Journal of Economics and Financial Analysis

This paper examines factors that influence prices of most common five cryptocurrencies such as Bitcoin, Ethereum, Dash, Litecoin, and Monero over 2010-2018 using weekly data. The study employs ARDL technique and documents several findings. First, cryptomarket-related factors such as market beta, trading volume, and volatility appear to be significant determinant for all five cryptocurrencies both in short- and long-run. Second, attractiveness of cryptocurrencies also matters in terms of their price determination, but only in long-run. This indicates that formation (recognition) of the attractiveness of cryptocurrencies are subjected to time factor. In other words, it travels slowly within the market. Third, SP500 index seems to have weak positive long-run impact on Bitcoin, Ethereum, and Litcoin, while its sign turns to negative losing significance in short-run, except Bitcoin that generates an estimate of -0.20 at 10% significance level.
Lastly, error-correction models for Bitcoin, Etherem, Dash, Litcoin, and Monero show that cointegrated series cannot drift too far apart, and converge to a long-run equilibrium at a speed of 23.68%, 12.76%, 10.20%, 22.91%, and 14.27% respectively.

When firms announce a restatement of their financial reports, they inform investors that their prior announcements were faulty. Not only do companies lose credibility at times such as this but also their securities are revalued as investors respond to the substance of the announcement. We investigate investor size to understand how large and small investors differ in their responses to restatement announcements. Our results indicate that large investors seemingly anticipate the announcement; their holdings decrease before restatement announcements; consequently large investors trading after announcements is less pronounced than for smaller investors. The response of small investors depends on who has prompted the restatement: the company itself, FASB or the SEC and not on the reason for the restatement such as problems with revenue recognition, restructuring or cost/expense. Large investor trading volume is affected by both the source of the restatement and the reason for it. Large investors seem to anticipate potential problems, and sell securities before restatement announcements.

Using a panel data set of 4995 banks across 11 developed and emerging countries during the period (2011-2015), this report analyses the amount of liquidity created by banks, how liquidity creation, bank-specific and the macroeconomic factors affecting bank profitability. The results show evidence of increased creation of liquidity over the period. By applying the panel data fixed effect technique, banks that create more liquidity, are set up to have lower profitability. As well as, Asset management, bank size and capital ratio are positively correlated with bank profitability. While, credit quality and operating efficiency affect bank’s profits negatively. Additionally, macroeconomic factors have different impact on profitability indicators in each market. Our findings may help decision makers inside and outside bank to determine important factors affecting bank profitability.

In this paper we outline the European interest rate swaption pricing formula from first principles using the Martingale Representation Theorem and the annuity measure. This leads to an expression that allows us to apply the generalized Black-Scholes result. We show that a swaption pricing formula is nothing more than the Black-76 formula scaled by the underlying swap annuity factor. Firstly, we review the Martingale Representation Theorem for pricing options, which allows us to price options under a numeraire of our choice. We also highlight and consider European call and put option pricing payoffs. Next, we discuss how to evaluate and price an interest swap, which is the swaption underlying instrument. We proceed to examine how to price interest rate swaptions using the martingale representation theorem with the annuity measure to simplify the calculation. Finally, applying the Radon-Nikodym derivative to change measure from the annuity measure to the savings account measure we arrive at the swaption pricing formula expressed in terms of the Black-76 formula. We also provide a full derivation of the generalized Black-Scholes formula for completeness.

This paper analyses cyclical behaviour of Orange stock price listed in French stock exchange over 01/03/2000 to 02/02/2017 by testing the nonlinearities through a class of conditional heteroscedastic nonparametric models. The linearity and Gaussianity assumptions are rejected for Orange Stock returns and informational shocks have transitory effects on returns and volatility. The forecasting results show that Orange stock prices are short-term predictable and nonparametric NAR-ARCH model has better performance over parametric MA-APARCH model for short horizons. Plus, the estimates of this model are also better comparing to the predictions of the random walk model. This finding provides evidence for weak form of inefficiency in Paris stock market with limited rationality, thus it emerges arbitrage opportunities.