Real time detection of structural breaks in GARCH models

He, Zhongfang; Maheu, John M.

doi:10.1016/j.csda.2009.09.038

Cited by 41 publications

(39 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to that, we found relevant to compare our results with the algorithm of [35] which allows for an online detection of the breaks in the GARCH parameters. A proper comparison of the two approaches is detailed in Appendix C.…”

Section: Mean Parametermentioning

confidence: 99%

“…For the sake of completeness, the next section briefly reviews the method of [35] and discusses the important differences with the TNT sampler. Eventually, in Section C.2, we apply the algorithm on the two financial time series of the paper to empirically compare the two approaches.…”

Section: Comparison With the Online Smc Algorithmmentioning

confidence: 99%

“…He and Maheu [35] provide an online algorithm able to detect structural breaks on the fly in a process which exhibits the path dependence issue. The paper strongly relies on [39] which develop an auxiliary particle filter (hereafter APF, see [40]) to estimate the static parameters.…”

Section: Comparison With the Online Smc Algorithmmentioning

confidence: 99%

“…For notational convenience, we gather all the model parameters into the set θ = {µ 1 , ω 1 , α 1 , β 1 , p 1 , ..., µ K , ω K , α K , β K , p K } and their associated mapping intoθ. The key idea of [35] consists in including the lagged conditional variances into the set of the SMC particles (and the previous error terms as we have included mean parameters {µ i } K i=1 in the CP-GARCH specification). Let define the filtration F t−1 = {θ i t−1 ,…”

Section: C1 the Apf Algorithm For Cp-garch Modelsmentioning

confidence: 99%

“…To solve the problem, [35] use an APF algorithm (see [40]) in combination with an artificial sequence for the static parameters (see [39]). The static parameters are assumed to evolve over time according to a shrinkage random walk process.…”

Section: C1 the Apf Algorithm For Cp-garch Modelsmentioning

confidence: 99%

See 4 more Smart Citations

Evolutionary Sequential Monte Carlo Samplers for Change-Point Models

Dufays

2016

Econometrics

View full text Add to dashboard Cite

Sequential Monte Carlo (SMC) methods are widely used for non-linear filtering purposes. However, the SMC scope encompasses wider applications such as estimating static model parameters so much that it is becoming a serious alternative to Markov-Chain Monte-Carlo (MCMC) methods. Not only do SMC algorithms draw posterior distributions of static or dynamic parameters but additionally they provide an estimate of the marginal likelihood. The tempered and time (TNT) algorithm, developed in this paper, combines (off-line) tempered SMC inference with on-line SMC inference for drawing realizations from many sequential posterior distributions without experiencing a particle degeneracy problem. Furthermore, it introduces a new MCMC rejuvenation step that is generic, automated and well-suited for multi-modal distributions. As this update relies on the wide heuristic optimization literature, numerous extensions are readily available. The algorithm is notably appropriate for estimating change-point models. As an example, we compare several change-point GARCH models through their marginal log-likelihoods over time.

show abstract

Section: Mean Parametermentioning

confidence: 99%

Section: Comparison With the Online Smc Algorithmmentioning

confidence: 99%

Section: Comparison With the Online Smc Algorithmmentioning

confidence: 99%

Section: C1 the Apf Algorithm For Cp-garch Modelsmentioning

confidence: 99%

Section: C1 the Apf Algorithm For Cp-garch Modelsmentioning

confidence: 99%

See 3 more Smart Citations

Evolutionary Sequential Monte Carlo Samplers for Change-Point Models

Dufays

2016

Econometrics

View full text Add to dashboard Cite

show abstract

Bibliography

2012

Handbook of Volatility Models and Their Applications

View full text Add to dashboard Cite

A Kalman particle filter for online parameter estimation with applications to affine models

Khedher

Spreij

2021

Stat Inference Stoch Process

View full text Add to dashboard Cite

In this paper we address the problem of estimating the posterior distribution of the static parameters of a continuous-time state space model with discrete-time observations by an algorithm that combines the Kalman filter and a particle filter. The proposed algorithm is semi-recursive and has a two layer structure, in which the outer layer provides the estimation of the posterior distribution of the unknown parameters and the inner layer provides the estimation of the posterior distribution of the state variables. This algorithm has a similar structure as the so-called recursive nested particle filter, but unlike the latter filter, in which both layers use a particle filter, our algorithm introduces a dynamic kernel to sample the parameter particles in the outer layer to obtain a higher convergence speed. Moreover, this algorithm also implements the Kalman filter in the inner layer to reduce the computational time. This algorithm can also be used to estimate the parameters that suddenly change value. We prove that, for a state space model with a certain structure, the estimated posterior distribution of the unknown parameters and the state variables converge to the actual distribution in $$L^p$$ L p with rate of order $${\mathcal {O}}(N^{-\frac{1}{2}}+\varDelta ^{\frac{1}{2}})$$ O ( N - 1 2 + Δ 1 2 ) , where N is the number of particles for the parameters in the outer layer and $$\varDelta $$ Δ is the maximum time step between two consecutive observations. We present numerical results of the implementation of this algorithm, in particularly we implement this algorithm for affine interest models, possibly with stochastic volatility, although the algorithm can be applied to a much broader class of models.

show abstract

Real time detection of structural breaks in GARCH models

Cited by 41 publications

References 34 publications

Evolutionary Sequential Monte Carlo Samplers for Change-Point Models

Evolutionary Sequential Monte Carlo Samplers for Change-Point Models

Bibliography

A Kalman particle filter for online parameter estimation with applications to affine models

Contact Info

Product

Resources

About