Local Temperature Deviance And National Prices: The U.S. Natural Gas Market

In this study we investigate the price discovery process in the U.S. natural gas spot and futures markets. We explore the relationships between the spot and futures markets, the effect of U.S. temperature changes on these markets and, in addition, test whether New York City temperature changes have a special impact on the national market for natural gas.  We find that most price discovery occurs in the futures market. We also find that colder days in winter and hotter days in summer result in higher gas prices, although daily changes in temperature have a stronger effect on prices during the winter. Furthermore, we find that the daily temperature changes in New York City, where the futures market for natural gas is physically located, have an additional effect on gas prices beyond what could be explained by the temperature changes aggregated across the U.S

An Empirical Examination of the “Rule of Three”: Strategy Implications for Top Management, Marketers, and Investors

This study represents the first empirical examination of the “Rule of Three,” a theory at odds with several popular notions regarding industry structure and business performance, including the positive linear market share–performance relationship. In general, the findings from more than 160 industries support the Rule of Three and provide five main insights: First, there appears to be a prevalent competitive structure for mature industries in which three “generalist” firms control the market. Second, industries that conform to this structure tend to perform better than industries with a fewer or greater number of generalists. Third, both “specialists” and generalists outperform firms that are “stuck in the middle.” Fourth, the performance benefits of market leadership appear to diminish with excessive market share. Fifth, the Rule of Three industry structure and its influence over firm profitability do not appear to be priced appropriately by financial markets. The authors discuss the implications for multiple stakeholders

Long Memory in the Volatility of Selected Cryptocurrencies: Bitcoin, Ethereum and Ripple

This paper examines the volatility of cryptocurrencies, with particular attention to their potential long memory properties. Using daily data for the three major cryptocurrencies, namely Ripple, Ethereum, and Bitcoin, we test for the long memory property using, Rescaled Range Statistics (R/S), Gaussian Semi Parametric (GSP) and the Geweke and Porter-Hudak (GPH) Model Method. Our findings show that squared returns of three cryptocurrencies have a significant long memory, supporting the use of fractional Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) extensions as suitable modelling technique. Our findings indicate that the Hyperbolic GARCH (HYGARCH) model appears to be the best fitted model for Bitcoin. On the other hand, the Fractional Integrated GARCH (FIGARCH) model with skewed student distribution produces better estimations for Ethereum. Finally, FIGARCH model with student distribution appears to give a good fit for Ripple return. Based on Kupieck’s tests for Value at Risk (VaR) back-testing and expected shortfalls we can conclude that our models perform correctly in most of the cases for both the negative and positive returns

Long memory in the volatility of selected cryptocurrencies: Bitcoin, Ethereum and Ripple

This paper examines the volatility of cryptocurrencies, with particular attention to their potential long memory properties. Using daily data for the three major cryptocurrencies, namely Ripple, Ethereum, and Bitcoin, we test for the long memory property using, Rescaled Range Statistics (R/S), Gaussian Semi Parametric (GSP) and the Geweke and Porter-Hudak (GPH) Model Method. Our findings show that squared returns of three cryptocurrencies have a significant long memory, supporting the use of fractional Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) extensions as suitable modelling technique. Our findings indicate that the Hyperbolic GARCH (HYGARCH) model appears to be the best fitted model for Bitcoin. On the other hand, the Fractional Integrated GARCH (FIGARCH) model with skewed student distribution produces better estimations for Ethereum. Finally, FIGARCH model with student distribution appears to give a good fit for Ripple return. Based on Kupieck's tests for Value at Risk (VaR) back-testing and expected shortfalls we can conclude that our models perform correctly in most of the cases for both the negative and positive returns