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In this paper, we propose a robust test of monotonicity in asset returns that is valid under a general setting. We develop a test that allows for dependent data and is robust to conditional heteroskedasticity or heavy-tailed distributions of return differentials. Many postulated theories in economics and finance assume monotonic relationships between expected asset returns and certain underlying characteristics of an asset. Existing tests in literature fail to control the probability of a type 1 error or have low power under heavy-tailed distributions of return differentials. Monte Carlo simulations illustrate that our test statistic has a correct empirical size under all data-generating processes together with a similar power to other tests. Conversely, alternative tests are nonconservative under conditional heteroskedasticity or heavy-tailed distributions of return differentials. We also present an empirical application on the monotonicity of returns on various portfolios sorts that highlights the usefulness of our approach.

The edge-effect concerning the standard estimators’ bias for the parameters of multi-indexed ARMA-type series is a common hurdle; it is investigated whether an alternative ARMA parameterization might release any unwelcome complication. The theoretical blocks, of when the factorized model is free of the edge-effect, are provided and simulation results are used to reinforce the same views. Estimation or other perspectives are discussed in a conclusion.

Johansen’s (2000. “A Bartlett Correction Factor for Tests of on the Cointegrating Relations.” Econometric Theory 16: 740–78) Bartlett correction factor for the LR test of linear restrictions on cointegrated vectors is derived under the i.i.d. Gaussian assumption for the innovation terms. However, the distribution of most data relating to financial variables is fat-tailed and often skewed; there is therefore a need to examine small sample inference procedures that require weaker assumptions for the innovation term. This paper suggests that using the non-parametric bootstrap to approximate a Bartlett-type correction provides a statistic that does not require specification of the innovation distribution and can be used by applied econometricians to perform a small sample inference procedure that is less computationally demanding than it’s analytical counterpart. The procedure involves calculating a number of bootstrap values of the LR test statistic and estimating the expected value of the test statistic by the average value of the bootstrapped LR statistic. Simulation results suggest that the inference procedure has good finite sample property and is less dependent on the parameter space of the data generating process.

The present study examines the Brazilian economy in the light of the export-led growth (ELG) hypothesis, in order to examine if this hypothesis is valid for periods in which commodities occupy a significant part of exports, for this reason, for the period known as the “commodity boom”. In order to address the proposed objective, the estimation method used was the autoregression with vector error correction (VEC) in its structural version. The results suggest that the economic growth that occurred in Brazil during the analysed period does not corroborate the ELG hypothesis, which is endorsed by results obtained in similar studies.