This study investigates the determinants of business failure rates for low-technology and high-technology manufacturing industries across the forty-eight contiguous US states over the period 1984 to 1993. The main focus is on examining the role that some key state fiscal measures and federal transfer grants to states play in explaining business failure rates. It is found that regional variations in sales taxes, highway expenditures, bank loans, university R&D, patents, and outstanding debt play a statistically significant role in explaining variations in regional business failure rates. Interestingly, it is found that corporate development assistance programmes and small business loans tend to improve small business survival rates for hightechnology industries but they do not for low-technology industries. Findings also suggest that policymakers can effectively use local/regional policy instruments to bring the current business failure rates to the desired level more easily within hightechnology industry groups than within low-technology industry groups.
Using the Exponentially Generalized AutoRegressive Conditional Heteroscedasticity-AutoRegressive Moving Average (EGARCH-ARMA) model, there are no differences in terms of the spillover of returns from volatilities and leverage effects between ethical and non-ethical Exchange Traded Funds (ETFs) against benchmark indexes after applying negative ethical screening on ETFs. The lagged ethical ETF returns unilaterally influence current stock index returns or the bilateral relationships between them. This article sheds new light on the selection process involved in ethical ETFs and may provide clues for fund managers as they reward investors who prefer ethical value investments.
This research utilizes the Autoregressive Moving Average–General Autoregressive Conditional Heteroskedasticity (ARMA–GARCH) and Autoregressive Moving Average–Exponential General Autoregressive Conditional Heteroskedasticity (ARMA–EGARCH) in studying the spillover and leverage effects of returns and volatilities of seven equity exchange-traded notes (ETNs) and their tracked stock indices. This study finds positive returns transmissions between the two investment instruments. Unilateral influence and bilateral relationships also exist that may help investors in finding investment clues to approximate possible movements of ETNs about stock indices and vice versa. This paper also observes negative returns and volatility transmissions that may caution traders in the possible reversal of movement of the other instrument. Disinvestments, transfer of allocation, and inverse investing strategies are some of the possible reasons attributable to this negative relation.
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