wavelet-based approach

This paper investigates time–frequency co-movements between crude oil prices and interest rates. To test this relationship, the study applied a continuous wavelet and cross wavelet approaches to data from West Texas Intermediate (WTI) crude oil prices and interest rates in the United States (U.S.). Results from the sample period revealed significant relationships, in the intermediate term, between WTI crude oil prices and U.S. interest rates. Moreover, co-movements between oil price and interest rate variables were especially sensitive during abnormal political events and periods of financial ‘meltdown’. We further use Partial Wavelet Coherence (PWC) and Multiple Wavelet Coherence (MWC) methods to investigate the impacts of five major control variables namely GDP growth, unemployment, three-month Treasury bill, CPI index and industrial production index. The results show a powerful impact of control variables on oil-interest rates co-movements under different frequencies. Finally, we show evidence of co-integrating long run relationship between oil markets and control variables. These results have important implications for energy investors and policy makers.     

Modeling asymmetric price transmission in the European food market

In this study, we employ a multivariate panel error correction model (PVECM) to investigate asymmetric price transmission among the farm, processor, and retail segments of the European food supply chain for the 2005–2016 period. The results indicate that, in both the long- and short-run, retail prices respond more strongly to processor price increases than decreases and the same occurs for processor prices due to farm price changes. Thus, the findings demonstrate the presence of positive asymmetric price transmission in the European food supply chain. Finally, the results of the present study indicate that the food price pass-through varies greatly across product category and across countries, and that the pass-through to producer prices is greater than that to consumer prices.     

GRADING JOURNALS IN ECONOMICS: THE ABCS OF THE ABDC

Many institutions and governments grade academic journals for the evaluation of research. In this paper, we implement a multi-bibliometric methodology for the evaluation of such a list of journal grades. We examine the grades assigned by the Australian Business Deans Council (ABDC) for over 750 journals in the fields of economics and statistics. Firstly, we generate up to 48 bibliometric-based grades for each journal based on the grade distribution implied by the ABDC. Secondly, we categorize the bibliometrics employing a cluster analysis of an interrater agreement statistic. Thirdly, we present a visualization of the consistency of the grading by journal. Finally, we list those journals where the majority of the matched bibliometrics indicate a higher or lower grade than their ABDC grade.