LARCH, Leverage and Long Memory

We consider the long memory and leverage properties of a model for the conditional variance of an observable stationary sequence, where the conditional variance is the square of an inhomogeneous linear combination of past values of the observable sequence, with square summable weights. This model, which we call linear ARCH (LARCH), specializes to the asymmetric ARCH model of Engle (1990), and to a version of the quadratic ARCH model of Sentana (1995), these authors having discussed leverage potential in such models. The model which we consider was suggested by Robinson (1991), for use as a possibly long memory conditionally heteroscedastic alternative to i.i.d. behaviour, and further studied by Giraitis, Robinson and Surgailis (2000), who showed that integer powers, of degree at least 2, can have long memory autocorrelation. We establish conditions under which the cross-autovariance function between volatility and levels decays in the manner of moving average weights of long memory processes. We also establish a leverage property and conditions for finiteness of third and higher moments.Leverage, long memory, linear ARCH, LARCH, finiteness of moments.

Semiparametric Approaches to the Prediction of Conditional Correlation Matrices in Finance

We consider the problem of ex-ante forecasting conditional correlation patterns using ultra high frequency data. Flexible semiparametric predictors referring to the class of dynamic panel and dynamic factor models are adopted for daily forecasts. The parsimonious set up of our approach allows to forecast correlations exploiting both estimated realized correlation matrices and exogenous factors. The Fisher-z transformation guarantees robustness of correlation estimators under elliptically constrained departures from normality. For the purpose of performance comparison we contrast our methodology with prominent parametric and nonparametric alternatives to correlation modeling. Based on economic performance criteria, we distinguish dynamic factor models as having the highest predictive content. --Correlation forecasting,Epps effect,Fourier method,Dynamic panel model,Dynamic factor model

Ether: Bitcoin's competitor or ally?

Although Bitcoin has long been dominant in the crypto scene, it is certainly not alone. Ether is another cryptocurrency related project that has attracted an intensive attention because of its additional features. This study seeks to test whether these cryptocurrencies differ in terms of their volatile and speculative behaviors, hedge, safe haven and risk diversification properties. Using different econometric techniques, we show that a) Bitcoin and Ether are volatile and relatively more responsive to bad news, but the volatility of Ether is more persistent than that of Bitcoin; b) for both cryptocurrencies, the exuberance and the collapse of bubbles were identified, but Bitcoin appears more speculative than Ether; c) there is negative and significant correlation between Bitcoin/Ether and other assets (S\&P500 stocks, US bonds, oil), which would indicate that digital currencies can hedge against the price movements of these assets; d) there is negative tail independence between Bitcoin/Ether and other financial assets, implying that these cryptocurrencies exhibit the function of a weak safe haven; and e) The inclusion of Bitcoin/ Ether in a portfolio improve its efficiency in terms of higher reward-to-risk ratios. But investors who hold diversified portfolios made of stocks or bonds and Ether may face losses over bearish regime. In such situation, stock and bond investors may take a short position on Bitcoin

Semiparametric Regression During 2003–2007

Semiparametric regression is a fusion between parametric regression and nonparametric regression and the title of a book that we published on the topic in early 2003. We review developments in the field during the five year period since the book was written. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application

The underground economy : definition, measurement and its consequences for the fiscal stance : an application to the Netherlands Antilles

Mestrado em EconomiaThis dissertation investigates the underground economy in the Netherlands Antilles. I start with a review of the concept of underground economy, to establish its definition in this dissertation, and provide some insight into its main causes and consequences. Estimating the size of the underground economy is difficult, because those operating underground have an incentive to conceal their activity and avoid detection by the authorities. The methods proposed in the literature to measure the size of the underground economy are reviewed. I proceed with an empirical analysis of the underground economy for the Netherlands Antilles. Finally, I assess its consequences for the fiscal stance of the Netherlands Antilles and conclude that once the impact of the underground economy is accounted for the fiscal stance turns sustainable. So, while eliminating the underground economy totally is almost impossible, efforts to reduce its size are imperative.Esta dissertação estuda a economia subterrânea das Antilhas Holandesas. Começa por apresentar o conceito de economia subterrãnea, de forma a definir este conceito no contexto desta dissertação. Avança ainda, com as principais causas e consequências da economia subterrânea. É difícil estimar a economia subterrânea porque os agentes envolvidos em actividades subterrâneas têm incentivos para ocultá-las e evitar que sejam detectadas pelas autoridades. Os métodos propostos na literatura para estimar a economia subterrãnea são apresentados. Segue-se uma análise empírica do caso das Antilhas Holandesas. Esta dissertação conclui com a analise das consequências da economia subterrânea para a sustentabilidade das finanças publicas. A conclusão é que as finanças publicas das Antilhas Holandesas tornam-se sustentáveis quando corrigidas para o impacto da economia subterrãnea. Conclui-se portanto que, embora seja praticamente impossível erradicar por completo a economia subterrãnea, é fundamental reduzir o seu peso.info:eu-repo/semantics/publishedVersio

1 A Primal-Dual Proximal Algorithm for Sparse Template-Based Adaptive Filtering: Application to Seismic Multiple Removal

Abstract—Unveiling meaningful geophysical information from seismic data requires to deal with both random and structured “noises”. As their amplitude may be greater than signals of interest (primaries), additional prior information is especially important in performing efficient signal separation. We address here the problem of multiple reflections, caused by wave-field bouncing between layers. Since only approximate models of these phenomena are available, we propose a flexible framework for time-varying adaptive filtering of seismic signals, using sparse representations, based on inaccurate templates. We recast the joint estimation of adaptive filters and primaries in a new convex variational formulation. This approach allows us to incorporate plausible knowledge about noise statistics, data sparsity and slow filter variation in parsimony-promoting wavelet frames. The designed primal-dual algorithm solves a constrained minimization problem that alleviates standard regularization issues in finding hyperparameters. The approach demonstrates significantly good performance in low signal-to-noise ratio conditions, both for simulated and real field seismic data. Index Terms—Convex optimization, Parallel algorithms, Wavelet transforms, Adaptive filters, Geophysical signal processing, Signal restoration, Sparsity, Signal separation