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The existing literature on the Black-Litterman (BL) model does not offer adequate guidance on how to generate investors’ views in an objective manner. Therefore, the purpose of this paper is to establish a generalized multivariate Vector Error Correction Model (VECM)/Vector Auto-Regressive (VAR)-Dynamic Conditional Correlation (DCC)/Asymmetric DCC (ADCC) framework, and applies it to generate objective views to improve the practicality of the BL model.

This paper establishes a generalized VECM/VAR-DCC/ADCC framework that can be utilized to model multivariate financial time series in general, and produce objective views as inputs to the BL model in particular. To test the VECM/VAR-DCC/ADCC preconditioned BL model’s practical utility, it is applied to a six-asset China portfolio (including one risk-free asset).

With dynamically optimized view confidence parameters, the VECM/VAR-DCC/ADCC preconditioned BL model offers clear advantage over the standard mean-variance method, and provides an automated portfolio optimization alternative to the classic BL approach.

The VECM/VAR-DCC/ADCC framework and its application in the BL model proposed by this paper provide an alternative approach to the classic BL method. Since all the view parameters, including estimated mean return vectors, conditional covariance matrices and pick matrices, are generated in the VECM/VAR and DCC/ADCC preconditioning stage, the model improves the objectiveness of the inputs to the BL stage. In conclusion, the proposed model offers a practical choice for automated portfolio balancing and optimization in a China context.

Refers to previous research on the relationship between returns for different asset classes and on cointegration; and applies Johansen’s (1988) methodology to develop a prediction model. Uses 1978‐1995 data on five US asset classes (treasury bills, long‐term bonds, large capitalization common stocks, unsecuritized real estate and securitized real estate equity) to investigate cointegration between them. Shows that the index of unsecuritized real estate is positively related to treasury bills and negatively related to long‐term bonds and securitized real estate; and that returns for it can be forecast more accurately by using VECM models rather than unrestricted VAR models. Considers the implications for portfolio allocation, compares the results with other research fundings and calls for further research.

The authors aim to report empirical linkages between the US and Brazil, Russia, India and China (BRIC) financial stress indices catalyzing catalyzing dependent economic policy initiatives (an extended version of Singh and Singh, 2017a).

Initially, the study develops financial stress indices for the respective BRIC financial markets. Later, it captures linkages among the said US-BRIC indices by using Johansen cointegration, vector autoregression/vector error correction models (VECM), generalized impulse response functions, Toda–Yamamoto Granger causality, variance decomposition analyses and bivariate generalized autoregressive conditional heteroskedasticity (GARCH) model under constant conditional correlation framework, in general. Markov regime switching and efficient causality tests proposed by Hill (2007) are also used.

Overall, there are both short-run and long-run dynamic interactions observed between the US and Indian financial stress indices. For rest of the markets, only short-run interactions are found to be in existence. The time-varying co-movement coefficients report financial contagion impact of the US financial crisis on Russian and Indian financial systems only. Contrary to this, Brazilian and Chinese financial systems are largely exhibiting interdependence with the US financial system. Efficient causality tests report indirect impact of the Russian financial system on Brazilian via auxiliary Indian financial system.

The present study is the first of its kind capturing linkages among the US-BRIC financial stress indices by using diverse econometric models. The results support different market participants and policymakers in understanding effectiveness and implementation of economic policies while considering their cross-market interactions as well.

With the rapid-growing house market in the past decade, the purpose of this paper is to study the important issue of house price information flows among 12 major cities in China, including Shanghai, Beijing, Xiamen, Shenzhen, Guangzhou, Hangzhou, Ningbo, Nanjing, Zhuhai, Fuzhou, Suzhou and Dongguan, during the period of June 2010 to May 2019.

The authors approach this issue in both time and frequency domains, latter of which is facilitated through wavelet analysis and by exploring both linear and nonlinear causality under the vector autoregressive framework.

The main findings are threefold. First, in the long run of the time domain and for timescales beyond 16 months of the frequency domain, house prices of all cities significantly affect each other. For timescales up to 16 months, linear causality is weaker and is most often identified for the scale of four to eight months. Second, while nonlinear causality is seldom determined in the time domain and is never found for timescales up to four months, it is identified for scales beyond four months and particularly for those beyond 32 months. Third, nonlinear causality found in the frequency domain is partly explained by the volatility spillover effect.

Results here should be of use to policymakers in certain policy analysis.

This paper aims to identify various macroeconomic variables that affect the stock market performance of developed and emerging economies. It also investigates the effect of these factors on the stock markets of both economies. The impact of these variables on broad market indices and sectoral indices is investigated and compared too.

The publications for the study were retrieved from databases such as Emerald Insight, EBSCO, ScienceDirect and JSTOR using the keywords “Macroeconomic variables” and “Stock market” or “Stock market performance.” The result demonstrated a growing corpus of scholarly work in the domain of stock market. The study was carried out separately for each macroeconomic indicator. Given a large number of articles under consideration, the authors began by reading the titles and abstracts of all publications to identify those that were relevant. The papers are evaluated in Excel and the articles for review range from 1972 to 2021.

The authors found that gross domestic product (GDP), FDI (Foreign Direct Investment) and FII (Foreign Institutional Investment) have a positive effect on both emerging and developed economies’ stock market while gold price has a negative effect. Interest rates had a negative impact on both economies except for a few developing countries. The relationship with oil prices was positive for oil exporting countries while negative for oil importing countries. Inflation, money supply and GDP are the macroeconomic variables that have the same effect on sectoral indices as they do on broad market indices. The impact was sector-specific for the remaining variables.

This paper gives an overview of relation and effect covering variety of macroeconomic variables and stock market indices. Still, there is a scope for further research to analyze the effect on thematic, strategy and sectoral indices. A longer time horizon with new variables, such as bank deposit growth rate, nonperforming assets of banks, consumer confidence index and investor sentiment, can be studied using high-frequency data. This research may help stakeholders adopt and manage their policies during a crisis or economic slump.

This study will assist investors, researchers and educators in the fields of economics and finance in understanding how macroeconomic factors affect the stock market. Furthermore, this study can guide in portfolio diversification strategy across multiple sectors by examining the impact of macroeconomic factors specific to sectoral indices. This paper provides insight into society and researchers since it integrates a number of macroeconomic variables and their interaction with the stock market. It may also help pension funds and mutual fund firms to hedge their funds and allocate equity portfolios.

With respect to India, this study looked at new macroeconomic variables and sectors. It contrasted the impact of these variables in developed and developing economies. The effect of broad and sectoral stock indexes was also investigated and compared. The authors examined how these variables responded during crisis and economic downturns by using articles from a longer time frame. This research also looked into how changing the frequency of data for the variables altered stock performance. This paper emphasized the need for more research into thematic, strategy and broad market indices, such as small-cap and mid-cap indices.

This paper proposes a Bayesian procedure to investigate the purchasing power parity (PPP) utilizing an exponential smooth transition vector error correction model (VECM). Employing a simple Gibbs sampler, we jointly estimate the cointegrating relationship along with the nonlinearities caused by the departures from the long-run equilibrium. By allowing for nonlinear regime changes, we provide strong evidence that PPP holds between the US and each of the remaining G7 countries. The model we employed implies that the dynamics of the PPP deviations can be rather complex, which is attested to by the impulse response analysis.

The purpose of this paper is to deeply study and compare the dual and single hedging strategy, from the direct and cross hedging perspective.

The authors not only first consider the dual hedge of integrated risks in this oil prices and foreign exchange rates setting but also make a novel comparison between the dual and single hedging strategy from a direct and cross hedging perspective. In total, six econometric models (to conduct one-step-ahead out-of-sample rolling estimation of the optimal hedge ratio) and two hedging performance criteria are employed in two different hedging backgrounds (direct and cross hedging).

Results show that in the direct hedging background, a dual hedge cannot outperform the single hedge. But in the cross dual hedging setting, a dual hedge performs much better, possibly because the dual hedge brings different levels of advantages and disadvantages in the two different settings and the superiority of the dual hedge is more obvious in the cross dual hedging setting.

The existing literature that deals with oil prices and foreign exchange rates mostly concentrates on their relationship and comovements, while the dual hedge of integrated risks in this setting remains underresearched. Besides, the existing literature that deals with dual hedge gets its conclusions only based on a single specific background (direct or cross hedging) and lacks deeper investigation. In this paper, the authors expand the width and depth of the existing literature. Results and implications are revealing.

This study aims to examine the relationships between aggregated and disaggregated energy use in the industrial sector, carbon emissions and industrial output in China.

The study utilizes annual frequency data for the period of 1984-2015. The unit root properties of data are tested using augmented Dickey–Fuller and Phillips and Perron unit root tests. Furthermore, the Zivot–Andrew structural breaks unit root test is used to detect the structural breaks steaming into series. The autoregressive distributed lag bound test and newly developed Bayer–Hanck combined cointegration are used to check the existence of a cointegration relationship between underlying variables. Last, the direction of causality is determined applying vector error correction model (VECM) Granger causality.

The results confirm the existence of a long-run relationship in the presence of structural breaks. The authors conclude that aggregated and disaggregated energy consumption in the industrial sector increases CO₂ emission in both long and short run. The VECM Granger causality analysis indicates the bidirectional relationships between CO₂ emission, industrial growth and aggregated and disaggregated (coal, oil and natural gas) energy consumption.

Based on the empirical results mentioned above, the study proposes the recommendation that China should focus on the use of natural gas in the industrial sector instead of coal and oil consumption. The most potent reasons for such a transformation are twofold: natural gas is much more environment-friendly, thus being a much lesser polluting source of energy, and, most significantly, such a change would have no adverse impact upon the output level.

This study contributes to the existing literature on estimating CO₂ emission by using aggregated and disaggregated energy consumption in case of China. Notwithstanding, it also adds to the existing applied literature by using newly developed combined cointegration to confirm and substantiate the cointegration relationship between the underlying variables. Moreover, this study incorporates the role of structural breaks while investigating CO₂ emission function, which helps in providing more valuable policy suggestions.