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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

Macroeconomic forecasting in times of crises

Guerrón-Quintana, Pablo A.; Zhong, Molin - 2017

We propose a parsimonious semiparametric method for macroeconomic forecasting during episodes of sudden changes. Based …

Persistent link: https://www.econbiz.de/10011708260

Generalized Additive Models for Pair-Copula Constructions

Vatter, Thibault - 2016

Pair-copula constructions are flexible models for the dependence in a random vector that have attracted a lot of interest in recent years. In this paper, we use generalized additive models to extend pair-copula constructions to allow for effects of covariates on the dependence parameters. We let...

Persistent link: https://www.econbiz.de/10012985827

Matrix Box-Cox models for multivariate realized volatility

Weigand, Roland - 2014

estimator and discuss bias-corrected point and density forecasting by simulation. The methods are applied to stock market data …

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

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

Penalized Averaging of Parametric and Non-Parametric Quantile Forecasts

De Gooijer, Jan G. - 2019

of macroeconomic predictors and technical indicators with the goal of forecasting the S&P 500 equity risk premium. To … illustrate the merit of the proposed approach, we extend the mean-based equity premium forecasting into the conditional quantile … systematic forecasting power. Third, different predictors are identified as important when considering lower, central and upper …

Persistent link: https://www.econbiz.de/10012859663

Nonparametric Identification and Estimation of the Extended Roy Model

Lee, Ji Hyung; Park, Byoung - 2022

We propose a new identification and estimation method for the extended Roy model, in which the agents maximize their utility rather than just their outcome. We nonparametrically identify the joint distribution of potential outcomes, which is of great importance particularly in treatment effect...

Persistent link: https://www.econbiz.de/10014243797

Capturing the zero: A new class of zero-augmented distributions and multiplicative error processes

Hautsch, Nikolaus; Malec, Peter; Schienle, Melanie - 2011

We propose a novel approach to model serially dependent positive-valued variables which realize a non-trivial proportion of zero outcomes. This is a typical phenomenon in financial time series observed at high frequencies, such as cumulated trading volumes. We introduce a flexible point-mass...

Persistent link: https://www.econbiz.de/10010308578

Nonparametric Kernel density estimation near the boundary

Malec, Peter; Schienle, Melanie - 2012

Standard fixed symmetric kernel type density estimators are known to encounter problems for positive random variables with a large probability mass close to zero. We show that in such settings, alternatives of asymmetric gamma kernel estimators are superior but also differ in asymptotic and...

Persistent link: https://www.econbiz.de/10010318760

A Structural Labour Supply Model with Nonparametric Preferences

van Soest, Arthur; Das, Marcel; Gong, Xiaodong - 2000

Nonparametric techniques are usually seen as a statistic device for data description and exploration, and not as a tool for estimating models with a richer economic structure, which are often required for policy analysis. This paper presents an example where nonparametric flexibility can be...

Persistent link: https://www.econbiz.de/10010262417