1,036 research outputs found
Eigenvalue Dynamics of a Central Wishart Matrix with Application to MIMO Systems
We investigate the dynamic behavior of the stationary random process defined
by a central complex Wishart (CW) matrix as it varies along a
certain dimension . We characterize the second-order joint cdf of the
largest eigenvalue, and the second-order joint cdf of the smallest eigenvalue
of this matrix. We show that both cdfs can be expressed in exact closed-form in
terms of a finite number of well-known special functions in the context of
communication theory. As a direct application, we investigate the dynamic
behavior of the parallel channels associated with multiple-input
multiple-output (MIMO) systems in the presence of Rayleigh fading. Studying the
complex random matrix that defines the MIMO channel, we characterize the
second-order joint cdf of the signal-to-noise ratio (SNR) for the best and
worst channels. We use these results to study the rate of change of MIMO
parallel channels, using different performance metrics. For a given value of
the MIMO channel correlation coefficient, we observe how the SNR associated
with the best parallel channel changes slower than the SNR of the worst
channel. This different dynamic behavior is much more appreciable when the
number of transmit () and receive () antennas is similar. However, as
is increased while keeping fixed, we see how the best and worst
channels tend to have a similar rate of change.Comment: 15 pages, 9 figures and 1 table. This work has been accepted for
publication at IEEE Trans. Inf. Theory. Copyright (c) 2014 IEEE. Personal use
of this material is permitted. However, permission to use this material for
any other purposes must be obtained from the IEEE by sending a request to
[email protected]
Graph presentations for moments of noncentral Wishart distributions and their applications
We provide formulas for the moments of the real and complex noncentral
Wishart distributions of general degrees. The obtained formulas for the real
and complex cases are described in terms of the undirected and directed graphs,
respectively. By considering degenerate cases, we give explicit formulas for
the moments of bivariate chi-square distributions and Wishart
distributions by enumerating the graphs. Noting that the Laguerre polynomials
can be considered to be moments of a noncentral chi-square distributions
formally, we demonstrate a combinatorial interpretation of the coefficients of
the Laguerre polynomials
A mixed effects model for longitudinal relational and network data, with applications to international trade and conflict
The focus of this paper is an approach to the modeling of longitudinal social
network or relational data. Such data arise from measurements on pairs of
objects or actors made at regular temporal intervals, resulting in a social
network for each point in time. In this article we represent the network and
temporal dependencies with a random effects model, resulting in a stochastic
process defined by a set of stationary covariance matrices. Our approach builds
upon the social relations models of Warner, Kenny and Stoto [Journal of
Personality and Social Psychology 37 (1979) 1742--1757] and Gill and Swartz
[Canad. J. Statist. 29 (2001) 321--331] and allows for an intra- and
inter-temporal representation of network structures. We apply the methodology
to two longitudinal data sets: international trade (continuous response) and
militarized interstate disputes (binary response).Comment: Published in at http://dx.doi.org/10.1214/10-AOAS403 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Approximate inference of the bandwidth in multivariate kernel density estimation
Kernel density estimation is a popular and widely used non-parametric method for data-driven density estimation. Its appeal lies in its simplicity and ease of implementation, as well as its strong asymptotic results regarding its convergence to the true data distribution. However, a major difficulty is the setting of the bandwidth, particularly in high dimensions and with limited amount of data. An approximate Bayesian method is proposed, based on the Expectation–Propagation algorithm with a likelihood obtained from a leave-one-out cross validation approach. The proposed method yields an iterative procedure to approximate the posterior distribution of the inverse bandwidth. The approximate posterior can be used to estimate the model evidence for selecting the structure of the bandwidth and approach online learning. Extensive experimental validation shows that the proposed method is competitive in terms of performance with state-of-the-art plug-in methods
Option Pricing in Multivariate Stochastic Volatility Models of OU Type
We present a multivariate stochastic volatility model with leverage, which is
flexible enough to recapture the individual dynamics as well as the
interdependencies between several assets while still being highly analytically
tractable.
First we derive the characteristic function and give conditions that ensure
its analyticity and absolute integrability in some open complex strip around
zero. Therefore we can use Fourier methods to compute the prices of multi-asset
options efficiently. To show the applicability of our results, we propose a
concrete specification, the OU-Wishart model, where the dynamics of each
individual asset coincide with the popular Gamma-OU BNS model. This model can
be well calibrated to market prices, which we illustrate with an example using
options on the exchange rates of some major currencies. Finally, we show that
covariance swaps can also be priced in closed form.Comment: 28 pages, 5 figures, to appear in SIAM Journal on Financial
Mathematic
Estimating Correlated Jumps and Stochastic Volatilities
We formulate a bivariate stochastic volatility jump-diffusion model with correlated jumps and volatilities. An MCMC Metropolis-Hastings sampling algorithm is proposed to estimate the model’s parameters and latent state variables (jumps and stochastic volatilities) given observed returns. The methodology is successfully tested on several artificially generated bivariate time series and then on the two most important Czech domestic financial market time series of the FX (CZK/EUR) and stock (PX index) returns. Four bivariate models with and without jumps and/or stochastic volatility are compared using the deviance information criterion (DIC) confirming importance of incorporation of jumps and stochastic volatility into the model.jump-diffusion, stochastic volatility, MCMC, Value at Risk, Monte Carlo
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