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An infinite hidden Markov model with stochastic volatility

Li, Chenxing; Maheu, John M.; Yang, Qiao - In: Journal of forecasting 43 (2024) 6, pp. 2187-2211

Persistent link: https://www.econbiz.de/10015110400

Estimation of stochastic volatility models by nonparametric filtering

Kanaya, Shin; Kristensen, Dennis - 2015

estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle both jumps …

Persistent link: https://www.econbiz.de/10010487528

Renewal Based Volatility Estimation

Li, Yifan; Nolte, Ingmar; Nolte (Lechner), Sandra - 2019

This paper develops the idea of renewal time sampling, a novel sampling scheme constructed from stopping times of semimartingales. Based on this new sampling scheme we propose a class of volatility estimators named renewal based volatility estimators. In this paper we show that: (1) The spot...

Persistent link: https://www.econbiz.de/10014116287

Sensitivity of University Rankings : Implications of Stochastic Dominance Efficiency Analysis

Pinar, Mehmet; Milla, Joniada; Stengos, Thanasēs - 2018

To create their rankings, university-ranking agencies usually combine multiple performance measures into a composite index. However, both rankings and index scores are sensitive to the weights assigned to performance measures. This paper uses a stochastic dominance efficiency methodology to...

Persistent link: https://www.econbiz.de/10014112285

Markov Chain Monte Carlo Models, Gibbs Sampling, & Metropolis Algorithm for High-Dimensionality Complex Stochastic Problems

Malhotra, Yogesh - 2015

Markov chain Monte Carlo (MCMC) methods have an important role in solving high dimensionality stochastic problems characterized by computational complexity. Given their critical importance, there is need for network and security risk management research to relate the MCMC quantitative...

Persistent link: https://www.econbiz.de/10013029835

Neural Network Embedding of the Over-Dispersed Poisson Reserving Model

Gabrielli, Andrea - 2018

The main idea of this paper is to embed a classical actuarial regression model into a neural network architecture. This nesting allows us to learn model structure beyond the classical actuarial regression model if we use as starting point of the neural network calibration exactly the classical...

Persistent link: https://www.econbiz.de/10012907645

Mixed Semimartingales : Volatility Estimation in the Presence of Rough Noise

Chong, Carsten; Delerue, Thomas; Li, Guoying - 2021

We consider the problem of estimating volatility based on high-frequency data when the observed price process is a continuous Itô semimartingale contaminated by microstructure noise. Assuming that the noise process is compatible across different sampling frequencies, we argue that it typically...

Persistent link: https://www.econbiz.de/10013220217

Efficient Estimation of a Multivariate Multiplicative Volatility Model

Hafner, Christian M. - 2010

nonparametric long run component and a unit multivariate GARCH short run dynamic component. We suggest various kernel …-based estimation procedures for the parametric and nonparametric components, and derive the asymptotic properties thereof. For the … parametric part of the model, we obtain the semiparametric efficiency bound. Our method is applied to a bivariate stock index …

Persistent link: https://www.econbiz.de/10013148178

Limit Theorems for Functionals of Higher Order Differences of Brownian Semi-Stationary Processes

Barndorff-Nielsen, Ole E. - 2010

We present some new asymptotic results for functionals of higher order differences of Brownian semi-stationary processes. In an earlier work we have derived a similar asymptotic theory for first order differences. However, the central limit theorems were valid only for certain values of the...

Persistent link: https://www.econbiz.de/10013149605

Nonparametric Specification Testing in Nonlinear and Nonstationary Time Series Models : Theory and Practice

Chen, Jia - 2013

propose using a nonparametric kernel test for specifying whether the regression function is of a known parametric nonlinear … form. The power function of the proposed nonparametric test is systematically studied and an asymptotic distribution of the … general semiparametric local alternatives. The asymptotic theory developed in this paper differs from existing work on …

Persistent link: https://www.econbiz.de/10013084965