5,476 research outputs found
Estimating factor models for multivariate volatilities : an innovation expansion method
We introduce an innovation expansion method for estimation of factor models for conditional variance (volatility) of a multivariate time series. We estimate the factor loading space and the number of factors by a stepwise optimization algorithm on expanding the "white noise space". Simulation and a real data example are given for illustration
Forecasting Transaction Rates: The Autoregressive Conditional Duration Model
This paper will propose a new statistical model for the analysis of data that does not arrive in equal time intervals such as financial transactions data, telephone calls, or sales data on commodities that are tracked electronically. In contrast to fixed interval analysis, the model treats the time between observation arrivals as a stochastic time varying process and therefore is in the spirit of the models of time deformation initially proposed by Tauchen and Pitts (1983), Clark (1973) and more recently discussed by Stock (1988), Lamoureux and Lastrapes (1992), Muller et al. (1990) and Ghysels and Jasiak (1994) but does not require auxiliary data or assumptions on the causes of time flow. Strong evidence is provided for duration clustering beyond a deterministic component for the financial transactions data analyzed. We will show that a very simple version of the model can successfully account for the significant autocorrelations in the observed durations between trades of IBM stock on the consolidated market. A simple transformation of the duration data allows us to include volume in the model.
Low-energy electron transport with the method of discrete ordinates
The one-dimensional discrete ordinates code ANISN was adapted to transport low energy (a few MeV) electrons. Calculated results obtained with ANISN were compared with experimental data for transmitted electron energy and angular distribution data for electrons normally incident on aluminum slabs of various thicknesses. The calculated and experimental results are in good agreement for a thin slab (0.2 of the electron range), but not for the thicker slabs (0.6 of the electron range). Calculated results obtained with ANISN were also compared with results obtained using Monte Carlo methods
Value at Risk models with long memory features and their economic performance
We study alternative dynamics for Value at Risk (VaR) that incorporate a slow moving component and information on recent aggregate returns in established quantile (auto) regression models. These models are compared on their economic performance, and also on metrics of first-order importance such as violation ratios. By better economic performance, we mean that changes in the VaR forecasts should have a lower variance to reduce transaction costs and should lead to lower exceedance sizes without raising the average level of the VaR. We find that, in combination with a targeted estimation strategy, our proposed models lead to improved performance in both statistical and economic terms
Asymptotics of 4d spin foam models
We study the asymptotic properties of four-simplex amplitudes for various
four-dimensional spin foam models. We investigate the semi-classical limit of
the Ooguri, Euclidean and Lorentzian EPRL models using coherent states for the
boundary data. For some classes of geometrical boundary data, the asymptotic
formulae are given, in all three cases, by simple functions of the Regge action
for the four-simplex geometry.Comment: 10 pages, Proceedings for the 2nd Corfu summer school and workshop on
quantum gravity and quantum geometry, talk given by Winston J. Fairbair
Hedgehog Pathway Activation Alters Ciliary Signaling in Primary Hypothalamic Cultures
Primary cilia dysfunction has been associated with hyperphagia and obesity in both ciliopathy patients and mouse models of cilia perturbation. Neurons throughout the brain possess these solitary cellular appendages, including in the feeding centers of the hypothalamus. Several cell biology questions associated with primary neuronal cilia signaling are challenging to address in vivo. Here we utilize primary hypothalamic neuronal cultures to study ciliary signaling in relevant cell types. Importantly, these cultures contain neuronal populations critical for appetite and satiety such as pro-opiomelanocortin (POMC) and agouti related peptide (AgRP) expressing neurons and are thus useful for studying signaling involved in feeding behavior. Correspondingly, these cultured neurons also display electrophysiological activity and respond to both local and peripheral signals that act on the hypothalamus to influence feeding behaviors, such as leptin and melanin concentrating hormone (MCH). Interestingly, we found that cilia mediated hedgehog signaling, generally associated with developmental processes, can influence ciliary GPCR signaling (Mchr1) in terminally differentiated neurons. Specifically, pharmacological activation of the hedgehog-signaling pathway using the smoothened agonist, SAG, attenuated the ability of neurons to respond to ligands (MCH) of ciliary GPCRs. Understanding how the hedgehog pathway influences cilia GPCR signaling in terminally differentiated neurons could reveal the molecular mechanisms associated with clinical features of ciliopathies, such as hyperphagia-associated obesity
Revisiting the Simplicity Constraints and Coherent Intertwiners
In the context of loop quantum gravity and spinfoam models, the simplicity
constraints are essential in that they allow to write general relativity as a
constrained topological BF theory. In this work, we apply the recently
developed U(N) framework for SU(2) intertwiners to the issue of imposing the
simplicity constraints to spin network states. More particularly, we focus on
solving them on individual intertwiners in the 4d Euclidean theory. We review
the standard way of solving the simplicity constraints using coherent
intertwiners and we explain how these fit within the U(N) framework. Then we
show how these constraints can be written as a closed u(N) algebra and we
propose a set of U(N) coherent states that solves all the simplicity
constraints weakly for an arbitrary Immirzi parameter.Comment: 28 page
Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models
We examine the properties and forecast performance of multiplicative volatility
specifications that belong to the class of generalized autoregressive conditional
heteroskedasticity–mixed-data sampling (GARCH-MIDAS) models suggested in
Engle, Ghysels, and Sohn (Review of Economics and Statistics, 2013, 95, 776–797).
In those models volatility is decomposed into a short-term GARCH component
and a long-term component that is driven by an explanatory variable. We derive
the kurtosis of returns, the autocorrelation function of squared returns, and
the R2 of a Mincer–Zarnowitz regression and evaluate the QMLE and forecast
performance of these models in a Monte Carlo simulation. For S&P 500 data,
we compare the forecast performance of GARCH-MIDAS models with a wide
range of competitor models such as HAR (heterogeneous autoregression), realized GARCH, HEAVY (high-frequency-based volatility) and Markov-switching
GARCH. Our results show that the GARCH-MIDAS based on housing starts
as an explanatory variable significantly outperforms all competitor models at
forecast horizons of 2 and 3 months ahead
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