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The purpose of this paper is to evaluate the impact of macroeconomic condition and real estate price trend on the amount of residential loan.

The paper using a sample of 16 European Countries for the time period 2007–2015 evaluates the impact of change in the gross domestic product (GDP) growth and the inflation rate on the amount of residential loans. The analysis is performed by using a vector autoregressive (VAR) and generalized VAR approach for the full sample and for each country considered.

For a short-term horizon, shocks to mortgages, the house price index (HPI) and the GDP have a positive effect on the GDP, a shock to the amount of mortgages has a positive effect on the mortgage supply and a shock to the GDP has a negative effect on HPI. The main results for the long-term horizon are that a GDP shock has a positive and persistent effect on the amount of mortgages, a shock to HPI has a negative and persistent effect on mortgages and a shock to the amount of mortgages seems to have no persistent effect on the GDP or the HPI. Moreover, the analysis shows that a spillover risk among countries exists and a GDP shock in a European area has an effect on the GDP, real estate prices and residential mortgages in almost all European countries.

Results obtained show that both macroeconomic and housing prices shocks matter for the real estate lending and the effect are different in the short- and in the medium–long-term horizon. Results are also different country by country and they are affected by the level of financial development of the country.

The paper studies a lending crisis period and evaluates for the European market the impact of shock on macro-variables for mortgages focusing the attention for the first time only on residential mortgages.

This paper aims to investigate the dynamic linkage between stock prices and exchange rate changes for the Gulf Arab countries (Kuwait, Qatar, Saudi Arabia and United Arab Emirates [UAE]).

The author uses the Markov-switching autoregression to detect regime-shift behavior in the stock returns of the Gulf Arab countries and Markov-switching vector autoregressive (MS-VAR) model to capture the dynamic interrelatedness between exchange and stock returns over the period 2000–2018.

This study’s analysis finds evidence to support the persistence of two distinct regimes for all markets, namely, a low-volatility regime and a high-volatility regime. The low-volatility regime illustrates more persistence than the high-volatility regime. Specifically, exchange rate changes do not have an influence on the stock market returns of the Gulf Arab countries, regardless of the regimes. On the other hand, stock market returns have a substantial impact on exchange markets for all countries, except Saudi Arabia, and it is more noticeable during the regime of high volatility.

The findings shed light on the interconnectedness between two of the most important financial markets in the complex international financial environment. They are thus of particular interest for economic policymakers and portfolio investors.

The author distinguishes this study from previous studies in several ways. First, while previous empirical studies of the dynamic linkage between stock prices and foreign exchange markets are primarily devoted to developed markets or emerging markets, this study’s interest is concentrated on four Gulf Arab financial markets (Kuwait, Qatar, Saudi Arabia and UAE). Second, unlike most investigations in the literature that only estimate this link for the whole period, this study attempts to estimate during the good and bad period by using a two-regime MS-VAR model. To the best of the author’s knowledge, this is the first study of the Gulf Arab countries on the stock and foreign exchange markets to apply this model.

This study analyzes the static and dynamic risk spillover between US/Chinese stock markets, cryptocurrencies and gold using daily data from August 24, 2018, to January 29, 2021. This study provides practical policy implications for investors and portfolio managers.

The authors use the Diebold and Yilmaz (2012) spillover indices based on the forecast error variance decomposition from vector autoregression framework. This approach allows the authors to examine both return and volatility spillover before and after the COVID-19 pandemic crisis. First, the authors used a static analysis to calculate the return and volatility spillover indices. Second, the authors make a dynamic analysis based on the 30-day moving window spillover index estimation.

Generally, results show evidence of significant spillovers between markets, particularly during the COVID-19 pandemic. In addition, cryptocurrencies and gold markets are net receivers of risk. This study provides also practical policy implications for investors and portfolio managers. The reached findings suggest that the mix of Bitcoin (or Ethereum), gold and equities could offer diversification opportunities for US and Chinese investors. Gold, Bitcoin and Ethereum can be considered as safe havens or as hedging instruments during the COVID-19 crisis. In contrast, Stablecoins (Tether and TrueUSD) do not offer hedging opportunities for US and Chinese investors.

The paper's empirical contribution lies in examining both return and volatility spillover between the US and Chinese stock market indices, gold and cryptocurrencies before and after the COVID-19 pandemic crisis. This contribution goes a long way in helping investors to identify optimal diversification and hedging strategies during a crisis.

The purpose of this paper is to investigate the influence of economic policy uncertainty (EPU) on China’s grain futures prices. Related literature has discussed several factors contributing to the dramatic boom and bust in China’s grain futures prices, but has overlooked the influence of EPU.

The study employs a newly developed time-varying parameter vector autoregressive model to study and contrast the impact of different types of uncertainty on China’s grain futures prices. The directional volatility spillover index is used to measure the impact of EPU on China’s grain futures prices and compare the differences among commodities.

The results show that EPU affects China’s grain futures prices significantly. The 2008 global financial crisis had stronger influence on China’s grain futures prices than other types of uncertainty. Furthermore, EPU has smaller influence on wheat futures price than on maize and soybean. The Chinese Government interventions may be the reason for this difference.

This study addresses the lack of empirical investigation on the influence of EPU on China’s grain futures price volatility.

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.

Empirical studies have documented the linkage between exchange rate movement and food prices. However, the purpose of this study is to investigate the degree and direction of returns and volatility spillover transmission between exchange rate and domestic food prices in Nigeria.

The study uses weekly data from January 2010 to January 2019. Also, the study adopts the improved Diebold and Yilmaz (2012) approach to evaluate the return and volatility spillover between food price and naira to dollar exchange rate. The study also account for 2016 exchange rate crash in the interconnectedness between food prices and naira to dollar exchange rate.

The paper finds evidence of directional interdependence among the considered food prices and exchange rate based on the obtained spillover indexes. In addition, exchange rate returns and volatility transmission to food prices is more than it receives, particularly after the exchange rate crash.

The high consumption of staple foods requires policies on price stabilisation such as massive investment in local production and reduction in import dependence, in order to cushion the effects of exchange rate depreciation on domestic prices of food.

This study is the first empirical study to investigate the interconnectedness between exchange rate and domestic food prices for a food import–dependent developing country using the Diebold and Yilmaz approach.

The purpose of this study is to reveal the lead–lag structure between international crude oil price and stock markets.

The methods used for this study are as follows: empirical mode decomposition; shift-window-based Pearson coefficient and thermal causal path method.

The fluctuation characteristic of Chinese stock market before 2010 is very similar to international crude oil prices. After 2010, their fluctuation patterns are significantly different from each other. The two stock markets significantly led international crude oil prices, revealing varying lead–lag orders among stock markets. During 2000 and 2004, the stock markets significantly led international crude oil prices but they are less distinct from the lead–lag orders. After 2004, the effects changed so that the leading effect of Shanghai composite index remains no longer significant, and after 2012, S&P index just significantly lagged behind the international crude oil prices.

China and the US stock markets develop different pattens to handle the crude oil prices fluctuation after finance crisis in 1998.

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.

Trade uncertainty does influence the firm’s new investment, profitability and supply chain finance. Consequently, it results in decreased consumption and low consumer confidence and eventually disrupts global economic activity. This paper aims to propose a model to uncover the effects of trade policy uncertainty (TPU) on the real economic activity and economy’s health measured in terms of the purchasing manager’s index (PMI).

This study uses the PMI, trade policy uncertainty index, economic policy uncertainty index and short-term interest rate. The relation between economic activity and uncertainty was studied using nested regression and vector autoregressive model.

The empirical results show that PMI of China and Japan were more responsive to the TPU of the USA and remained more fluctuating during the year 2018–2019. Importantly, this paper notices that the US’s PMI reached a low historically subject to its own trade policy and tension with China. Overall, TPU has shown more pronounced effects on PMI across China, Japan and the USA, followed by important economic and political events and major trade tariff uncertainty deals.

The empirical outcome holds some practical implications trade uncertainty affects not only the economic health of the economy but also market participants, global investors and international political environment, recent trade barriers, tariff wars and ambiguity raise question about free and fair global trade and competitiveness of the member country of the world trade organization.

The work is a novel that attempts to explain economic activity and supply chain through PMI. Unlike conventional economic indicators, e.g. gross domestic product, producer price index, consumer price index, employment, etc. PMI measures manufacturing industries’ overall status concerning the number of orders, inventory levels, productions, supplier deliveries and employment.

The purpose of this study is to investigate the interactive relationships between oil price shocks and the Nigeria stock market.

The paper applied the multivariate vector auto‐regression that employed the generalized impulse response function and the forecast variance decomposition error.

Empirical evidence reveals that stock market returns exhibit insignificant positive response to oil price shocks but reverts to negative effects after a period of time depending on the nature of the oil price shocks. The results are similar even with the inclusion of other variables. Also, the asymmetric effect of oil price shocks on the Nigerian stock returns indices is not supported by statistical evidences.

This is the first study to examine the dynamic linkages between stock market behaviour and oil price shocks in Nigeria.