This paper studies the relationship between Tunisian listed banks performance and two types of determinants; internal and external. The internal explanatory variables are: (1) the bank size, (2) privatization, (3) board size, (4) capital-to-assets ratio, and (5) cost of efficiency. The macro-economic (external) exogenous variables are: (1) gross domestic product growth rate and (2) inflation. Our panel-data analyses suggest a statistically significant and negative relationship between bank profitability (endogenous variable) and board size. However, the remaining variables were found to be statistically insignificant. This can be explained by two main sub-hypotheses: (a) state-owned banks included in the sample disturb the statistical significance of the results and (b) the year 2011 is a cut-off point that changed the Tunisian bank performance determinants.
Our paper aims to model the yield curve that corresponds to a graphical representation of the yields offered by the bonds of the same issuer according to their maturity, from the shortest to the longest expiration date in the Tunisian bond market (TBM). To get to our objective, we will compare the Nelson-Siegel modeling strategy, which is most often used for the analysis and the hedging of the interest rate risk of portfolios with known flows in practice, to the Svensson modeling strategy, which is the extension of the Nelson-Siegel model. Our sampling data statistically support the evidence that the more appropriate yield curve for the TBM is that estimated by the NelsonSiegel model.
In this paper we reconsider the Fama (1984)'s seminal paper and we make extensions. We take into account for ARMA dynamics and ARCH-M effects in exchange rates and we introduce in equation regressions a proxy for the liquidity. We find out that the differenced relative bid-ask spread is a significant determinant of forward risk premia. In addition we evidence the outperformance of the multimarket hypothesis vs the single market hypothesis and the existence of common factors between forward risk premia in the EUR/USD, EUR/GBP and EUR/JPY forward exchange rates.
Is an autoregressive moving average model for the unobserved forward risk premium component always identifiable? Is the signal extraction-based approach always feasible? In this paper, we point out a theoretical framework to shed the light on the statistical problem of model identification. We find out that whenever a model for the unobservable forward risk premium is unidentifiable, we identify a new class of functions that we call: the noise generating functions (Hereafter NGF). These functions circumvent the model identification problem and help us make insights on the noise variances. We demonstrate that a model for the risk premium in the forward exchange rate is not always identifiable and the signal extraction methodology is not always feasible. In addition, our theoretical statements are applied to the empirically evidenced models within the related literature.
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