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Aims/hypothesis Inflammation is a common feature in cardiovascular diseases, including diabetes mellitus. In addition to the well-known inflammatory role of cyclooxygenase- 2 (COX-2), this protein has also been implicated in apoptosis resistance in tumour cells. Vascular smooth muscle cells (VSMC) from diabetic patients are also resistant to apoptosis because of an increased abundance of B cell lymphoma 2 protein (BCL2). In this work, we investigated whether overproduction of COX-2 was involved in the resistance to apoptosis in VSMC fromdiabetic patients. Methods VSMC were obtained from internal mammary arteries from patients who had undergone coronary artery bypass graft surgery. Apoptosis was measured by DNA fragmentation, BCL2 degradation and cytochrome c release. Results Apoptosis induced by C-reactive protein in cells from non-diabetic patients was mediated by COX-2. VSMC from diabetic patients showed higher basal levels of COX-2 compared with those from non-diabetic patients. Transfection of VSMC from non-diabetic patients with a plasmid containing COX-2 (also known as PTGS2) increased basal production of COX-2 and BCL2 and mimicked the resistance to apoptosis that occurs in diabetic patients. We also found a significant correlation (R=0.846, p=0.016) between COX-2 and BCL2 production in arterial rings from diabetic patients measured by confocal microscopy. However, inhibition of COX-2 production by small interfering RNA proved unable to reverse BCL2 production in diabetic VSMC. Conclusions/interpretation These results suggest a link between inflammation (COX-2) and apoptosis resistance (BCL2) in the arteries of diabetic patients. This relationship is not causative and the common production of these two proteins may be co-regulated by shared regulatory elements in diabetes., Depto. de Farmacología y Toxicología, Fac. de Medicina, TRUE, pub

In this paper we estimate the skewness of the unconditional distribution of energy returns and test its statistical significance. We compare the performance of traditional and robust tests for skewness with those based on the implied unconditional skewness in a TGARCH model with Gram-Charlier (TGARCH-GC) innovations. We also analyze the implications of TGARCH-GC skewness for tail risk through evaluation of Value-at-Risk (VaR) and expected shortfall (ES) accuracy. Our results show that crude oil (Brent and WTI) and Gasoline returns are negatively skewed, while we do not find evidence of skewed distribution for other energy returns such as Heating oil, Kerosene and Natural gas. This indicates that the returns of the former are likely to encapsulate more largely the effect of negative shocks and so present higher tail risk than those of the latter. These results differ from traditional and robust tests for skewness providing important information on how to improve mean-variance risk management measures. Indeed, we find that the three-moment VaR and ES measures based on the third-order Cornish Fisher (CF3) expansion for the unconditional distribution of returns considerably improve their corresponding two-moment ones. We adopt CF3 to disentangle skewness effects from kurtosis in tail risk.

This article studies the services exchanged in a particular Spanish time bank. Using data from users and transactions, we analyse the users’ profile as well as the determinants of providing and receiving different services. Our results show that the representative user is a Spanish female, not married, middle aged, highly educated and unemployed. We also find differences in the personal characteristics driving the supply and demand of services., Ministerio de Economía y Competitividad (MINECO), Generalitat Valenciana, Depto. de Análisis Económico y Economía Cuantitativa, Fac. de Ciencias Económicas y Empresariales, TRUE, pub

Turning points in business cycles are defined as the onset of a recession or an expansion which are quite difficult to be predicted. In this thesis, we approach the problem of turning (change) point detection as the viewpoint of binary classification task. Due to the small ratio of changes to total data (as the number of recessions is relatively low), we face heavily class-imbalance challenge in this problem. We explore a wide variety of machine learning-based solutions for this problem: from base classifier to the multi-step classifier ensemble algorithm as well as a feature selection step. We examined the proposed classification methods on Canadian large dataset. Among different examined methods, the hybrid ensemble method including data sampling followed by a feature selection and multi-step ensemble can predict the Covid19 recession’s changepoints precisely with all the time series available one month ago. Some robustness checks such as the effect of window size on the model performance are also provided. Moreover, excluding the financial crisis from the training set, the method 8 is still able to predict the changepoints in the case of financial crisis precisely, however, in the case of the Covid-19 recession, they were detected one-period late, suggesting importance of financial crisis’ data in detecting Covid-19 change points.

This paper illustrates how outliers can affect both the estimation and testing of leverage effect by focusing on the TGARCH model. Three estimation methods are compared through Monte Carlo experiments: Gaussian Quasi-Maximum Likelihood, Quasi-Maximum Likelihood based on the Student-t likelihood and Least Absolute Deviation method. The empirical behavior of the t-ratio and the Likelihood Ratio tests for the significance of the leverage parameter is also analyzed. Our results put forward the unreliability of Gaussian Quasi-Maximum Likelihood methods in the presence of outliers. In particular, we show that one isolated outlier could hide true leverage effect whereas two consecutive outliers bias the estimated leverage coefficient in a direction that crucially depends on the sign of the first outlier and could lead to wrongly reject the null of no leverage effect or to estimate asymmetries of the wrong sign. By contrast, we highlight the good performance of the robust estimators in the presence of one isolated outlier. However, when there are patches of outliers, our findings suggest that the sizes and powers of the tests as well as the estimated parameters based on robust methods may still be distorted in some cases. We illustrate these results with two series of daily returns.

The objective of this paper is to replicate the results in Kristoufek (2014) on the leverage effect in energy futures and to analyze its robustness to both the methodology and the type of returns used. We first apply correlation-based tools for detecting both conditional heteroscedasticity and leverage effect. Then, we estimate asymmetric and long memory GARCH-type models using the data provided by Kristoufek (2014) by considering different software and the possibility that innovations follow a non-Gaussian distribution. Our findings confirm most of the results in the replicated paper. In particular, we can strongly confirm there is a significant leverage effect in the return series of WTI (West Texas Intermediate) and Brent crude oils. For the heating oil and the natural gas series, the statistical significance of the leverage effect depends on both the methodology and the type of returns used.

This article studies the relationship between the prices of fuel and EU Allowances (EUA) for carbon emissions during Phase 3 of the European Union Emissions Trading System. We find that the forward prices of EUA, coal, gas and Brent oil are jointly determined in equilibrium. The existence of such a long-run relationship entails a permanent-transitory decomposition for the series of EUA and fuel prices that reveals the short- and long-term causal influence of the EUA market in shaping the joint dynamics of fuel prices. This result complements the literature that suggests that EUA prices are driven by the dynamics of fuel prices. Interestingly, we do not find an equilibrium relationship in the spot market. EUA and fuel spot prices are driven by independent unit root processes. The differences between spot and forward markets are attributed to the tradability of forward prices that are used for speculation and hedging in financial markets. In contrast, spot prices are mainly driven by supply and demand in energy markets.