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Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

The purpose of this paper is to determine the effect of the industrial sector, renewable energy consumption and nonrenewable energy consumption in Indonesia on the ecological footprint from 1990 to 2020 in the short and long term.

This paper uses vector error correction model (VECM) analysis to examine the relationship in the short and long term. In addition, the impulse response function is used to enable future forecasts up to 2060 of the ecological footprint as a measure of environmental degradation caused by changes or shocks in industrial value-added, renewable energy consumption and nonrenewable energy consumption. Furthermore, forecast error decomposition of variance (FEVD) analysis is carried out to predict the percentage contribution of each variable’s variance to changes in a specific variable. Granger causality testing is used to enhance the analysis outcomes within the framework of VECM.

Using VECM analysis, the speed of adjustment for environmental damage is quite high in the short term, at 246%. This finding suggests that when there is a short-term imbalance in industrial value-added, renewable energy consumption and nonrenewable energy consumption, the ecological footprint experiences a very rapid adjustment, at 246%, to move towards long-term balance. Then, in the long term, the ecological footprint in Indonesia is most influenced by nonrenewable energy consumption. This is also confirmed by the Granger causality test and the results of FEVD, which show that the contribution of nonrenewable energy consumption will be 10.207% in 2060 and will be the main contributor to the ecological footprint in the coming years to achieve net-zero emissions in 2060. In the long run, renewable energy consumption has a negative effect on the ecological footprint, whereas industrial value-added and nonrenewable energy consumption have a positive effect.

For the first time, value added from the industrial sector is being used alongside renewable and nonrenewable energy consumption to measure Indonesia’s ecological footprint. The primary cause of Indonesia’s alarming environmental degradation is the industrial sector, which acts as the driving force behind this issue. Consequently, this contribution is expected to inform the policy implications required to achieve zero carbon emissions by 2060, aligned with the G20 countries’ Bali agreement of 2022.

This study investigates the impact of the COVID-19 pandemic on financial stability in Vietnam, a developing country characterized by a bank-based financial system.

Using a sample of daily data from January 23, 2020 to June 30, 2022, the VECM and NARDL models are employed to study Vietnam’s financial stability in face of the COVID-19 disaster. Following the literature on COVID-19, the authors measure the impact of the pandemic by the number of daily infected cases and the national lockdown. Given the reliance of the Vietnamese government on the banking system to regulate the economy, the authors evaluate financial stability from the interbank market and stock market perspectives.

The authors find that the pandemic imposes a destructive effect on financial stability during the early time of the pandemic; however, the analysis with an extended period indicates that this effect gradually fades in the long term. In addition, from the NARDL results, the authors reveal an asymmetric relationship between the financial market and the COVID-19 pandemic in both short term and long term.

An implication drawn from this study is that unprecedented health disasters should be resolved by unprecedented stringent countermeasures when conventional methods are ineffective. Although rigorous remedies may increase short-term liabilities, their implementation quickly ceases disease diffusion and helps an economy enter the recovery stage in a timelier manner.

The study is the first to examine the impact of the COVID-19 pandemic on financial stability, via the interbank market lens, in a developing country that relies on the bank-based financial system.

This study aims to investigate the effect of monetary policy on housing prices for US economy. It specifically examines whether nominal or real interest rates are the key drivers behind fluctuations in housing prices in US.

Monthly data from January 1991 to July 2023 and various appropriate analytical tools such as unit root tests, Johansen’s cointegration test, vector error correction model (VECM), impulse response function and Granger causality test were applied for the data analysis.

The Johansen cointegration findings reveal the presence of a long-term relationship among the variables. VECM results indicate a negative correlation between nominal and real interest rates and housing prices in both the short and long terms, suggesting that a strict monetary policy can help in controlling the housing price increase in the USA. However, housing prices are more responsive to changes in nominal interest rates than to real interest rates. Additionally, the study reveals that the COVID-19 pandemic contributed to the upsurge in housing prices in the USA.

This study contributes by examining the role that nominal or real interest rates play in shaping housing prices in the USA. Moreover, given the recent significant upsurge in housing prices, this study presents a unique opportunity to investigate whether these price increases are influenced by the Federal Reserve's monetary policy decisions regarding nominal or real interest rates. Additionally, using monthly data, this study provides a deeper understanding of the fluctuations in housing prices and their connection to monetary policy tools.

This paper investigates the relationship between domestic gold prices and inflation in Vietnam based on the monthly series of the gold price index and consumer price index over the period of December 2001–July 2020.

The co-integration between the domestic gold price and inflation is examined within the autoregressive distributed lag-error correction (ARDL bounds testing) framework. This paper also applies the vector error correction model (VECM) and impulse response function analysis to explore the causal relationship between these two variables. Moreover, since both gold and inflation series are likely to have structural changes over time, a unit root test controlling for significant breaks is employed in this paper.

Findings from the ARDL bounds testing model suggest the presence of a co-integration between the underlying variables. The VECM indicates that shocks in inflation lead to a negative response to gold prices in the long run. In the short term, only fluctuations in gold prices impact inflation, and this causality is unidirectional.

Gold is regarded as a critical financial asset to preserve wealth from inflation pressure in the case of Vietnam. These findings propose implications for both investors and policymakers.

Empirical results suggest that inflation has a long-term impact on gold prices in the Vietnamese market. In the existence of a permanent inflationary shock, domestic prices of gold respond negatively to this shock; hence, gold can act as a good hedge against inflation in Vietnam.

The purpose of this paper is to examine the impact of trade openness on the cost of financial intermediation and bank performance. Developed and developing countries are currently pursuing trade openness to achieve higher bank performance with less intermediation costs.

In attaining the study's objectives, several regression methodologies were employed (i.e. system generalized method of moments (GMM), fixed effect, pooled ordinary least squares (OLS) and vector error correction model (VECM)). The authors tested the hypothesis on data of 885 banks from BRICS countries, which span 18 years (2000–2017).

The results from this robust study showed that embedding higher trade openness reduces financial intermediation costs and improves banks' performance. The results remain robust following the use of different estimation methods and alternative variables as proxies. In addition, results were still valid upon considering bank level, industry level and country level as control variables. It was also observed that the relation pattern holds its rigidity during “good” and “bad” times (i.e. the global financial crisis).

The results provide better references for bank regulators, academics and policymakers to take advantage of the low financial intermediation costs resulting from trade openness.

This research aims to determine the factors that affected Bitcoin price return in the period before and during the COVID-19 pandemic.

The independent variables used in this study are hashrate, transaction volume, social media and some macroeconomics variables. The data are processed using the vector error correction model (VECM) to determine the short-term and long-term relationships between variables.

The research shows that (1) Twitter and Gold significantly affected Bitcoin in the short term before the COVID-19 pandemic; (2) hashrate, transaction volume, Twitter and the financial stress index had a significant effect on Bitcoin in the long term before the COVID-19 pandemic; (3) the volatility index had a significant effect on Bitcoin in the short term during the COVID-19 pandemic; and (4) hashrate, transaction volume, Twitter and CHF/USD had a significant effect on Bitcoin in the long term during the COVID-19 pandemic.

This research provides explanation about factors affecting Bitcoin so investors and regulators can pay more attention and prepare for the potential risks as well as to get a good understanding of market conditions for greater crypto adoption in the future.

The novelty in this study is the various factors driving the Bitcoin price were analyzed before and during the COVID-19 pandemic including the social media, as sentiment, interestingly, is being a predictive power for Bitcoin price return.

The purpose of this study is to examine the effect of Islamic banks (IBs) and macroeconomic variables on economic growth in Saudi Arabia, the United Arab Emirates, Kuwait, Malaysia, Qatar, Bahrain and Bangladesh.

Based on these criteria, 672 observations from 24 IBs in Saudi Arabia, the United Arab Emirates, Kuwait, Malaysia, Qatar, Bahrain and Bangladesh were chosen for further investigation. Time series analysis is a well-known method for determining if model variables are stationary and how long-term relationships function through cointegration analysis. This study uses impulse response function (IRF) and variance decomposition (VD) methodologies to demonstrate how each macroeconomic variable shock influences the short-term dynamic path of all system variables.

Islamic banking promotes economic growth, especially in Saudi Arabia, the UAE, Kuwait, Malaysia, Qatar, Bahrain and Bangladesh. The findings of the Islamic banking VDC test have a direct and long-term effect on economic growth.

The literature on this topic can be improved in a number of ways, including by adopting a more robust method to analyze over a longer time frame. By researching specific financing in various areas of the economy, one can gain a deeper understanding of Islamic financing. This will enable the identification of sectors that contribute to economic expansion. Future research should examine combining nations with pure Islam and dual-banking systems to acquire sufficient data.

This paper has practice and research implications. It recommends adopting the nation’s successful experiment with the Islamic banking system as a model for attaining economic growth through Islamic financing. To replicate this successful experiment, government-based decision-makers and monetary policy experts must collaborate to make Islamic money flows simple and rapid through financial channels that enhance economic growth.

The study of the contribution of Islamic banking to economic growth in developing nations, particularly those with the highest total assets (TAs) and total deposits (TDs) in the world, remains of modest value. To the best of the authors’ knowledge, this is the first study to empirically assess the impact of IBs in developing nations, particularly those with the highest TAs and TDs in the world, on economic growth as measured by gross domestic product (GDP).

The Russian Federation is one of the world’s largest exporters of fossil-based energy sources such as oil, natural gas and coal. Approximately 90% of the energy production in the Russian Federation consists of oil, natural gas and coal. Renewable energy (RE) in the Russian Federation mainly comprises hydroelectric energy. The purpose of this paper is to identify the factors that influenced the growth of RE resources in the Russian Federation between 1990 and 2020.

The unit root tests augmented Dickey and Fuller and Phillips and Perron, as well as Johansen cointegration and Granger causality approaches, were used. This study was conducted using vector error correction models for the years 1990–2020.

The cointegration method's findings demonstrate that while a rise in non-RE sources has a negative impact on RE development, an increase in income, energy consumption, trade openness and CO₂ emissions has a favorable impact on RE expansion. The vector error correction model Granger causality test also shows a unidirectional relationship between RE and non-RE sources, gross domestic product, energy consumption and CO₂ emissions. Trade openness, on the other hand, has no causal association with RE.

The Russian Federation must consider the practical implications of RE sources. However, there is a greater need for the Russian Federation to frame sound energy policies for RE development.

This paper aims to fill a gap in the literature on Russian RE development. Furthermore, the results of the methodological analysis can be used to guide policymakers in the field of RE development. This paper is also more policy-relevant and is quite useful in the context of sustainable energy development.

This paper aims to understand the effect of a change in Economic Policy Uncertainty (EPU) of India and the USA on investors' sentiment in the Indian context, consisting of Sensex returns and volatility index (Vix).

The authors employ bounds testing approach to cointegration to capture the short-and long-run effects of EPU on investors' sentiment, along with impulse response functions and variance decompositions to check the effect of a shock on Sensex and Vix.

The study concludes the existence of a cointegrating relationship for both models, that is, Vix and Sensex. In the long-run, changes in EPU_India affect Vix and Sensex positively and negatively, respectively. On the other hand, EPU_USA affects Vix and Sensex positively. Furthermore, Gregory and Hansen (1996) cointegration with endogenous structural break reveals a long-run cointegrating relationship for both models.

The effect of EPUs on investors' sentiment reveals that when there is an uncertain event that adversely affects the stock prices, investors should not make haste to take a decision as the impact on stock prices perturbation might be temporary. Therefore, one should persevere for the dip in prices to hit the desired target.

Various studies look at the effect of cross-country EPU on the home country, However, there is no such study in the Indian context. The present study examines the impact of India's EPU on investors' sentiments after controlling the USA's EPU, one of India's largest trading partners and a key determinant of global economic policy.