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The purpose of this study is to explore the intricate relationship between oil prices, house prices in the UK and Norway, and the mediating role of gold and stock prices in both the short- and long-term, unraveling these complex linkages by employing an empirical approach.

This study benefits from a comprehensive set of econometric tools, including a multiequation vector autoregressive (VAR) system, Granger causality test, impulse response function, variance decomposition and a single-equation autoregressive distributed lag (ARDL) system. This rigorous approach enables to identify both short- and long-run dynamics to unravel the intricate linkages between Brent oil prices, housing prices, gold prices and stock prices in the UK and Norway over the period from 2005:Q1 to 2022:Q2.

The findings indicate that rising oil prices negatively impact house prices, whereas the positive influence of stock market performance on housing is more pronounced. A two-way causal relationship exists between stock market indices and house prices, whereas a one-way causal relationship exists from crude oil prices to house prices in both countries. The VAR model reveals that past housing prices, stock market indices in each country and Brent oil prices are the primary determinants of current housing prices. The single-equation ARDL results for housing prices demonstrate the existence of a long-run cointegrating relationship between real estate and stock prices. The variance decomposition analysis indicates that oil prices have a more pronounced impact on housing prices compared with stock prices. The findings reveal that shocks in stock markets have a greater influence on housing market prices than those in oil or gold prices. Consequently, house prices exhibit a stronger reaction to general financial market indicators than to commodity prices.

This study may have several limitations. First, the model does not include all relevant macroeconomic variables, such as interest rates, unemployment rates and gross domestic product growth. This omission may affect the accuracy of the model’s predictions and lead to inefficiencies in the real estate market. Second, this study does not consider alternative explanations for market inefficiencies, such as behavioral finance factors, information asymmetry or market microstructure effects. Third, the models have limitations in revealing how predictors react to positive and negative shocks. Therefore, the results of this study should be interpreted with caution.

These findings hold significant implications for formulating dynamic policies aimed at stabilizing the housing markets of these two oil-producing nations. The practical implications of this study extend to academics, investors and policymakers, particularly in light of the volatility characterizing both housing and commodity markets. The findings reveal that shocks in stock markets have a more profound impact on housing market prices compared with those in oil or gold prices. Consequently, house prices exhibit a stronger reaction to general financial market indicators than to commodity prices.

These findings could also serve as valuable insights for future research endeavors aimed at constructing models that link real estate market dynamics to macroeconomic indicators.

Using a variety of econometric approaches, this paper presents an innovative empirical analysis of the intricate relationship between euro property prices, stock prices, gold prices and oil prices in the UK and Norway from 2005:Q1 to 2022:Q2. Expanding upon the existing literature on housing market price determinants, this study delves into the role of gold and oil prices, considering their impact on industrial production and overall economic growth. This paper provides valuable policy insights for effectively managing the impact of oil price shocks on the housing market.

The housing market in Kenya continues to experience an excessive imbalance between supply and demand. This imbalance renders the housing market volatile, and stakeholders lose repeatedly. The purpose of the study was to forecast housing prices (HPs) in Kenya using simple and complex regression models to assess the best model for projecting the HPs in Kenya.

The study used time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited. Linear regression, multiple regression, autoregressive integrated moving average (ARIMA) and autoregressive distributed lag (ARDL) models regression techniques were used to model HPs.

The study concludes that the performance of the housing market is very sensitive to changes in the economic indicators, and therefore, the key players in the housing market should consider the performance of the economy during the project feasibility studies and appraisals. From the results, it can be deduced that complex models outperform simple models in forecasting HPs in Kenya. The vector autoregressive (VAR) model performs the best in forecasting HPs considering its lowest root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and bias proportion coefficient. ARIMA models perform dismally in forecasting HPs, and therefore, we conclude that HP is not a self-projecting variable.

A model for projecting HPs could be a game changer if applied during the project appraisal stage by the developers and project managers. The study thoroughly compared the various regression models to ascertain the best model for forecasting the prices and revealed that complex models perform better than simple models in forecasting HPs. The study recommends a VAR model in forecasting HPs considering its lowest RMSE, MAE, MAPE and bias proportion coefficient compared to other models. The model, if used in collaboration with the already existing hedonic models, will ensure that the investments in the housing markets are well-informed, and hence, a reduction in economic losses arising from poor market forecasting techniques. However, these study findings are only applicable to the commercial housing market i.e. houses for sale and rent.

While more research has been done on HP projections, this study was based on a comparison of simple and complex regression models of projecting HPs. A total of five models were compared in the study: the simple regression model, multiple regression model, ARIMA model, ARDL model and VAR model. The findings reveal that complex models outperform simple models in projecting HPs. Nonetheless, the study also used nine macroeconomic indicators in the model-building process. Granger causality test reveals that only household income (HHI), gross domestic product, interest rate, exchange rates (EXCR) and private capital inflows have a significant effect on the changes in HPs. Nonetheless, the study adds two little-known indicators in the projection of HPs, which are the EXCR and HHI.

In light of Bangladesh’s economy, the goal of this study is to examine the “Twin Deficit Hypothesis (TDH),” which refers to a link between the budget deficit and the current account deficit. This study used yearly time series data from 1980 to 2020 to investigate the phenomena.

A multivariate autoregressive distributive lag (ARDL) model has been presented for empirical investigation, with the ARDL bound test investigating the co-integration between the inadequacies. As some of the variables in the bound test lack co-integration, the study adds a multivariate vector autoregressive (VAR) model later on.

With evidence of the result, the study supports the validation of twin deficit hypothesis in Bangladesh economy since both current account deficit and fiscal deficit affects each other significantly whereas Granger causality test confirms that fiscal deficit causes current account deficit but not the other way around.

The government should maintain a restrictive monetary policy in order to stabilize the current account deficit.

The novelty of this study is the incorporation of inflation, real exchange rate and GDP per capital to TDH that together form the basis for a macroeconomic snapshot of the economy.

The main aim of this paper is to examine the mechanism of determining the exchange rate of the US dollar against the Indonesian rupiah (USD/IDR) by market players to manage the USD/IDR exchange rate stability. Thus, this study is expected to provide a better understanding of the determinants of the USD/IDR, given that the data set completely encompasses all the USD/IDR transactions in the Indonesian foreign exchange market. Order flow data used in this study cover all transactions on the USD/IDR conducted by domestic residents including both individuals and corporations and foreign investors in the Indonesian foreign exchange market.

This study covers the data set over the period January 3, 2011 to December 31, 2015, and the vector autoregression and autoregressive distributed lag models are used in examining the research questions. More particularly, in this study, the author examines whether the net total domestic individual transactions (DOVA), net total domestic corporation transactions (KOVA), net total foreign investor transactions (IOVA), Asian Dollar Index (ADXY), non-deliverable forward (NDF) for USD/IDR and Volatility Index (VIX) are statistically significant determinants of the USD/IDR exchange rate.

Overall, this study suggests that in the short run, lag of the USD/IDR exchange rate or inertia level, lag of the IOVA, lag of the NDF of the USD/IDR exchange rate and lag of the ADXY are statistically significant determinants of the USD/IDR. On the other hand, in the long run, DOVA, NDF and ADXY are found to be statistically significant determinants of USD/IDR. This study also found that there is a market leader and asymmetric information among market players in the Indonesian foreign exchange market, and their USD/IDR exchange rate level becomes a reference for other market players when conducting transactions with each other.

The paper is original along two lines. First, the data set used in this study is unique. It encompasses all the USD/IDR transactions in the Indonesian foreign exchange market. The order flow data used in this study cover all transactions on the USD/IDR conducted by domestic residents (includes both individuals and corporations) and foreign investors in the Indonesian foreign exchange market. Such an approach has not been used previously to study the exchange rate behavior in an emerging market. Second, there is limited knowledge on Indonesia’s exchange rate dynamics. This study fills this gap.

The British government takes equity issues formally into account in its appraisal of social projects and policies. However, evidence on which the measured distributional welfare weights are based is neither broad enough nor sufficiently reliable. This paper seeks to address these issues by considering a wider body of evidence.

An important component of the welfare weight measure advocated by HM Treasury is the elasticity of marginal utility of consumption (e). A critical review of existing evidence on e is provided with a view to establishing priority areas for further research. New measures of e are presented based on revealed social values as indicated in specific government policies relating to both foreign aid and proposed income‐related fines for offences. Behavioural evidence based on demand analysis using a co‐integration approach is also presented.

The results for e are sensitive to the estimation approach adopted. While the evidence based on a revealed social values approach including modified tax‐based results suggests that e is close to unity, the measure currently used by HM Treasury, demand analysis suggests an e value close to 1.5. The evidence based on lifetime consumption behaviour is sensitive to model specification and needs updating.

Modified tax‐based findings on e are presented along with new evidence based on alternative revealed social values approaches. The new evidence from demand analysis is based on an Autoregressive Distributed Lag (ARDL) approach to co‐integration. This paper will be of interest to academics specialising in welfare economics and to practitioners involved in social project appraisal.

The purpose of this paper is to analyse the long-run relationship between geopolitical risk and exchange rates in four ASEAN countries.

We augment theoretical nominal exchange rate models available in the literature with the geopolitical risk index developed by Caldara and Iacoviello (2019), and then estimate these models using the ARDL approach to Cointegration.

Our analysis uncovers evidence of Cointegration in the exchange rate models when the MYR-USD, IDR-USD, THB-USD and PHP-USD exchange rates are used as dependent variable. Next, geopolitical risk is a significant long-run driver for these exchange rates. Third, in all countries higher geopolitical risk leads to a depreciation of domestic currency.

There are implications for entrepreneurs, central banks, portfolio managers and arbitrageurs who actively trade in financial markets. Financial market players can benefit from a better understanding of how geopolitical events affect the portfolio of financial assets across various countries, while entrepreneurs can work out hedging strategies.

This is a contribution to the study of interlinkages between political risk and foreign exchange markets. It is the first study to adopt the geopolitical risk index of Caldara and Iacoviello (2019) to the study the foreign exchange markets of ASEAN countries.

The purpose of this paper is to investigate the dynamic relationship between 19 pandemic and government actions, such as governmental response index and economic support packages.

The authors use a panel dataset of 10 American and Latin countries for the period spanning from January 2020 to April 2021 to analyze the effect of government actions on stock market returns. The authors provide robust test results that improve the understanding of the impact of the pandemic on stock market indices through the break-up structure method and the new measure of Covid-19 extracted from Narayan et al. (2021) study.

Empirical results show the harmful effect of the corona virus on stock prices, hence the risk adverse behavior of investors. On the other hand, the quantitative approach reveals that the positive impact of government actions is degraded during Covid-19.

This article highlight that government actions may be effective in reducing new infections but could generate perverse economic impact through increasing uncertainty. The authors conclude that the adjustment of macroeconomic factors and the integration of financial news improve the forecasting performance of the model based on health news.

This paper aims to model the relationship between oil price and six major agricultural commodity prices using monthly data from January 1997 to December 2016.

The authors use both the linear autoregressive distributed lag by Pesaran et al. (2001) and the nonlinear autoregressive distributed lag by Shin et al. (2014), and they also account for structural breaks using the Bai and Perron (2003) test that allows for multiple structural changes in regression models.

These findings are discernible from the authors’ analyses. First, the linear analysis indicates a significant positive effect of oil prices on the agricultural commodity prices, which supports evidence on the non-neutrality hypothesis. Second, oil price asymmetries seem to matter more when dealing with agricultural commodity prices, except for groundnut. Third, it may be necessary to pre-test for structural breaks when modelling the relationship between oil price and agricultural prices regardless of the commodity being analysed. Fourth, the asymmetric effect for the agricultural commodity prices is non-neutral to oil prices, except for rice in the case of structural breaks.

This paper contributes to the on-going debate on the oil–agricultural commodity nexus using the recent technique of asymmetry and also considering the role structural breaks play in the relationship between oil price and agricultural commodity prices.

The idea of this study is to provide a solid Financial Condition Index (FCI) that allows the monetary transmission policy to be monitored in a country which in recent decades has suffered from major financial and monetary crises.

The authors construct three FCIs for Mexico to analyse the role of financial asset prices in formulating monetary policy under an inflation-targeting regime. Using monthly data from 1995 to 2017, the authors estimate FCIs with two different methodologies and build the index by taking into account the mechanism of transmission of monetary policy and incorporating the most relevant financial variables.

This study’s results show that, likewise for developing countries as Mexico, an FCI could be a useful tool for managing monetary policy in reducing macroeconomic fluctuations.

Apart from building a predictor of possible financial stress, the authors construct an FCI for a central bank that pursues inflation targeting and to analyse the role of financial asset prices in formulating monetary policy.

1. We construct three FCIs for Mexico to analyse the role of financial asset prices in formulating monetary policy under an inflation-targeting regime.

2. The FCIs are based on (1) a vector autoregression model (VAR); (2) an autoregressive distributed lag model (ARDL) and (3) a factor-augmented vector autoregression model (FAVAR).

3. FCI could become a new target for monetary policy within a hybrid inflation-targeting framework.

4. FCI could be a good tool for managing monetary policy in developing countries with a low-inflation environment.

We construct three FCIs for Mexico to analyse the role of financial asset prices in formulating monetary policy under an inflation-targeting regime.

The FCIs are based on (1) a vector autoregression model (VAR); (2) an autoregressive distributed lag model (ARDL) and (3) a factor-augmented vector autoregression model (FAVAR).

FCI could become a new target for monetary policy within a hybrid inflation-targeting framework.

FCI could be a good tool for managing monetary policy in developing countries with a low-inflation environment.