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The purpose of the paper is to back-test value-at-risk (VaR) models for conditional distributions belonging to a Generalized Hyperbolic (GH) family of Lévy processes – Variance Gamma, Normal Inverse Gaussian, Hyperbolic distribution and GH – and compare their risk-management features with a traditional unconditional extreme value (EV) approach using data from future contracts return data of S&P500, FTSE100, DAX, HangSeng and Nikkei 225 indices.

The authors apply tail-based and Lévy-based calibration to estimate the parameters of the models as part of the initial data analysis. While the authors utilize the peaks-over-threshold approach for generalized Pareto distribution, the conditional maximum likelihood method is followed in case of Lévy models. As the Lévy models do not have closed form expressions for VaR, the authors follow a bootstrap method to determine the VaR and the confidence intervals. Finally, for back-testing, they use both static calibration (on the entire data) and dynamic calibration (on a four-year rolling window) to test the unconditional, independence and conditional coverage hypotheses implemented with 95 and 99 per cent VaRs.

Both EV and Lévy models provide the authors with a conservative proportion of violation for VaR forecasts. A model targeting tail or fitting the entire distribution has little effect on either VaR calculation or a VaR model’s back-testing performance.

To the best of the authors’ knowledge, this is the first study to explore the back-testing performance of Lévy-based VaR models. The authors conduct various calibration and bootstrap techniques to test the unconditional, independence and conditional coverage hypotheses for the VaRs.

This study aims to empirically examine the relationship between energy consumption and agricultural economic growth in Pakistan over the period from 1984 to 2016.

This study used the autoregressive distributed lag (ARDL) bounds testing approach to cointegration to investigate the long-run and short-run determinants of agricultural economic growth in Pakistan.

The results of the ARDL bounds testing approach to cointegration revealed that long-run linkage exists among the study variables. The findings of this paper showed that agricultural economic growth is positively affected by gas consumption and electricity consumption both in the long-run and short run. The long-run and short-run coefficients of gas consumption and electricity consumption were estimated to be 0.906, 0.421, 0.595 and 0.276, respectively. The estimated equation remains stable during the period from 1984 to 2016 as analyzed by the stability tests.

This study considers the relationship between energy consumption and agricultural economic growth in Pakistan by using an ARDL bounds testing approach to cointegration. The study has three contributions to economic literature:this study used different unit root tests to test stationarity of the variables such as ADF unit root test by Dicky and Fuller and P-P unit root test by Philip and Perron; the ARDL bounds testing approach to cointegration is applied to test the existence of long-run analysis between energy consumption and agricultural economic growth; and to check the robustness, the authors used the Johansen cointegration test to examine the long-run relationship between dependent and independent variables.

The purpose of this study is to see the energy relation to economic growth and find a way to solve the energy crisis for Bangladesh. Bangladesh is facing a high rate depletion of traditional energy sources. Renewable energy technology may be an alternative solution to meeting Bangladesh’s rising energy demand. Despite huge potential, Bangladesh fails to use renewable energy sources properly due to insufficient information and technical knowledge. The present research studied the current energy condition and potentiality of renewable energy with its influence on economic growth in Bangladesh.

This study analyzes the relationship between renewable energy consumption and economic growth of Bangladesh for the period of 2001–2016, based on yearly data, by using multiple regression model where augmented Dickey–Fuller unit root test has been chosen for testing the viability.

The result of this study showed that economic growth of Bangladesh is influenced positively by the consumption of renewable energy.

In addition, SWOT analysis has also done to develop a roadmap, and suggest some policies which will be able to accomplish the country’s climbing energy demands for a short- and long-term solution.

This study is an original work for Bangladesh, showing the results of the relationship between renewable energy consumption and economic growth. Therefore, this research will be useful to contribute to the literature review in the near future.

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.

Urban built-up has been increasing exponentially in the world. Urban population growth and migration are depleting the land resources and creating thermal discomfort. Cities all around the world are facing urban heat island effects and increased temperatures. This study aims to map land cover and formulate local climate zones for enhancing urban resilience against disaster and climate risks.

This study uses exploratory research to identify local climate zones for Lahore, Pakistan. Landsat 8 imagery was used to develop a land use land cover map. For mapping local climate zones, the standard World Urban and Access Portal Tool procedure was used.

Results have revealed that Lahore has grown exponentially. Compact low rise and open low rise were the two most common local climate zones prevalent in the city. In contrast, the outer regions of the city consisted of LCZ D (low plants) and LCZ F (bare soil).

This study highlights the need to consider local climate zones in future development plans and policies for ensuring sustainable, resilient and climate-friendly cities.

Local climate zone studies are missing in Pakistan. This study has empirically analyzed the ground situation of local climate zones for Lahore metropolitan city. This study will provide baseline support for future studies on urban heat island and climate change adaptation planning.