279 research outputs found
Forecasting Realized Volatility with Linear and Nonlinear Models
In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 indexes. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high frequency intra-day returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed in the paper.neural networks;nonlinear models;financial econometrics;realized volatility;bagging;volatility forecasting
Asymmetry and leverage in realized volatility
A wide variety of conditional and stochastic variance models has been used to estimate latent volatility (or risk). In both the conditional and stochastic volatility literature, there has been some confusion between the definitions of asymmetry and leverage. In this paper, we first show the relationship among conditional, stochastic, integrated and realized volatilities. Then we develop a new asymmetric volatility model, which takes account of small and large, and positive and negative, shocks. Using the new specification, we examine alternative volatility models that have recently been developed and estimated in order to understand the differences and similarities in the definitions of asymmetry and leverage. We extend the new specification to realized volatility by taking account of measurement errors. As an empirical example, we apply the new model to the realized volatility of Standard and PoorââŹâ˘s 500 Composite Index using Efficient Importance Sampling to show the new specification of asymmetry significantly improves the goodness of fit.
Structure and Asymptotic theory for Nonlinear Models with GARCH Errors
Nonlinear time series models, especially those with regime-switching and conditionally heteroskedastic
errors, have become increasingly popular in the economics and finance literature. However, much of the research has concentrated on the empirical applications of various models, with little theoretical or statistical
analysis associated with the structure of the processes or the associated asymptotic theory. In this paper,
we first derive necessary conditions for strict stationarity and ergodicity of three different specifications of
the first-order smooth transition autoregressions with heteroskedastic errors. This is important, among other
reasons, to establish the conditions under which the traditional LMlinearity tests based on Taylor expansions
are valid. Second, we provide sufficient conditions for consistency and asymptotic normality of the Quasi-
Maximum Likelihood Estimator for a general nonlinear conditional mean model with first-order GARCH
errors
Asymmetry and Long Memory in Volatility Modelling
A wide variety of conditional and stochastic variance models has been used to estimate latent volatility (or risk). In this paper, we propose a new long memory asymmetric volatility model which captures more flexible asymmetric patterns as compared with existing models. We extend the new specification to realized volatility by taking account of measurement errors, and use the Efficient Importance Sampling technique to estimate the model. As an empirical example, we apply the new model to the realized volatility of Standard and Poorâs 500 Composite Index to show that the new specification of asymmetry significantly improves the goodness of fit, and that the out-of-sample forecasts and Value-at-Risk (VaR) thresholds are satisfactory. Overall, the results of the out-of-sample forecasts show the adequacy of the new asymmetric and long memory volatility model for the period including the global financial crisis
Asymmetry and leverage in realized volatility
A wide variety of conditional and stochastic variance models has been used to estimate latent volatility (or risk). In both the conditional and stochastic volatility literature, there has been some confusion between the definitions of asymmetry and leverage. In this paper, we first show the relationship among conditional, stochastic, integrated and realized volatilities. Then we develop a new asymmetric volatility model, which takes account of small and large, and positive and negative, shocks. Using the new specification, we examine alternative volatility models that have recently been developed and estimated in order to understand the differences and similarities in the definitions of asymmetry and leverage. We extend the new specification to realized volatility by taking account of measurement errors. As an empirical example, we apply the new model to the realized volatility of Standard and Poorâs 500 Composite Index using Efficient Importance Sampling to show the new specification of asymmetry significantly improves the goodness of fit
Moment-bases estimation of smooth transition regression models with endogenous variables
Nonlinear regression models have been widely used in practice for a variety of time series and
cross-section datasets. For purposes of analyzing univariate and multivariate time series data, in particular,
Smooth Transition Regression (STR) models have been shown to be very useful for representing and capturing
asymmetric behavior. Most STR models have been applied to univariate processes, and have made a
variety of assumptions, including stationary or cointegrated processes, uncorrelated, homoskedastic or conditionally
heteroskedastic errors, and weakly exogenous regressors. Under the assumption of exogeneity,
the standard method of estimation is nonlinear least squares. The primary purpose of this paper is to relax
the assumption of weakly exogenous regressors and to discuss moment based methods for estimating STR
models. The paper analyzes the properties of the STR model with endogenous variables by providing a diagnostic
test of linearity of the underlying process under endogeneity, developing an estimation procedure
and a misspecification test for the STR model, presenting the results of Monte Carlo simulations to show
the usefulness of the model and estimation method, and providing an empirical application for inflation rate
targeting in Brazil. We show that STR models with endogenous variables can be specified and estimated
by a straightforward application of existing results in the literature
Tri-Bimaximal Mixing from Twisted Friedberg-Lee Symmetry
We investigate the Friedberg-Lee (FL) symmetry and its promotion to include
the symmetry, and call that the twisted FL symmetry.Based on the
twisted FL symmetry, two possible schemes are presented toward the realistic
neutrino mass spectrum and the tri-bimaximal mixing.In the first scheme, we
suggest the semi-uniform translation of the FL symmetry.The second one is based
on the permutation family symmetry.The breaking terms, which are twisted
FL symmetric, are introduced.Some viable models in each scheme are also
presented.Comment: 14 pages, no figure. v2: 16 pages, modified some sentences, appendix
added, references added. v3: 14 pages, composition simplified, accepted
version in EPJ
Realistic Equations of State for the Primeval Universe
Early universe equations of state including realistic interactions between
constituents are built up. Under certain reasonable assumptions, these
equations are able to generate an inflationary regime prior to the
nucleosynthesis period. The resulting accelerated expansion is intense enough
to solve the flatness and horizon problems. In the cases of curvature parameter
\kappa equal to 0 or +1, the model is able to avoid the initial singularity and
offers a natural explanation for why the universe is in expansion.Comment: 32 pages, 5 figures. Citations added in this version. Accepted EPJ
Effects of riparian vegetation on the structure of the marginal aquatic habitat and the associated fish assemblage in a tropical Brazilian reservoir
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