Regime changes and the sustainability of fiscal imbalance in East Asian countries

The main purpose of this paper is to assess empirically the fiscal policy regimes in five Asian countries using a formal framework based on the government's intertemporal budget constraints (GIBC). To this end, we relied on an array of time-series methods and quarterly data of nearly three decades. Our conclusions are; first, the evidence indicates that the fiscal stance in Thailand and South Korea are on their sustainable path while the Philippines and Malaysia demonstrate only ‘weak sustainability’. Second, revenues are growing at a rate faster than government spending for Singapore, a country that has recorded large surpluses for most of the sample period. Third, the results show a one-way causation from expenditure to revenue for Korea, Singapore and Thailand. This finding indicates that reducing the size of government spending may improve fiscal budget deficits without having to undergo changes in the overall strategy. Fourth, we observed a long-run feedback causality in the revenue–expenditure nexus for the case of Malaysia and the Philippines, which may require fiscal synchronization instrument policies to moderate the post-crisis fiscal imbalances. Together, the results demonstrate diverse fiscal patterns but they should be useful to understand the complexities of economic integration in the region.     

Evaluating interpretable machine learning predictions for cryptocurrencies

This study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features N-BEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources.     

Measuring volatility persistence in the presence of sudden changes in the variance of Canadian stock returns

Abstract.  It is well known that volatility persistence is overestimated if regime shifts are not accounted for in the standard GARCH model. This research detects time periods of sudden changes in variance using the iterated cumulated sums of squares (ICSS) algorithm. Using weekly data for the Canadian stock market indicates that after accounting for endogenously determined volatility shifts in the GARCH model, the estimated persistence in volatility is significantly reduced. This casts some doubt on previous findings that volatility in financial markets is highly persistent. The findings have important implications for investors and financial market participants. JEL classification: G1     

Radar remote sensing for monitoring of dynamic ecosystem processes related to biogeochemical exchanges in tropical peatlands

Peatlands are an important multipurpose ecosystem, supporting huge quantities of biomass and peat soil carbon. A time series of Japanese Earth Resource Satellite-1 (JERS-1) L-band Synthetic Aperture Radar (SAR) data was employed to monitor two dynamic ecosystem processes; deforestation and inundation patterns. Using a change detection analysis for three images acquired during dry seasons of 1994, 1997 and 1998, we detected the deforestation that has occurred in the region due to the anthropogenic and natural causes. At a threshold of ±2 dB change in backscattering response, an area of about 98 km² of these forests was found to have been cleared during 1994–1997 for conversion to cultivatable lands. However, the agricultural crops miserably failed to grow on these cleared lands because of the adverse water and soil chemistry conditions. The deliberate draining of these lands, by laying and extension of a huge network of canals, created congenial ecological conditions for the spread of forest fires, particularly during the 1997 El Niño period. An area of 250 km² of forests was thus detected to have been destroyed by these fires between September 1997 and January 1998. These deforested lands are rapidly regenerating since their abandonment and the regenerating carbon stocks were simulated using the CENTURY ecosystem dynamics model. Furthermore, the L-band SAR was able to detect the pixel-wise seasonal and spatial inundation information for particular forest types where the transmissivity of the L-band SAR signals was quite significant. These forest types corresponded to comparatively low biomass areas. The SAR derived information about these two important dynamic processes would be useful for improving the accuracy of modelling the spatial and temporal distribution of the carbon and other trace gases in these ecosystems.     

Recurrence relations for moment and conditional moment generating functions of generalized order statistics

In this paper, we obtain recurrence relations for moment and conditional moment generating functions of generalized order statistics (gos) based on random samples drawn from a population whose distribution is a member of a doubly truncated class of distributions denoted by . Members of the class are characterized in Section (2) based on recurrence relations for moment generating functions (moments) of gos. In Section (3), we shall characterize members of the class based on recurrence relations for conditional moment generating functions (conditional moments) of gos. These results are specialized to the left, right and non-truncated cases. Ordinary order statistics and ordinary record values are also obtained as special cases of the gos. Characterizations of some members of class such as the Weibull, compound Weibull, Pareto, power function (beta is a special case), Gompertz and compound Gompertz distributions are given as illustrative examples.     

Time-Varying Spillover and the Portfolio Diversification Implications of Clean Energy Equity with Commodities and Financial Assets

This article examines the time-varying spillover and its implications on hedging and portfolio diversification for clean energy equities (WilderHill New Energy Global Innovation Index (NEX)) with technology stocks (PSE), four energy sub-indices of Standard & Poor Goldman Sachs Commodity Index (S&P-GSCI) viz., Crude oil, Brent crude oil, Gasoline and Heating oil and three major global equities indices represented by the USA, Europe, World, Dow-Jones Islamic Market Index (DJIMI) along with USD-Euro exchange rate. We find that in a mixed portfolio set-up, the inclusion of NEX in energy portfolio provides better diversification and risk reduction benefits for hedgers and portfolio managers.     

Why Did FDI Inflows of Pakistan Decline? From the Perspective of Terrorism,Energy Shortage,Financial Instability,and Political Instability

Foreign direct investment (FDI) inflows are important for economic development in all countries, especially developing ones. In many developing countries, FDI inflows have increased over the past two decades. However, in Pakistan FDI inflows declined over the past decade. This study examines the reasons for declining FDI inflows to Pakistan, considering the main issues, such as terrorism, energy shortages, financial instability, and political instability, with some macroeconomic indicators as control variables. These analyses are based on pre- and post-global financial crisis events, and we check the robustness by controlling for the global financial crisis. Our analyses are conducted using an autoregressive distributed lag model (ARDL) for co-integration among variables. The results show that energy shortages, financial instability, and political instability have adverse effects, and terrorism has insignificant effects on FDI inflows to Pakistan before the financial crisis in the long term. However, the post-financial crisis period indicates that terrorism and energy shortages are the main drivers of decline in FDI inflows to Pakistan. Market size, inflation, and exchange rates affect FDI inflows positively. The global financial crisis has an adverse impact on FDI inflows to Pakistan. This study is helpful for the Pakistani government as it attempts to design useful policies for attracting FDI.     

Financial market development,global financial crisis and economic growth: evidence from developing nations

Emerging and frontier markets in Africa have witnessed various economic and financial reforms aimed at integrating the domestic markets into the global financial market to attract investment. Whether these reforms promote high economic growth remains inconclusive. The paper applies the pooled mean group estimation technique to empirically re-investigate the link between financial market development, global financial crisis, and economic growth in selected African economies. The results strongly support our hypotheses that stock market and banking sector development promotes economic growth in the selected countries. Moreover, financial crisis reduce the positive effects of both the stock market and banking sector developments on economic growth. The study suggests that both the banking sector and stock market are important to deliver the long-run economic growth that the African region desired. Moreover, effort should be made to enact policy measures that would ensure development of the stock market which has received inadequate attention.     

Consumer's Perception of Website Service Quality: An Empirical Study

With the increasing penetration of the Internet, service quality has become one of the key areas of concern for online shopping sites. Website service quality has become a vital factor in making e-commerce successful because comparing the features of products in the online environment is easier, practically free of cost, and saves on time in comparison to conventional offline markets. To empirically explore the critical factors that determine perceptions of the service quality of online shopping sites, researchers adopted the eTailQ scale suggested by Wolfinbarger and Gilly (2003 Wolfinbarger, M., and M. C. Gilly. 2003. eTailQ: dimensionalizing, measuring and predicting etail quality. Journal of retailing 79 (3):183–98. doi:10.1016/s0022-4359(03)00034-4.[Crossref], [Web of Science ®] , [Google Scholar]). Web layout, web info, customer service, fulfillment, and privacy emerged as the critical factors affecting website service quality. Findings of the study are expected to provide valuable insights to academicians in better conceptualizing the constructs and also help marketing practitioners in fine-tuning their strategies by addressing the peculiar needs of the Indian online shoppers.     

Gender diversity in academic publishing—comment on Galak and Kahn (2021)

Marketing Letters - Galak and Kahn (Marketing Letters, 2021) report that females and underrepresented minorities face a less favorable organizational climate within academic marketing as compared...