Nonparametric conditional variance and error density estimation in regression models with dependent errors and predictors [PDF]
, 2011 : This paper considers nonparametric regression models with long memory errors and predictors. Unlike in weak dependence situations, we show that the estimation of the conditional mean has influence on the estimation of both, the conditional variance and
Rafal Kulik, Cornelia Wichelhaus
semanticscholar +2 more sources
Modelling and forecasting of growth rate of new COVID-19 cases in top nine affected countries: Considering conditional variance and asymmetric effect. [PDF]
Chaos Solitons Fractals, 2021 Ekinci A.
europepmc +2 more sources
Direction-of-Change Forecasts Based on Conditional Variance, Skewness and Kurtosis Dynamics: International Evidence [PDF]
, 2006 Recent theoretical work has revealed a direct connection between asset return volatility forecastability and asset return sign forecastability. This suggests that the pervasive volatility forecastability in equity returns could, via induced sign ...
Peter F. Christoffersen +4 more
semanticscholar +2 more sources
Estimation of the Conditional Variance - Covariance Matrix of Returns Using the Intraday Range [PDF]
, 2007 This paper proposes a hybrid multivariate exponentially weighted moving average (EWMA) estimator of the variance-covariance matrix of returns. The proposed estimator employs a range-based EWMA specification to estimate the conditional variances of ...
Richard D. F. Harris, Fatih Yılmaz
semanticscholar +2 more sources
Changes in the Unconditional Variance and Autoregressive Conditional Heteroscedasticity
International Journal of Economics and Financial Issues, 2016 This paper argues that a simple white noise process with one jump in its unconditional variance may give rise to the presence of ARCH effects, and, surprisingly, this may occur in determinate circumstances even when the jump is very brief.
Amado Peiró
doaj +8 more sources
On the estimation of a monotone conditional variance in nonparametric regression [PDF]
Annals of the Institute of Statistical Mathematics, 2009 A monotone estimate of the conditional variance function in a heteroscedastic, nonparametric regression model is proposed. The method is based on the application of a kernel density estimate to an unconstrained estimate of the variance function and yields an estimate of the inverse variance function. The final monotone estimate of the variance function
H. Dette, Kay F. Pilz
semanticscholar +6 more sources
An analysis of conditional mean-variance portfolio performance using hierarchical clustering
Journal of Finance and Data Science, 2023 This paper studies portfolio optimization through improvements of ex-ante conditional covariance estimates. We use the cross-section of stock returns over a 52-year sample to analyze trading performance by implementing the machine learning algorithm of ...
Stephen R. Owen
doaj +2 more sources
A characterization using the conditional variance
Metrika, 1981 A class of probability distributions is characterized assuming that the conditional variance of a functionh (X), givenX>x, is constant.
A. Dallas
semanticscholar +4 more sources
Dependence Measuring from Conditional Variances
Dependence Modeling, 2015 Abstract A conditional variance is an indicator of the level of independence between two random variables. We exploit this intuitive relationship and define a measure v which is almost a measure of mutual complete dependence. Unsurprisingly, the measure attains its minimum value for many pairs of non-independent ran- dom variables ...
Noppadon Kamnitui +2 more
openalex +5 more sources
Models with multiplicative decomposition of conditional variances and correlations [PDF]
, 2019 Univariate and multivariate GARCH type models with multiplicative decomposition of the variance to short and long run components are surveyed. The latter component can be either deterministic or stochastic. Examples of both types are studied.
Cristina Amado +2 more
openalex +8 more sources