scholarly journals Selecting between causal and noncausal models with quantile autoregressions

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Alain Hecq ◽  
Li Sun
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Latin American ◽  
Financial Time Series ◽  
Selection Criterion ◽  
Likelihood Methods ◽  
Approximate Maximum Likelihood ◽  
Financial Time ◽  
Maximum Likelihood Methods ◽  
Latin American Countries

AbstractWe propose a model selection criterion to detect purely causal from purely noncausal models in the framework of quantile autoregressions (QAR). We also present asymptotics for the i.i.d. case with regularly varying distributed innovations in QAR. This new modelling perspective is appealing for investigating the presence of bubbles in economic and financial time series, and is an alternative to approximate maximum likelihood methods. We illustrate our analysis using hyperinflation episodes of Latin American countries.

scholarly journals Financial time series volatility analysis using Gaussian process state-space models

2021 ◽  
Author(s):  
Jianan Han
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
State Space ◽  
Financial Time Series ◽  
State Space Model ◽  
Likelihood Methods ◽  
Nonparametric Modeling ◽  
Space Model ◽  
Financial Time ◽  
Maximum Likelihood Methods

In this thesis, we propose a novel nonparametric modeling framework for financial time series data analysis, and we apply the framework to the problem of time varying volatility modeling. Existing parametric models have a rigid transition function form and they often have over-fitting problems when model parameters are estimated using maximum likelihood methods. These drawbacks effect the models' forecast performance. To solve this problem, we take Bayesian nonparametric modeling approach. By adding Gaussian process prior to the hidden state transition process, we extend the standard state-space model to a Gaussian process state-space model. We introduce our Gaussian process regression stochastic volatility (GPRSV) model. Instead of using maximum likelihood methods, we use Monte Carlo inference algorithms. Both online particle filter and offline particle Markov chain Monte Carlo methods are studied to learn the proposed model. We demonstrate our model and inference methods with both simulated and empirical financial data.

scholarly journals Financial time series volatility analysis using Gaussian process state-space models

2021 ◽  
Author(s):  
Jianan Han
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
State Space ◽  
Financial Time Series ◽  
State Space Model ◽  
Likelihood Methods ◽  
Nonparametric Modeling ◽  
Space Model ◽  
Financial Time ◽  
Maximum Likelihood Methods

In this thesis, we propose a novel nonparametric modeling framework for financial time series data analysis, and we apply the framework to the problem of time varying volatility modeling. Existing parametric models have a rigid transition function form and they often have over-fitting problems when model parameters are estimated using maximum likelihood methods. These drawbacks effect the models' forecast performance. To solve this problem, we take Bayesian nonparametric modeling approach. By adding Gaussian process prior to the hidden state transition process, we extend the standard state-space model to a Gaussian process state-space model. We introduce our Gaussian process regression stochastic volatility (GPRSV) model. Instead of using maximum likelihood methods, we use Monte Carlo inference algorithms. Both online particle filter and offline particle Markov chain Monte Carlo methods are studied to learn the proposed model. We demonstrate our model and inference methods with both simulated and empirical financial data.

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