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This paper investigates how various strategies for combining forecasts, both simple and optimised approaches, are compared with popular individual risk models in estimating value-at-risk (VaR) and expected shortfall (ES) in emerging market at alternative risk levels.

Using the case study of the Vietnamese stock market, the author produced one-day-ahead VaR and ES forecast from seven individual risk models and ten alternative forecast combinations. Next, the author employed a battery of backtesting procedures and alternative loss functions to evaluate the global predictive accuracy of the different methods. Finally, the author investigated the relative performance over time of VaR and ES forecasts using fluctuation test.

The empirical results indicate that, although combined forecasts have reasonable predictive abilities, they are often outperformed by one individual risk model. Furthermore, the author showed that the complex combining methods with optimised weighting functions do not perform better than simple combining methods. The fluctuation test suggests that the poor performance of combined forecasts is mainly due to their inability to cope with periods of instability.

This study reveals the limitation of combining strategies in the one-day-ahead VaR and ES forecasts in emerging markets. A possible direction for further research is to investigate whether this finding holds for multi-day ahead forecasts. Moreover, the inferior performance of combined forecasts during periods of instability motivates further research on the combining strategies that take into account for potential structure breaks in the performance of individual risk models. A potential approach is to improve the individual risk models with macroeconomic variables using a mixed-data sampling approach.

First, the authors contribute to the literature on the forecasting combinations for VaR and ES measures. Second, the author explored a wide range of alternative risk models to forecast both VaR and ES with recent data including periods of the COVID-19 pandemic. Although forecast combination strategies have been providing several good results in several fields, the literature of forecast combination in the VaR and ES context is surprisingly limited, especially for emerging market returns. To the best of the author’s knowledge, this is the first study investigating predictive power of combining methods for VaR and ES in an emerging 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.

Might the impact of the financial stress on exchange markets be asymmetric and exposed to regime changes? Departing from the existing literature, highlighting that the domestic and foreign financial stress in terms of money market have substantial effects on exchange market, this paper aims to investigate the impacts of the bond yield spreads of three emerging countries (Mexico, Russia, and South Korea) on their exchange market pressure indices using monthly observations for the period 2010:01–2019:12. Additionally, the paper analyses the impact of bond yield spread of the US on the exchange market pressure indices of the three mentioned emerging countries. The authors hypothesized whether the negative and positive changes in the bond yield spreads have varying effects on exchange market pressure indices.

To address the research question, we measure the bond yield spread of the selected countries by using the interest rate spread between 10-year and 3-month treasury bills. At the same time, the exchange market pressure index is proxied by the index introduced by Desai et al. (2017). We base the empirical analysis on nonlinear vector autoregression (VAR) models and an asymmetric quantile-based approach.

The results of the impulse response functions indicate that increases/decreases in the bond yield spreads of Mexico, Russia and South Korea raise/lower their exchange market pressure, and the effects of shocks in the bond yield spreads of the US also lead to depreciation/appreciation pressures in the local currencies of the emerging countries. The quantile connectedness analysis, which allows for the role of regimes, reveals that the weights of the domestic and foreign bond yield spread in explaining variations of exchange market pressure indices are higher when exchange market pressure indices are not in a normal regime, indicating the role of extreme development conditions in the exchange market. The quantile regression model underlines that an increase in the domestic bond yield spread leads to a rise in its exchange market pressure index during all exchange market pressure periods in Mexico, and the relevant effects are valid during periods of high exchange market pressure in Russia. Our results also show that Russia differs from Mexico and South Korea in terms of the factors influencing the demand for domestic currency, and we have demonstrated the role of domestic macroeconomic and financial conditions in surpassing the effects of US financial stress. More specifically, the impacts of the domestic and foreign financial stress vary across regimes and are asymmetric.

This study enriches the literature on factors affecting the exchange market pressure of emerging countries. The results have significant economic implications for policymakers, indicating that the exchange market pressure index may trigger a financial crisis and economic recession.

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.

In this study, the authors propose a VaR method for evaluating the market risk of investing in the stock portfolio of Pension Institutions. The data used for this research is hypothetical data, including the exposure or the amount of value invested by Pension Institutions in their stock portfolio. With the VaR – Monte Carlo simulation, the authors know the loss level will occur when the Indonesian economy or market conditions deteriorate. The lost value amount is determined in the Rupiah value, according to the confidence level or the desired percentile level. The results revealed that at the 5% (99.95%) percentile level of confidence, a pension fund with an investment value of IDR 4,070,000,000 would suffer a loss of IDR 1,110,000,000. While at the 1% (99.995%), the loss rate will be of IDR 1,480,000,000. The conclusion is that the results of this study are useful for Pension Institutions in managing their asset portfolios with the VaR model.

The main purpose of this paper is to investigate the dynamic effects of the oil price shocks on Iran’s inflation in the period 1993:2–2018:2

The main purpose of this paper is to investigate the dynamic effects of the oil price shocks on Iran’s inflation in the period 1993:2–2018:2 using the time-varying parameter vector autoregressive (TVP-VAR) model. The dynamics of the results enable us to study the amount and severity of the impact of the oil price shocks on inflation with the distinction of the sanctioned and non-sanctioned periods. The volume of oil export is used to identify the effective oil sanctions. The period is divided into sanctioned and non-sanctioned periods by Markov switching model.

The results show that the pass-through of oil price shocks into Iran’s inflation are time-varying, and there are significant differences at sanction period from other time horizons. An increase in oil price has a positive effect on inflation and its effects are stronger during the sanctions period. It is also observed that the producer price index is more sensitive to changes in the oil price than the consumer price index. The necessity of the government’s earnest efforts to improve international relations to lift the sanctions, along with diversification of exports, and making the economy of Iran independent of oil revenues is obvious.

In addition to the exogenous oil price shocks, Iran’s economy faces numerous restrictions for its oil exports due to the sanctions. The main purpose of this paper is to investigate the dynamics effects of the oil price shocks on Iran’s inflation in the period 1993:2–2018:2 using the time-varying parameter vector autoregressive (TVP-VAR) model. The dynamics of the results enable us to study the amount and severity of the impact of the oil price shocks on inflation with the distinction of the sanctioned and non-sanctioned periods. The volume of oil export is used to identify the effective oil sanctions.

The increased capital requirements and the implementation of new liquidity standards under Basel III sparked various concerns among researchers, academics and other stakeholders. The question is whether Basel III regulation is ideal, that is, adequate to deal with a crisis, such as the 2007–2009 global financial crisis? The purpose of this paper is threefold: First, perform a stress testing exercise on the US banking sector, while examining liquidity and solvency risk indicators jointly under the Basel III regulatory framework. Second, allow the study to cover the post-crisis period, while referring to key Basel III regulatory requirements. And third, focus on the resilience of domestic systemically important banks (D-SIBs), which are supposed to support the US financial system in times of stress and therefore whose failure causes the entire financial system to fail.

The authors used a sample of the 24 largest US banks observed over the period Q1-2015 to Q1-2021 and a scenario-based vector autoregressive conditional forecasting approach.

The authors found that the model successfully produces accurate forecasts and simulates the responses of the solvency and liquidity indicators to different real and historical macroeconomic shocks. The authors also found that the US banking sector is resilient and can withstand both historical and hypothetical macroeconomic shocks because of its compliance with the Basel III capital and liquidity regulations, which consist of encouraging banks to hold high-quality liquid assets and stable funding resources and to strengthen their capital, which absorbs the losses incurred in a crisis.

The authors developed a framework for testing the resilience of the US banking sector under macroeconomic shocks, while examining liquidity and solvency risk indicators jointly under Basel III regulatory framework, a point not yet well studied elsewhere, and most studies on this subject are based on precrisis data. The authors also focused on the resilience of D-SIBs, whose failure causes the failure of the entire financial system, which previous studies have failed to examine.

The present article aims to estimate an autoregressive vector model covering the period of 1990–2021 to analyze the effect of public spending and monetary supply increases in economic activity in Morocco.

A literature review on the policy of recovery with fiscal and monetary tools and its theoretical foundations was established. Then, an empirical study on the Moroccan context was executed to study the effectiveness of these instruments in Morocco from 1990 to 2021, using autoregressive vector modeling.

The results present a state of a positive relationship and statistical significance of public spending, money supply and economic growth. The impulse response function analysis and the forecast error variance decomposition showed that public spending does not have a large impact on gross domestic product, while the money supply has a real power to stimulate the growth of economic activity in Morocco.

This study aims to demonstrate the positive effect of the coordination of public spending and monetary supply increases on gross domestic product in Morocco. Additionally, the analysis using vector autoregressive modeling, impulse response functions, variance decomposition techniques and causality tests, provides crucial insights to guide researchers, practitioners and policymakers in developing more effective and resilient economic strategies. The findings from this study not only illuminate immediate recovery strategies but also contribute to strengthening the resilience of economies against potential future shocks.

The study focuses on examining the impact of the supply shock on the Indian macroeconomic variables during the COVID-19 period.

Time-varying factor augmented vector autoregressive model has been employed to study the asymmetry in transmission of supply shock on Indian economy during pre- and post-COVID-19 times.

The authors find that with supply shock, retail food inflation outpaced in COVID-19 times. Production levels reported by IIP fell to abysmally low levels in the post-COVID-19 times when the economy stalled. The liquidity stimulus provided by the central bank led to the negative response of policy rates to the supply shocks during the COVID-19 times.

The study stands novel in examining the impact of COVID-19 pandemic on Indian economy through the lenses of asymmetric transmission of supply shock during pre- and post-COVID-19 times.

This study aims at exploring the nexus between herding behavior and the spillover effect in G7 and BRICS stock markets.

The authors used the dynamic connectedness approach TVP-VAR model of Antonakakis et al. (2019) to capture the spillovers across different markets. Moreover, to explore herding behavior, the authors used a modified version of the CSAD measure of Chang et al. (2000) including extreme market movements. Finally, to study the link between these two phenomena, the authors estimated a DCC-GARCH model.

The results show that herding behavior exists in the American market and some BRICS markets. Furthermore, spillover between G7 and BRICS increases in times of crisis. Moreover, the authors find a dynamic conditional correlation between herding behavior and spillovers both in the short and long run. The authors conclude that in times of crisis, the transmission of shocks between markets is more frequent, fuelling uncertainty and pushing investors to suppress their own beliefs and follow the general market trends.

This paper uses the TVP-VAR model to explore the spillover effect and the DCC-GARCH model to explore the connectedness between herding behavior and the spillover effect in G7 and BRICS countries in both the short and long run.