1,356,403 research outputs found
Automatic compensation system for impedance measurement
No full textThis paper deals with the realization of the four-pair terminal definition of impedance standards. A simple, though reliable, system is described that allows an automatic compensation of the voltage at the low potential port of impedance standards to be obtained. Such a system employs a commercial data acquisition board and a signal generator with adjustable-phase capability, which acts as the phase reference for the generator that feeds the impedance standard. A standard PC controls the whole system and implements the demodulation and the control algorithms. Preliminary tests have been performed in the frequency range of 50 Hz to 20 kHz with different kinds of impedance standards (resistive, inductive and capacitive), obtaining a residual voltage at the low potential port of less than 5 μV
Quantized calibration in local volatility
No full textPricing of a derivative should be fast and accurate, otherwise it cannot be calibrated efficiently. Here, Giorgia Callegaro,
Lucio Fiorin and Martino Grasselli apply a fast quantization methodology, in a local volatility context, to the pricing of vanilla
and barrier options that overcomes the numerical problems in existing method
Short communication: improvements to INRIM Johnson noise thermometer
No full textThis paper presents a progress report for the Johnson noise thermometry experiment which is under development at the Istituto Nazionale di Ricerca Metrologica. In order to aim at an uncertainty level better than 10−5 and to reduce the measurement time, a new setup has been developed. In particular, several modifications have been applied to the experiment described by Callegaro et al. (Metrologia 46: 409,
2009) to improve the traceability to voltage, resistance and frequency standards, and new amplifiers have been designed in order to expand the working frequency range up to 20 kHz, with a sensing resistor of 1 kohm, while maintaining amplifier induced systematic errors to an acceptable level
An application to credit risk of a hybrid Monte Carlo-optimal quantization method
No full textIn this paper, we use a hybrid Monte Carlo-optimal quantization method to approximate the conditional survival probabilities of a firm, given a structural model for its credit default, under partial information. We consider the case when the firm’s value is a nonobservable stochastic process (Vt)t≽0 and investors in the market have access to a process (St)t≽0, whose value at each time t is related to (Vs,0 ≼ s ≼ t). We are interested in the computation of the conditional survival probabilities of the firm given the “investor’s information”. As an application, we analyze the shape of the credit spread curve for zero-coupon bonds in two examples. Calibration to available market data is also analyzed
The S_{n+1} Action on Spherical Models and Supermaximal models of Type A_{n−1}
No full textIn this paper we recall the construction of the De Concini–Procesi wonderful models of the braid arrangement: these models, in the case of the braid arrangement of type A_{n-1}, are equipped with a natural S_n action, but only the minimal model admits an ‘hidden’ symmetry, i.e. an action of S_{n+1} that comes from its moduli space interpretation. We show that this hidden action can be
lifted to the face poset of the corresponding minimal real spherical model and we compute the number of its orbits. Then we provide a spherical version of the construction of the supermaximal model (see Callegaro, Gaiffi, On models of the braid arrangement and their hidden symmetries. Int. Math. Res. Not. (published online 2015). doi: 10.1093/imrn/rnv009), i.e. the smallest model that can be
projected onto the maximal model and again admits the extended S_{n+1} action
Analysis of gene expression profiles at chromosomal level
No full textTranscriptional profiling of whole genomes using cDNA or oligonucleotide high-density arrays is becoming increasingly popular among the biomedical research community. Although advances in technology and the rapid rise in microarray data availability are leading to new insight into fundamental biological problems, investigators are still confronted with the major problem of upgrading the information content of regulated gene lists obtained from microarray experiments. Indeed, the efficient exploitation of gene expression databases requires not only computational tools for management, analysis, and functional annotation of primary data, but also integrating lists of modulated genes with of other sources of genomic information, such as gene sequence, locus or structural characteristics. In particular, integration between expression profiles and chromosomal localizations could be effective in detecting gene structural abnormalities such as genomic gains and losses and/or translocations. The aim of the present study is to apply computational tools for mapping transcriptional data at chromosomal level and detecting clusters of regionally modulated genes in cancer specimens.Statistical tests and signal processing procedures are used to integrate expression profiles and gene sequence information and identify peculiar regions of modulated expression. In particular, the method is based on the application of a smoothing, coordinate-dependent function (e.g., cubic splines) to a standard transcriptional specificity statistic (e.g., standard F-statistic), commonly used to detect differentially expressed genes.This computational tool has been tested on different microarray data sets obtained from various human tumor samples (e.g., solid tumors and hematological disorders). In particular, the application of chromosomal level analysis to the transcriptional database presented by Bhattacharjee (Bhattacharjee et al., 2001), Armstrong (Armstrong et al., 2002), and Ross (Ross et al., 2003) allowed the detection of regional signals corresponding to known as well as putative loci with high frequent genomic losses and gains or marking translocation events
Optimal Reduction of Public Debt under Partial Observation of the Economic Growth
No full textCallegaro G, Ceci C, Ferrari G. Optimal Reduction of Public Debt under Partial Observation of the Economic Growth. Center for Mathematical Economics Working Papers. Vol 608. Bielefeld: Center for Mathematical Economics; 2019.We consider a government that aims at reducing the debt-to-gross domestic product
(GDP) ratio of a country. The government observes the level of the debt-to-GDP ratio and an
indicator of the state of the economy, but does not directly observe the development of the underlying
macroeconomic conditions. The government's criterion is to minimize the sum of the total expected
costs of holding debt and of debt's reduction policies. We model this problem as a singular stochastic
control problem under partial observation. The contribution of the paper is twofold. Firstly, we
provide a general formulation of the model in which the level of debt-to-GDP ratio and the value of
the macroeconomic indicator evolve as a diffusion and a jump-diffusion, respectively, with coefficients
depending on the regimes of the economy. These are described through a finite-state continuous-time
Markov chain. We reduce via filtering techniques the original problem to an equivalent one with full
information (the so-called separated problem), and we provide a general verification result in terms of
a related optimal stopping problem under full information. Secondly, we specialize to a case study in
which the economy faces only two regimes, and the macroeconomic indicator has a suitable diffusive
dynamics. In this setting we provide the optimal debt reduction policy. This is given in terms of the
continuous free boundary arising in an auxiliary fully two-dimensional optimal stopping problem
A Fully Quantization-based Scheme for FBSDEs
No full textWe propose a quantization-based numerical scheme for a family of decoupled FBSDEs. We simplify the scheme for the control in Pagès and Sagna (2018) so that our approach is fully based on recursive marginal quantization and does not involve any Monte Carlo simulation for the computation of conditional expectations. We analyse in detail the numerical error of our scheme and we show through some examples the performance of the whole procedure, which proves to be very effective in view of financial applications
A locally adaptive statistical procedure (LAP) to identify differentially expressed chromosomal regions
No full textMotivation: The systematic integration of expression profiles and other types of gene information, such as chromosomal localization, ontological annotations and sequence characteristics, still represents a challenge in the gene expression arena. In particular, the analysis of transcriptional data in context of the physical location of genes in a genome appears promising in detecting chromosomal regions with transcriptional imbalances often characterizing cancer.Results: A computational tool named locally adaptive statistical procedure (LAP), which incorporates transcriptional data and structural information for the identification of differentially expressed chromosomal regions, is described. LAP accounts for variations in the distance between genes and in gene density by smoothing standard statistics on gene position before testing the significance of their differential levels of gene expression. The procedure smoothes parameters and computes p-values locally to account for the complex structure of the genome and to more precisely estimate the differential expression of chromosomal regions. The application of LAP to three independent sets of raw expression data allowed identifying differentially expressed regions that are directly involved in known chromosomal aberrations characteristic of tumors
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