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Modelling and forecasting interval-valued time series (ITS) have received increasing attention in statistics and econometrics. An interval-valued observation contains more information than a point-valued observation in the same time period. The previous literature has mainly considered modelling and forecasting a univariate ITS. However, few works attempt to model a vector process of ITS. In this paper, we propose an interval-valued vector autoregressive moving average (IVARMA) model to capture the cross-dependence dynamics within an ITS vector system. A minimum-distance estimation method is developed to estimate the parameters of an IVARMA model, and consistency, asymptotic normality and asymptotic efficiency of the proposed estimator are established. A two-stage minimum-distance estimator is shown to be asymptotically most efficient among the class of minimum-distance estimators. Simulation studies show that the two-stage estimator indeed outperforms other minimum-distance estimators for various data-generating processes considered.

Jeliazkov and Poirier (2008) analyze the daily incidence of violence during the Second Intifada in a statistical way using an analytical Bayesian implementation of a second-order discrete Markov process. We tackle the same data and modeling problem from our perspective as cognitive scientists. First, we propose a psychological model of violence, based on a latent psychological construct we call “build up” that controls the retaliatory and repetitive violent behavior by both sides in the conflict. Build up is based on a social memory of recent violence and generates the probability and intensity of current violence. Our psychological model is implemented as a generative probabilistic graphical model, which allows for fully Bayesian inference using computational methods. We show that our model is both descriptively adequate, based on posterior predictive checks, and has good predictive performance. We then present a series of results that show how inferences based on the model can provide insight into the nature of the conflict. These inferences consider the base rates of violence in different periods of the Second Intifada, the nature of the social memory for recent violence, and the way repetitive versus retaliatory violent behavior affects each side in the conflict. Finally, we discuss possible extensions of our model and draw conclusions about the potential theoretical and methodological advantages of treating societal conflict as a cognitive modeling problem.

The purpose of this paper is to examine the temporal impact of individual and institutional investor sentiment on sin stock returns.

The authors estimate vector autoregressive models (VARs) to assess the dynamic relationships amongst pure sin returns and both types of investor sentiment. The justification for estimating VARs is that it allows one to study the potential influence that shocks (i.e. innovations) in individual and institutional investor sentiment might have on pure sin returns over time. Sin stock returns are separated into a market-based and pure sin component. Additionally, the authors split both measures of investor sentiment into rational- and irrational-based components.

This study finds that shocks to both individual and institutional rational-based sentiment positively influence pure sin returns for up to four months. However, irrational-based shocks have a positive, weaker and insignificant effect on pure sin returns. In addition, the results for the pure sin portfolio are compared to the S&P 500 and a comparables portfolio. The results show that sin stocks are less responsive than the S&P and the comparables portfolio to shocks in investor sentiment.

This study addresses some of the limitations found in the only prior study of sin stocks and investor sentiment (Perez Liston, 2016). Specially, this study investigates the link between sin stocks and sentiment in a dynamic context and also focuses the analysis on pure sin returns.

The purpose of this paper is to examine the extent of financial integration occurring in East Asia. Increasing economic integration in East Asia over the last two decades has been evidenced by consistent growth in intra‐regional trade and investment. Greater economic integration in the region, accompanied by financial deregulation and liberalization, has contributed to greater financial integration. This study confirms increasing degree of financial market integration in East Asia by comparing movements of monthly money market rates before and after the Asian financial crisis. Convergence of interest rates across the countries in East Asia is examined by analyzing deviations, correlation coefficients and multivariate co‐integration tests of interest rates.

Economic forecasting has become big business with over 40 institutions producing forecasts in the UK alone. Such forecasts are widely reported in the media and businesses typically subscribe to the output of at least one forecasting agency and use it for planning production and investment over future time periods. The value of forecasts is, however, questionable for actual out‐turns rarely coincide with predictions, particularly at those times when economic performance is deviating from trend. These, of course, are the times when forecasts are most needed. One reason for using them may be that business people believe the results or at least that there is no better alternative in a world in which they must have something as a basis for rational planning. In either case, they are probably wrong and many economists themselves are now exploring alternative approaches. Concludes that, in business, macroeconometric forecasts can easily be overvalued and should be used with care. They are certainly no substitute for fundamental scenario planning or indeed for short run risk management strategies.

Shanghai is the most important economic centre in China. It also has the nation's largest modern office market in terms of floorspace and investment values. However, as with office markets in other cities and countries, the Shanghai market displays rental volatility. This paper aims to examine this issue.

Rental volatility is examined by econometrically constructing a long‐run equilibrium relationship between rent and underlying demand and supply side factors. In order to establish the validity of this model, it is tested for the presence of a cointegrating vector. From this a short‐run dynamic adjustment model is constructed. This is an error correction mechanism that links the short‐ and long‐run models. The impact of office vacancies, foreign direct investment, and changes in the real interest rate on the office market are explicitly considered.

The results indicate that both demand (as represented by gross domestic product (GDP)) and supply (stock) are significant determinants of rents. Space demand is found to be both price and income elastic. In the short‐run model the error correction term is significant and correctly signed. In comparison to other office markets, the Shanghai market adjusts rather slowly. Foreign direct investment is found to have a positive impact on long‐run rents and the vacancy rate is found to impact on short‐term rental adjustment.

The Shanghai office market is the most important in China. However, it has displayed significant rental volatility. This paper is the first to examine explicitly the rental adjustment process in this office market. The results suggest a market that is performing as expected by economic theory but which nevertheless displays relatively slow adjustment to market imbalances.

The purpose of this paper is to ascertain whether weather variables can explain the stock return reaction on the Dow Jones Sustainability Europe Index by employing a number of macroeconomic indicators as control variables.

The authors incorporate the generalized autogressive conditional heteroskeasticity model in methodology for the period August 26, 2009 to May 30, 2014 using daily data.

The empirical results indicate that not only do changes in humidity and wind levels seem to affect positively the European stock market but changes in returns oil and gold prices as well. However, the results show that the volatility of the US dollar/Yen exchange rate and ten-year bond value exerts significant negative impact on companies’ stock returns.

This study adds to the international literature by documenting the impact of weather variables on socially responsible companies.

The purpose of this article is to estimate value at risk (VaR) using quantile regression and provide a risk analysis for defaultable bond portfolios.

The method proposed is based on quantile regression pioneered by Koenker and Bassett. The quantile regression approach allows for a general treatment on the error distribution and is robust to distributions with heavy tails.

This article provides a risk analysis for defaultable bond portfolios using quantile regression method. In the proposed model we use information variables such as short‐term interest rates and term spreads as covariates to improve the estimation accuracy. The study also finds that confidence intervals constructed around the estimated VaRs can be very wide under volatile market conditions, making the estimated VaRs less reliable when their accurate measurement is most needed.

Provides a risk analysis for defaultable bond using quantile regression approach.