The corticotropin-releasing factor (CRF) family of peptides as local modulators of adrenal function

Corticotropin-releasing factor (CRF), also termed corticotropin-releasing hormone (CRH) or corticoliberin, is the major regulator of the adaptive response to internal or external stresses. An essential component of the adaptation mechanism is the adrenal gland. CRF regulates adrenal function indirectly through the central nervous system (CNS) via the hypothalamic-pituitary-adrenal (HPA) axis and via the autonomic nervous system by way of locus coeruleus (LC) in the brain stem. Accumulating evidence suggests that CRF and its related peptides also affect the adrenals directly, i.e. not through the CNS but from within the adrenal gland where they form paracrine regulatory loops. Indeed, CRF and its related peptides, the urocortins (UCNs: UCN1, UCN2 and UCN3), their receptors CRF type 1 (CRF₁) and 2 (CRF₂) as well as the endogenous pseudo-receptor CRF-binding protein (CRF-BP) are all expressed in adrenal cortical, medullary chromaffin and resident immune cells. The intra-adrenal CRF-based regulatory system is complex and depends on the balance between the local concentration of CRF ligands and the availability of their receptors. Received 19 December 2006; received after revision 20 February 2007; accepted 26 March 2007     

The corticotropin releasing factor system in cancer: expression and pathophysiological implications

Malignant tumors express multiple factors that have some role in the regulating networks supporting their ectopic growth. Recently, increased interest has been developing in the expression and biological role of the neuropeptides and receptors of the corticotropin releasing factor (CRF) system, the principal neuroendocrine mediator of the stress response, especially in the light of several R&D programs for small molecule antagonists that could present some anticancer therapeutic benefit. In the present article, we review the literature suggesting that the CRF system could be involved in the regulation of human cancer development. Potential implication in growth, metastasis, angiogenesis, or immune parameters via activation of locally expressed receptors could be clinically exploited by presenting targets of new therapeutic approaches.     

A Quantile Regression Approach to Equity Premium Prediction

We propose a quantile regression approach to equity premium forecasting. Robust point forecasts are generated from a set of quantile forecasts using both fixed and time‐varying weighting schemes, thereby exploiting the entire distributional information associated with each predictor. Further gains are achieved by incorporating the forecast combination methodology into our quantile regression setting. Our approach using a time‐varying weighting scheme delivers statistically and economically significant out‐of‐sample forecasts relative to both the historical average benchmark and the combined predictive mean regression modeling approach. Copyright © 2014 John Wiley & Sons, Ltd.     

Measuring the market risk of freight rates: A forecast combination approach

This paper addresses the issue of freight rate risk measurement via value at risk (VaR) and forecast combination methodologies while focusing on detailed performance evaluation. We contribute to the literature in three ways: First, we reevaluate the performance of popular VaR estimation methods on freight rates amid the adverse economic consequences of the recent financial and sovereign debt crisis. Second, we provide a detailed and extensive backtesting and evaluation methodology. Last, we propose a forecast combination approach for estimating VaR. Our findings suggest that our combination methods produce more accurate estimates for all the sectors under scrutiny, while in some cases they may be viewed as conservative since they tend to overestimate nominal VaR.     

Forecasting growth and inflation in an enlarged euro area

We compare models for forecasting growth and inflation in the enlarged euro area. Forecasts are built from univariate autoregressive and single‐equation models. The analysis is undertaken for both individual countries and EU aggregate variables. Aggregate forecasts are constructed by both employing aggregate variables and by aggregating country‐specific forecasts. Using financial variables for country‐specific forecasts tends to add little to the predictive ability of a simple AR model. However, they do help to predict EU aggregates. Furthermore, forecasts from pooling individual country models usually outperform those of the aggregate itself, particularly for the EU25 grouping. This is particularly interesting from the perspective of the European Central Bank, who require forecasts of economic activity and inflation to formulate appropriate economic policy across the enlarged group. Copyright © 2008 John Wiley & Sons, Ltd.