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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.

The purpose of this study is to investigate the relationship between renewable energy and economic growth of Bulgaria.

This study analyzes the relationship between renewable energy and economic growth of Bulgaria for the period 1990-2016, based on annual data, by using the Toda–Yamamoto analysis and Autogressive Distrubuted Lag (ARDL) bound test. This period is characterized by the democratization of the Balkans and several crisis cycles in Bulgaria. Renewable energy consumption (REC, percentage of total final energy consumption), renewable electricity output (REO, percentage of total electricity output) and economic growth (GDP constant 2010 US$) were used. The levels or differences of the variables that are stationary were investigated using the augmented Dickey–Fuller (ADF), Philips–Perron (PP) and Kwiatkowski-Philips-Schmidt-Shin (KPSS) unit root tests.

Three different results were obtained from this study. One showed that renewable energy consumption and renewable electricity output are the causes of economic growth. Another result of this study is that economic growth and renewable electricity output are the causes of renewable energy consumption. The last result is that economic growth and renewable energy consumption are not causes of renewable electricity output. There was no long-term relationship between variables.

The ARDL and Toda–Yamamoto tests were used because of lack of data sets. Thus, it is estimated that there is no long-term relationship.

This study is an original work for Bulgaria, showing the results of the relationship between renewable energy and economic growth. In line with the results of this study, renewable energy projects related to Bulgaria can be predicted.

The purpose of this paper is on developing and implementing a model which provides a fuller and more comprehensive reflection of the interaction of house prices at the suburb level.

The authors examine how changes in housing prices evolve across space within the suburban context. In doing so, the authors developed a model which allows for suburbs to be connected both because of their geographic proximity but also by non-spatial factors, such as similarities in socioeconomic or demographic characteristics. This approach is applied to modelling home price dynamics in Melbourne, Australia, from 2007 to 2018.

The authors found that including both spatial and non-spatial linkages between suburbs provides a better representation of the data. It also provides new insights into the way spatial shocks are transmitted around the city and how suburban housing markets are clustered.

The authors have generalized the widely used SAR model and advocated building a spatial weights matrix that allows for both geographic and socioeconomic linkages between suburbs within the HOSAR framework. As the authors outlined, such a model can be easily estimated using maximum likelihood. The benefits of such a model are that it yields an improved fit to the data and more accurate spatial spill-over estimates.

In the first part of this paper a new method of applying the Maximum Entropy Principle (MEP) is presented, which makes use of a “frequency related” entropy, and which is valid for all stationary processes. The method is believed valid only in the case of discrete spectra. In the second part of the paper, a method of estimating continuous spectra in the presence of noise is presented, which makes use of the Mutual Information Principle (MIP). Although the method proceeds smoothly in mathematical terms, there appear to be some difficulties in interpreting the physical meaning of some of the expressions. Examples in the use of both methods are presented, for the usual practical problem of estimating a power spectrum for a process whose autocorrelation function is partially known a priori.

Analyzing and forecasting reliability is increasingly important for enterprises. An accurate product reliability forecasting model cannot only learn and track a product's reliability and operational performance, but can also offer useful information that allows managers to take follow‐up actions to improve the product's quality and cost. The Generalized Autoregressive Conditional Heteroskedastic (GARCH) model is already extensively used to analyze and forecast time series data. However, the GARCH model has not been used to analyze and forecast the failure data of repairable systems. Based on these concerns, this study proposes the GARCH model to analyze and forecast the field failure data of repairable systems.

This paper proposes the GARCH model to analyze and forecast the field failure data of repairable systems. Empirical results from electronic systems designed and manufactured by suppliers of the Chrysler Corporation are presented and discussed.

The proposed method can analyze and forecast failure data for repairable systems. Not only can this method analyze failure data volatility, it can also forecast the future failure data of repairable systems.

Advanced progress in the field of reliability prediction estimation can benefit engineers or management authorities by providing important decision support tools in which the prediction accuracy suggests financial and business outcomes as well as other outcome application results.