The upgrade of the ALICE Inner Tracking System

The Inner Tracking System (ITS) of the ALICE experiment will be upgraded during the second long LHC shutdown in $\mathrm{2019}-\mathrm{2020}$. The main goal of the ALICE ITS Upgrade is to enable high precision measurements of low - momentum particles (< 1 GeV/c) by acquiring a large sample of events, benefiting from the increase of the LHC instantaneous luminosity of $\mathrm{Pb}-\mathrm{Pb}$ collisions to $\mathcal{L} = 6 \cdot 10^{27} cm^{-2} s^{-1}$ during Run 3. Working in this direction the ITS upgrade project is focusing on the increase of the readout rate, on the improvement of the impact parameter resolution, as well as on the improvement of the tracking efficiency and the position resolution. The major setup modification is the substitution of the current ITS with seven layers of silicon pixel detectors. The ALPIDE chip, a CMOS Monolithic Active Pixel Sensor (MAPS), was developed for this purpose and offers a spatial resolution of 5 $\mu$m. The use of MAPS together with a stringent mechanical design allows for the reduction of the material budget down to 0.35% $X_0$ for the innermost layers and 1% $X_0$ for the outer layers. The detector design was validated during the research and development period through a variety of tests ensuring the proper operation for the full lifetime inside ALICE. The production phase is close to completion with all the new assembled components undergoing different tests that aim to characterize the modules and staves and determine their qualification level. This contribution describes the detector design, the measurements performed during the research and development phase, as well as the production status

Estimating the Impact of Monetary Policy Surprises on Fixed-Income Markets

In the interest of better understanding the impact of the Bank of Canada's policy actions on bond and bill yields, Andreou assesses the impact of policy-rate announcements on short and long bonds over the period 1996 to 2004. To aid the analysis, policy actions are decomposed into expected and surprise components. He also examines whether the introduction of fixed announcement dates (FADs) has affected these results, including markets' perceptions. The main finding is that unexpected policy actions by the Bank have a significant effect on market rates at the shorter end of the yield curve, with the effect dissipating as the maturity increases. A second finding, that the impact on longer-term interest rates of a surprise action by the Bank has diminished since the introduction of the FADs, suggests that the Bank's long-term policy goals are well understood and credible.

A Simple Asymptotic Analysis of Residual-Based Statistics

What s the asymptotic null distribution of a rank-based serial autocorrelation test applied to residuals of an estimated GARCH model?What s the limiting distribution of estimated ACD parameters applied to the residuals of some first-stage modelling procedure?This paper addresses the often occurring situation in econometrics of applying standard statistics to residuals instead of innovations.The paper provides a simple and unified way of calculating the necessary adjustment in the limiting distribution, be it of tests or estimators. On the technical side, we also provide a novel approach to this problem using Le Cam s theory of convergence of experiments (in this paper restricted to Gaussian shift experiments).The resulting formula is simple and the regularity conditions required fairly minimal.Numerous examples show the strength and wide applicability of our approach.statistics;estimation;ranking

The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests

The paper evaluates the performance of several recently proposed change-point tests applied to conditional variance dynamics and conditional distributions of asset returns. These are CUSUM-type tests for beta-mixing processes and EDF-based tests for the residuals of such nonlinear dependent processes. Hence the tests apply to the class of ARCH and SV type processes as well as data-driven volatility estimators using high-frequency data. It is shown that some of the high-frequency volatility estimators substantially improve the power of the structural breaks tests especially for detecting changes in the tail of the conditional distribution. Similarly, certain types of filtering and transformation of the returns process can improve the power of CUSUM statistics. We also explore the impact of sampling frequency on each of the test statistics. Ce papier évalue la performance de plusieurs tests de changement structurel CUSUM et EDF pour la structure dynamique de la variance conditionelle et de la distribution conditionnelle. Nous étudions l'impact 1) de la fréquence des observations, 2) de l'utilisation des données de haute fréquence pour le calcul des variances conditionnelles et 3) de transformation des séries pour améliorer la puissance des tests.Change-point tests, CUSUM, Kolmogorov-Smirnov, GARCH, quadratic variation, power variation, high-frequency data, location-scale distribution family, tests de changement structurel, CUSUM, Kolmogov-Smirnov, GARCH, variation quadratique, 'power variation', données de haute fréquence

Quality Control for Structural Credit Risk Models

Over the last four decades, a large number of structural models have been developed to estimate and price credit risk. The focus of the paper is on a neglected issue pertaining to fundamental shifts in the structural parameters governing default. We propose formal quality control procedures that allow risk managers to monitor fundamental shifts in the structural parameters of credit risk models. The procedures are sequential - hence apply in real time. The basic ingredients are the key processes used in credit risk analysis, such as most prominently the Merton distance to default process as well as financial returns. Moreover, while we propose different monitoring processes, we also show that one particular process is optimal in terms of minimal detection time of a break in the drift process and relates to the Radon-Nikodym derivative for a change of measure.

Test for Breaks in the Conditional Co-Movements of Asset Returns

We propose procedures designed to uncover structural breaks in the co-movements of financial markets. A reduced form approach is introduced that can be considered as a two stage method for reducing dimensionality of multivariate heteroskedastic conditional volatility models through marginalization. The main advantage is that one can use returns normalized by volatility filters that are purely data-driven and construct general conditional covariance dynamic specifications. The main thrust of our procedure is to examine change-points in the co-movements of normalized returns. We document, using a ten year period of two representative high frequency FX series, that regression models with non-Gaussian errors describe adequately their co-movements. Change-points are detected in the conditional covariance of the DM/US$and YN/US$ normalized returns over the decade 1986-1996.change-point tests, conditional covariance, high-frequency financial data, multivariate GARCH models

Monitoring for Disruptions in Financial Markets

Historical and sequential CUSUM change-point tests for strongly dependent nonlinear processes are studied. These tests are used to monitor the conditional variance of asset returns and to provide early information regarding instabilities or disruptions in financial risk. Data-driven monitoring schemes are investigated. Since the processes are strongly dependent several novel issues require special attention. One such issue is the sampling frequency. We study the power of detection as sampling frequencies vary. Analytical local power results are obtained for historical CUSUM tests and simulation evidence is presented for sequential tests. Finally, a prediction-based statistic is introduced that reduces the detection delay considerably. The prediction based formula is based on a local Brownian bridge approximation argument and provides an assessment of the likelihood of change-points. Nous étudions les tests CUSUM historiques et séquentiels pour des séries dépendantes avec des applications en finance. Pour les processus temporels, une nouvelle dimension se présente : l'effet du choix de la fréquence des observations. Un nouveau test est également proposé. Ce test est basé sur une formule de prévision locale d'un pont brownien.structural change, CUSUM, GARCH, quadratic variation, power variation, high frequency data, Brownian bridge, boundary crossing, sequential tests, local power, changement structurel, CUSUM, GARCH, variation quadratique, 'power variation', données de haute fréquence, pont Brownien, puissance locale, tests séquentiels

A network-aware framework for energy-efficient data acquisition in wireless sensor networks

Wireless sensor networks enable users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, the data management community has proposed the utilization of declarative data-acquisition frameworks. While these frameworks have facilitated the energy-efficient retrieval of data from the physical environment, they were agnostic of the underlying network topology and also did not support advanced query processing semantics. In this paper we present KSpot+, a distributed network-aware framework that optimizes network efficiency by combining three components: (i) the tree balancing module, which balances the workload of each sensor node by constructing efficient network topologies; (ii) the workload balancing module, which minimizes data reception inefficiencies by synchronizing the sensor network activity intervals; and (iii) the query processing module, which supports advanced query processing semantics. In order to validate the efficiency of our approach, we have developed a prototype implementation of KSpot+ in nesC and JAVA. In our experimental evaluation, we thoroughly assess the performance of KSpot+ using real datasets and show that KSpot+ provides significant energy reductions under a variety of conditions, thus significantly prolonging the longevity of a WSN

Extensions and applications of a second-order landsurface parameterization

Extensions and applications of a second order land surface parameterization, proposed by Andreou and Eagleson are developed. Procedures for evaluating the near surface storage depth used in one cell land surface parameterizations are suggested and tested by using the model. Sensitivity analysis to the key soil parameters is performed. A case study involving comparison with an "exact" numerical model and another simplified parameterization, under very dry climatic conditions and for two different soil types, is also incorporated

House Prices and School Quality: The Impact of Score and Non-score Components of Contextual Value-Added

This paper investigates how the newly introduced Contextual Value Added (CVA) indicator of school quality affects house prices in the catchment area of primary and secondary schools in England. The empirical analysis, based on the data drawn from three independent and previously unexplored UK data sources, shows that the score component of CVA has a strong positive effect on house prices at both primary and secondary levels of education; while the non-score component of this school quality indicator has a significant (negative) effect only in the analysis of secondary school data. Nevertheless, the effect of CVA and its score and non-score components on house prices also varies with the level of spatial aggregation at which empirical investigation is pursued, assuming a more positive role between rather than within Local Authorities (Las). This reflects the emphasis placed by CVA on public good aspects of school quality and suggests that LA policies aimed at raising the average non-score quality characteristics of school conform to household preferences.School quality, hedonic regression, house prices