Economic Policy Uncertainty and Emerging Stock Market Volatility

This research examines the effect of economic policy uncertainty (EPU) indices on Pakistan's stock market volatility. Particularly, we examine the impact of the economic policy uncertainty index for Pakistan and bilateral global trading partner countries, the US, China, and the UK. We employ the GARCH-MIDAS model and combination forecast approach to evaluate the performance of economic uncertainty indices. The empirical findings show that the US economic policy uncertainty index is a more powerful predictor of Pakistan stock market volatility. In addition, the EPU index for the UK also provides valuable information for equity market volatility prediction. Surprisingly, Pakistan and China EPU indices have no significant predictive information for volatility forecasting during the sample period. Lastly, we find evidence of all uncertainty indices during economic upheaval from the COVID-19 pandemic. We obtained identical results even during the Covid-19. Our findings are robust in various evaluation methods, like MCS tests and other forecasting windows.

Uncertainty indices and stock market volatility predictability during the global pandemic: evidence from G7 countries

This article attempts to examine the predictability of a significant number of uncertainty indices for the G7 stock market volatility based on a Heterogeneous AutoRegressive Realized Volatility (HARRV) model and a combination forecast framework during the global pandemic COVID-19. We include in our analysis the Infectious Disease Equity Market Volatility (IDEMV), the VIX, the Economic Policy Uncertainty (EPU), the Equity Market Volatility (EMV), the Geopolitical risk (GPR) as well as the crude oil futures' realized volatility. Out-of-sample evidence shows that models incorporating all uncertainty indices improve forecasting performance for most stock markets' volatility during a long out-of-sample period and also during the coronavirus period. The results are robust using an alternative volatility model, an alternative realized measure and a recursive window analysis. The predictability of the uncertainty indices is also evaluated through risk management and portfolio loss functions and results suggest that the LASSO combination and a HARRV model including all indices are the most profitable for all stock markets during the global pandemic. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Combined Hierarchical Tourist Arrival Forecasts for Great Zimbabwe National Monuments

Precise tourism estimates for tourism destination sites are crucial for decision-making. The objective of the study is to model and project Great Zimbabwe National Monuments (GZNM) tourist arrivals by combining hierarchical tourism forecasts. The approach improves tourism forecasting accuracy. GZNM monthly tourist arrivals are grouped according to tourism sources. A logarithm transformation is applied to tame the volatile data. Forecasting accuracy of the Simple Average Combination Method (SACM) and three hierarchical forecasting approaches (top-down, bottom-up, and optimal combination) were compared. The SACM under Autoregressive Integrated Moving Average (ARIMA) outperformed the other models, according to Root Mean Square Error (RMSE) measure. SACM is used to combine future tourist arrivals for the following 60 months and show a slow increase in tourist arrivals at GZNM. The data used in modeling are outside the COVID-19 pandemic period. Tourism stakeholders are encouraged to adopt the SACM in future tourism projections as it improves forecasting accuracy. Tourism stakeholders could carefully strategise and plan a recovery and ensure improvement in the tourism sector beyond the COVID-19 pandemic period. The COVID-19 pandemic is significantly affecting the tourism industry, reducing tourist arrivals to zero in some cases. The study revealed a fresh line of inquiry into how combining projections can increase forecasting accuracy © 2022 AJHTL /Author(s)

Forecasting United Kingdom's energy consumption using machine learning and hybrid approaches

Investigating the current and future dynamics of energy consumption in modern economies such as the UK is crucial. This paper predicts the UK's energy consumption using data spanning January 1995 to March 2022 by comparing and evaluating the forecast performance of machine learning, dynamic regression, time series and combination modelling techniques. The analysis reveals that the seasonal ARIMA and TBATS hybrid models yield the lowest forecast errors in predicting the UK's electricity and gas consumption. Although the combination forecasts performed poorly relative to other models, machine learning techniques such as neural network and support vector regression produced better results compared to the dynamic regression models, whereas the seasonal hybrid model performed better than the machine learning and time series models. The results indicate that the UK's electricity consumption would either stabilise or decline over the forecast horizon, suggesting that it will take some years for electricity consumption to attain pre-2019 levels. For gas consumption, the results indicate that consumption would either maintain current levels or increase over the forecast period. We also show that combination forecasts do not often generate the best predictions, and therefore, choice of methodology matters in energy consumption forecasting. Overall, changing seasonal patterns, energy efficiency improvements, shift to renewable sources and uncertainties due to the COVID-19 pandemic, Brexit, and the Russia–Ukraine crisis appear to be significant drivers of energy consumption in the UK in recent times. These findings are expected to help in designing more effective energy policies as well as guide investor decisions in the energy sector. © The Author(s) 2022.