CORRELATIONS BETWEEN OIL AND STOCK MARKETS: A WAVELET-BASED APPROACH

In a global economy, shocks occurring in one market can spill over to other markets. This paper investigates the impact of oil shocks and stock markets crashes on correlations between stock and oil markets. We test changes in correlations at different scales with non-overlapping confidence intervals based on estimated wavelet correlations. Contrary to other approaches, this method does not need adjustment for heteroskedasticity biases on the correlation coefficients. Our results show that oil shocks affect the correlation between both markets. The evidence on the change of correlation between stock markets after an oil shock is weaker; except in some specific cases during the Kuwait war and the OPEC cutback period. Conversely, we only find weak evidence that stock market crashes change the correlation between oil and stock markets. Overall, the evidence gives support to including oil as an asset class in asset allocation strategies.he authors acknowledge financial support from Financial Research Center–UNIDE and from the Spanish Ministry of Education and Science, research projects MTM2010-17323, ECO2011-25706, ECO2012-32401 and MTM2012-36163-C06-03

Binarized support vector machines

The widely used Support Vector Machine (SVM) method has shown to yield very good results in Supervised Classification problems. Other methods such as Classification Trees have become more popular among practitioners than SVM thanks to their interpretability, which is an important issue in Data Mining. In this work, we propose an SVM-based method that automatically detects the most important predictor variables, and the role they play in the classifier. In particular, the proposed method is able to detect those values and intervals which are critical for the classification. The method involves the optimization of a Linear Programming problem, with a large number of decision variables. The numerical experience reported shows that a rather direct use of the standard Column-Generation strategy leads to a classification method which, in terms of classification ability, is competitive against the standard linear SVM and Classification Trees. Moreover, the proposed method is robust, i.e., it is stable in the presence of outliers and invariant to change of scale or measurement units of the predictor variables. When the complexity of the classifier is an important issue, a wrapper feature selection method is applied, yielding simpler, still competitive, classifiers

Posthumanist Feminism and Interspecies Affect in Nalo Hopkinson’s Midnight Robber

AbstractThis paper examines the posthuman affective communities in Nalo Hopkinson’s dystopia Midnight Robber (2000), from an intersectional approach. It focuses on the interspecies affinity developed between a cyborg Black girl and other posthuman beings in outer space, where subaltern ‘artisans,’ machines, and indigenous communities provide nurturing affects of love and compassion that engender mutual respect and solidarity.RésuméCet article examine les communautés affectives posthumaines dans la dystopie Midnight Robber (2000) de Nalo Hopkinson, selon une approche intersectionnelle. Il met l’accent sur l’affinité interspécifique qui se développe entre une cyborg noire et d’autres êtres posthumains dans l’espace, où des « artisans » subalternes, des machines et des communautés indigènes fournissent des affects enrichissants d’amour et de compassion qui engendrent le respect mutuel et la solidarité

Computing (R, S) policies with correlated demand

This paper considers the single-item single-stocking non-stationary stochastic lot-sizing problem under correlated demand. By operating under a nonstationary (R, S) policy, in which R denote the reorder period and S the associated order-up-to-level, we introduce a mixed integer linear programming (MILP) model which can be easily implemented by using off-theshelf optimisation software. Our modelling strategy can tackle a wide range of time-seriesbased demand processes, such as autoregressive (AR), moving average(MA), autoregressive moving average(ARMA), and autoregressive with autoregressive conditional heteroskedasticity process(AR-ARCH). In an extensive computational study, we compare the performance of our model against the optimal policy obtained via stochastic dynamic programming. Our results demonstrate that the optimality gap of our approach averages 2.28% and that computational performance is good

Epicatechin induces NF-kappa B, activator rotein-1 (AP-1) and nuclear transcription factor erythroid 2p45-related factor-2 (Nrf2) via phosphatidylinositol-3-kinase/protein kinase B (PI3K/AKT) and extracellular regulated kinase (ERK) signalling in HepG2 cells

The dietary flavonoid epicatechin has been reported to exhibit a wide range of biological activities. The objective of the present study was to investigate the time-dependent regulation by epicatechin on the activity of the main transcription factors (NF-kappa B, activator protein-1 (AP-1) and nuclear transcription factor erythroid 2p45-related factor (Nrf2)) related to antioxidant defence and survival and proliferation pathways in HepG2 cells. Treatment of cells with 10 mu M-epicatechin induced the NF-kappa B pathway in a time-dependent manner characterised by increased levels Of I kappa B kinase (IKK) and phosphorylated inhibitor Of kappa B subunit-a (p-I kappa B alpha) and proteolytic degradation Of I kappa B, which was consistent with an up-regulation of the NF-kappa B-binding activity. Time-dependent activation of the AP-1 pathway, in concert with enhanced c-Jun nuclear levels and induction of Nrf2 translocation and phosphorylation were also demonstrated. Additionally, epicatechin-induced NF-kappa B and Nrf2 were connected to reactive oxygen species intracellular levels and to the activation of cell survival and proliferation pathways, being phosphatidylinositol-3-kinase/protein kinase B (PI3K/AKT) and extracellular regulated kinase (ERK) associated to Nrf2 modulation and ERK to NF-kappa B induction. These data suggest that the epicatechin-induced survival effect occurs by the induction of redox-sensitive transcription factors through a tight regulation of survival and proliferation pathways

Detecting outliers in multivariate volatility models:A wavelet procedure

It is well known that outliers can affect both the estimation of parameters and volatilities when fitting a univariate GARCH-type model. Similar biases and impacts are expected to be found on correlation dynamics in the context of multivariate time series. We study the impact of outliers on the estimation of correlations when fitting multivariate GARCH models and propose a general detection algorithm based on wavelets, that can be applied to a large class of multivariate volatility models. Its effectiveness is evaluated through a Monte Carlo study before it is applied to real data. The method is both effective and reliable, since it detects very few false outliers