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In addition to the seminal approach of Markowitz (1952) that is based on finding the optimal budget shares for minimizing risk, the authors also make use of the approach developed by Hatemi-J and El-Khatib (2015), which is built on finding the weights as budget shares for maximizing the risk-adjusted return of the underlying portfolio. For testing the stability of the portfolio benefits, the asymmetric interaction between oil, equity and bonds is tested.

Oil is a major investment commodity. The literature shows mixed results regarding oils' ability to provide diversification benefits. This paper re-examines this issue by applying a new portfolio optimization approach.

The authors find that oil still yields portfolio diversification benefits; contrary to the traditional Markowitz portfolio approach, the asymmetric causality test results show that oil does not cause bonds for either positive or negative changes; however, oil does cause stocks but only for stocks' negative changes. Hence, oil can still make the returns of a portfolio of stocks and bonds unstable through oil's effect on stocks.

This is the first attempt to investigate the potential portfolio diversification benefits of stocks, bonds and oil by using the combination of risk and return explicitly in the optimization problem. The new insights provided by this article might be valuable to the investors, financial institutions and policy makers.

The current paper proposes a prediction model for a cryptocurrency that encompasses three properties observed in the markets for cryptocurrencies—namely high volatility, illiquidity, and regime shifts. As far as the authors’ knowledge extends, this paper is the first attempt to introduce a stochastic differential equation (SDE) for pricing cryptocurrencies while explicitly integrating the mentioned three significant stylized facts.

Cryptocurrencies are increasingly utilized by investors and financial institutions worldwide as an alternative means of exchange. To the authors’ best knowledge, there is no SDE in the literature that can be used for representing and evaluating the data-generating process for the price of a cryptocurrency.

By using Ito calculus, the authors provide a solution for the suggested SDE along with mathematical proof. Numerical simulations are performed and compared to the real data, which seems to capture the dynamics of the price path of two main cryptocurrencies in the real markets.

The stochastic differential model that is introduced and solved in this article is expected to be useful for the pricing of cryptocurrencies in situations of high volatility combined with structural changes and illiquidity. These attributes are apparent in the real markets for cryptocurrencies; therefore, accounting explicitly for these underlying characteristics is a necessary condition for accurate evaluation of cryptocurrencies.

The purpose of this paper is to offer a review of the trade policy in the UAE. It also investigates the dynamic interaction between trade openness and GDP per capita in this emerging economy.

The asymmetric generalized impulse response functions and the asymmetric causality tests developed by Hatemi-J are used.

The results from asymmetric generalized impulse response functions indicate that a positive permanent shock in the trade openness results in a significant positive response in the cumulative sum of the positive component of the GDP per capita. Such a response is not found for the negative shocks in the trade openness. Furthermore, neither a positive nor a negative shock in the GPD per capita results in any significant response in the trade openness. These empirical findings are also supported by the implemented asymmetric causality tests.

This is the first attempt that investigates the impact of trade openness on economic performance in the UAE. Unlike previous literature on the topic, this paper allows for asymmetric impacts in the empirical model.

This study aims to investigate the dynamic relationship between renewable and non-renewable energy sources on the economic growth of eight countries, the capital stock and labour force being used as control variables in each case. Questions that need to be asked include the following: Is there is an asymmetric and, hence, a non-linear relationship between variables? If yes, how does economic growth interact with both renewable and non-renewable energy consumption (EC)? How different are these relationships in the countries highly rated in the performance of renewable EC compared to those lowly rated?

This paper uses asymmetric quantile-based methods to extract possible asymmetric and, hence, non-linear relationships between the underlying variables.

A newly developed asymmetric panel quantile approach suggests that EC has a significant effect on economic growth in both directions of shocks as well as for the considered sample. The results further support the findings in recent literature on renewable energy deployment, given the importance of renewable EC for economic growth with the increased levels of renewable EC, although the initial investments may have a negative effect on economic growth for some countries.

This study contributes to the literature in twofold. Firstly, it aims to contribute to the ongoing debate in literature by incorporating both renewable and non-renewable energy resources in the production function with labour and capital to test their asymmetric impact on economic growth. Secondly, this paper uses asymmetric quantile-based methods to extract possible asymmetric and, hence, non-linear relationships between the underlying variables. Another point that should be emphasised in this study is the need for studies analysing economic growth and EC for a sample of G20 countries based on a comparative view for the renewable and non-renewable EC in literature.

This paper investigates the dynamic relationship between the trade-weighted dollar exchange rates and the oil prices in the world market. Monthly data during 1980–2017 are used for this purpose.

The symmetric and asymmetric generalized impulse response functions are estimated for these important economic indicators.

The empirical findings show that if the dollar rate increases (i.e. the dollar depreciates), the oil price will increase. The reverse relationship is also supported empirically meaning that an increase in the oil price will results in a significant depreciation of the dollar rate. Based on the asymmetric impulses responses, it can also be claimed that the negative interaction is only significant for the positive changes and not for the negative ones. Thus, the underlying variables are negatively interrelated only for the positive shocks since a negative shock from any variable does not seem to have any significant impact on the other variable. These results have implications for cross hedging of price risk.

To the best knowledge, this is the first attempt to investigate the relationship between the dollar weighted exchange rate and the oil pieces via the asymmetric impulse response functions. Both of these variables and their interactions are very important for investors as well as policy makers worldwide.

In this study, a tool has been designed and developed for learning about the concept of lag order within a dynamic model, which can be used in any teaching classes on statistics and financial data computation. To show a solution for a complex and multi-step process of finding the optimal lag order for multiple variables data series based on an information criterion a module using Visual Basic for Applications (VBA) for Microsoft Excel (MS Excel) is being developed. This module can be used for estimating a multivariate dynamic model as well as determining the optimal lag order of such a model.

Option pricing is an integral part of modern financial risk management. The well-known Black and Scholes (1973) formula is commonly used for this purpose. The purpose of this paper is to extend their work to a situation in which the unconditional volatility of the original asset is increasing during a certain period of time.

The authors consider a market suffering from a financial crisis. The authors provide the solution for the equation of the underlying asset price as well as finding the hedging strategy. In addition, a closed formula of the pricing problem is proved for a particular case. Furthermore, the underlying price sensitivities are derived.

The suggested formulas are expected to make the valuation of options and the underlying hedging strategies during a financial crisis more precise. A numerical application is provided for determining the premium for a call and a put European option along with the underlying price sensitivities for each option.

An alternative option pricing model is introduced that performs better than existing ones, especially during a financial crisis.

The issue of model selection in applied research is of vital importance. Since the true model in such research is not known, which model should be used from among various potential ones is an empirical question. There might exist several competitive models. A typical approach to dealing with this is classic hypothesis testing using an arbitrarily chosen significance level based on the underlying assumption that a true null hypothesis exists. In this paper, the authors investigate how successful the traditional hypothesis testing approach is in determining the correct model for different data generating processes using time series data. An alternative approach based on more formal model selection techniques using an information criterion or cross-validation is also investigated.

Monte Carlo simulation experiments on various generating processes are used to look at the response surfaces resulting from hypothesis testing and response surfaces resulting from model selection based on minimizing an information criterion or the leave-one-out cross-validation prediction error.

The authors find that the minimization of an information criterion can work well for model selection in a time series environment, often performing better than hypothesis-testing strategies. In such an environment, the use of an information criterion can help reduce the number of models for consideration, but the authors recommend the use of other methods also, including hypothesis testing, to determine the appropriateness of a model.

This paper provides an alternative approach for selecting the best potential model among many for time series data. It demonstrates how minimizing an information criterion can be useful for model selection in a time-series environment in comparison to some standard hypothesis testing strategies.

The purpose of this paper is to investigate whether the Australian equity market is informationally efficient in the semi‐strong form with regard to interest rates and the exchange rate shocks during the period 1994‐2006.

There is evidence that the data are non‐normal and that autoregressive conditional heteroskedasticity (ARCH) effects exist and in such circumstances, standard estimation methods are not reliable. A new method introduced by Hacker and Hatemi‐J which is robust to non‐normality and the presence of ARCH is applied.

The results show the Australian equity market is not informationally efficient with regard to either the interest rate or the exchange rate.

The empirical findings, in contrast to several previous studies, imply that the possibility for arbitrage profits in the equity market might exist.

In all existing theoretical papers on causality it is assumed that the lag length is known a priori. However, in applied research the lag length has to be selected before testing for causality. The purpose of this paper is to suggest that in investigating the effectiveness of various Granger causality testing methodologies, including those using bootstrapping, the lag length choice should be endogenized, by which we mean the data‐driven preselection of lag length should be taken into account.

The size and power of a bootstrap test with endogenized lag‐length choice are investigated by simulation methods. A statistical software component is produced to implement the test, which is available online.

The simulation results show that this test performs well. An application of the test provides empirical support for the hypothesis that the UAE financial market is integrated with the US market.

The empirical results based on this test are expected to be more precise.

This paper considers a bootstrap test for causality with endogenous lag order. This test has superior properties compared to existing causality tests in terms of size, with similar if not better power and it is robust to ARCH effects that usually characterize financial data. Practitioners interested in causal inference based on time series data might find the test valuable.