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Matrix Box-Cox models for multivariate realized volatility

Weigand, Roland - 2014

covariance matrix eigenvalues, while for the Box-Cox dynamic correlation (BC-DC) specification the variances are transformed …

Persistent link: https://www.econbiz.de/10010344500

Regularized Semiparametric Estimation of High Dimensional Dynamic Conditional Covariance Matrices

Morana, Claudio - 2019

This paper proposes a three-step estimation strategy for dynamic conditional correlation models. In the first step … usual normalization. In the third step, the two-step conditional covariance and correlation matrices are regularized by … model. This yields the final, third step smoothed estimate of the conditional covariance and correlation matrices. Due to …

Persistent link: https://www.econbiz.de/10012899132

Instrumental variables estimation of a generalized correlated random coefficients model

Masten, Matthew; Torgovitsky, Alexander - 2014

We study identification and estimation of the average treatment effect in a correlated random coefficients model that allows for first stage heterogeneity and binary instruments. The model also allows for multiple endogenous variables and interactions between endogenous variables and covariates....

Persistent link: https://www.econbiz.de/10010227690

Nonparametric Covariance Model

Yin, Jianxin - 2008

There has been considerable attention on estimation of conditional variance function in the literature. We propose here a nonparametric model for conditional covariance matrix. A kernel estimator is developed accordingly, its asymptotic bias and variance are derived, and its asymptotic normality...

Persistent link: https://www.econbiz.de/10012768311

Specification tests for non-Gaussian maximum likelihood estimators

Fiorentini, Gabriele; Sentana, Enrique - In: Quantitative economics : QE ; journal of the … 12 (2021) 3, pp. 683-742

We propose generalized DWH specification tests which simultaneously compare three or more likelihood-based estimators in multivariate conditionally heteroskedastic dynamic regression models. Our tests are useful for Garch models and in many empirically relevant macro and finance applications...

Persistent link: https://www.econbiz.de/10012598494

Empirical Pricing Kernel Estimation Using a Functional Gradient Descent Algorithm Based on Splines

Meier, Pirmin - 2012

We propose a new methodology to estimate the empirical pricing kernel implied from option data. In contrast to most of the studies in the literature that use an indirect approach, i.e. first estimating the physical and risk-neutral densities and obtaining the pricing kernel in a second step, we...

Persistent link: https://www.econbiz.de/10013108080

Machine-learning-based semiparametric time series conditional variance : estimation and forecasting

Dang, Justin; Ullah, Aman - In: Journal of risk and financial management : JRFM 15 (2022) 1, pp. 1-12

This paper proposes a new combined semiparametric estimator of the conditional variance that takes the product of a parametric estimator and a nonparametric estimator based on machine learning. A popular kernel-based machine learning algorithm, known as the kernel-regularized least squares...

Persistent link: https://www.econbiz.de/10012814196

Jump variation estimation with noisy high frequency financial data via wavelets

Zhang, Xin; Kim, Donggyu; Wang, Yazhen - In: Econometrics : open access journal 4 (2016) 3, pp. 1-26

This paper develops a method to improve the estimation of jump variation using high frequency data with the existence of market microstructure noises. Accurate estimation of jump variation is in high demand, as it is an important component of volatility in finance for portfolio allocation,...

Persistent link: https://www.econbiz.de/10011568279

Non-Parametric Estimation of Intraday Spot Volatility : Disentangling Instantaneous Trend and Seasonality

Vatter, Thibault - 2016

We provide a new framework for modeling trends and periodic patterns in high-frequency financial data. Seeking adaptivity to ever-changing market conditions, we enlarge the Fourier flexible form into a richer functional class: both our smooth trend and the seasonality are non-parametrically...

Persistent link: https://www.econbiz.de/10013007161

Empirical Risk Minimization for Time Series : Nonparametric Performance Bounds for Prediction

Brownlees, Christian T.; Llorens-Terrazas, Jordi - 2021

Empirical risk minimization is a standard principle for choosing algorithms in learning theory. In this paper we study the properties of empirical risk minimization for time series. The analysis is carried out in a general framework that covers different types of forecasting applications...

Persistent link: https://www.econbiz.de/10013216191