Estimation of realized stochastic volatility models using Hamiltonian Monte Carlo-Based methods

Nugroho, Didit Budi; Morimoto, Takayuki

doi:10.1007/s00180-014-0546-6

Cited by 13 publications

(9 citation statements)

References 21 publications

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“…Because the likelihood function of the SV model is difficult to directly evaluate, many procedures have been proposed. In this study, authors apply a procedure from the Riemann Manifold Hamiltonian Monte Carlo (RMHMC) method within the Markov Chain Monte Carlo (MCMC), based on the paper by Nugroho and Morimoto (2015). T he RMHMC method is a tool which can efficiently estimate the entire log volatility at once.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Stochastic Volatility to Validate IDR Anchor Currency

Nugroho

Mahatma

Pratomo³

2018

GADJAH MADA INT. J. BUS.

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This study aims to assess the performance of stochastic volatility models for their estimation of foreign exchange rate returns' volatility using daily data from Bank Indonesia (BI). The model is then applied to validate the anchor currency of Indonesian rupiah (IDR). Two stylized facts are incorporated into the models: A correlation between the previous returns and their conditional variance, and return errors following four different error distributions namely Normal, Student-t, non-central Student-t, and generalized hyperbolic skew Student-t. The analysis is based on the application of daily returns data from nine foreign currency selling rates to IDR from 2010 to 2015, including the AUD, CHF, CNY, EUR, GBP, JPY, MYR, SGD, and USD. The main results are: (1) Mixed evidence of positive and negative relationships between the return and its variance were found, especially significant correlations being found for the IDR/AUD, IDR/CHF, IDR/JPY, IDR/SGD, and IDR/USD returns series; (2) the model with the generalized hyperbolic skew Student's t-distribution specification for the returns error provides the best performance; and (3) anchoring the IDR to established hard currencies is more appropriate than anchoring it to other currencies.

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Section: Introductionmentioning

confidence: 99%

Modeling of Stochastic Volatility to Validate IDR Anchor Currency

Nugroho

Mahatma

Pratomo³

2018

GADJAH MADA INT. J. BUS.

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“…Since these models often give rise to posterior distributions with high correlations the methods proposed can be particularly useful for estimation. More recently, Nugroho and Morimoto (2014) presented an algorithm based on Hamiltonian Monte Carlo methods for the estimation of realized stochastic volatility models.…”

Section: Introductionmentioning

confidence: 99%

Riemann manifold Langevin methods on stochastic volatility estimation

Zevallos

Gasco

Ehlers

2017

Communications in Statistics - Simulation and Computation

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“…One contribution of this article is to apply the Hamiltonian Monte Carlo (HMC) and Riemann Manifold HMC (RMHMC) methods within the Markov chain Monte Carlo (MCMC) algorithm to update the log-volatilities and parameters of the SVM model, respectively. Our MCMC simulation employs a HMC algorithm (Duane et al, 1987;Neal, 2011) for updating the log-volatilities at once and RMHC (Girolami and Calderhead, 2011;Nugroho and Morimoto, 2015) for parameters from the mean and volatility equations at once in two blocks. Time-varying volatility for financial variables of developed economies have been studied extensively (Liesenfeld and Jung, 2000;Jacquier et al, 2004;Abanto-Valle et al, 2010); however, empirical studies of the volatility characteristics of the financial markets in Latin America are very scarce and are far from being thoroughly analyzed despite their growth in recent years (Abanto-Valle et al, 2011;Rodríguez, 2017;Lengua Lafosse and Rodríguez, 2018).…”

Section: Introductionmentioning

confidence: 99%

Stochastic Volatility in Mean. Empirical Evidence from Stock Latin American Markets

Abanto-Valle¹,

Rodríguez²,

Garrafa-Aragón³

2020

Documento De Trabajo

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Utilizando un modelo de volatilidad estocástica en la media (SVM), realizamos un estudio empírico de los índices latinoamericanos en vivo para ver el impacto de la volatilidad en la media de los retornos. Utilizamos la dinámica hamiltoniana de MCMC. Los resultados indican que la volatilidad tiene un impacto negativo en los rendimientos, lo que sugiere que el efecto de retroalimentación de la volatilidad es más fuerte que el efecto relacionado con la volatilidad esperada. Este resultado es claro y contrario al hallazgo de Koopman y Uspensky (2002). Los demás países presentan valores negativos pero la cola superior de los intervalos se acerca al valor cero.

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Estimation of realized stochastic volatility models using Hamiltonian Monte Carlo-Based methods

Cited by 13 publications

References 21 publications

Modeling of Stochastic Volatility to Validate IDR Anchor Currency

Modeling of Stochastic Volatility to Validate IDR Anchor Currency

Riemann manifold Langevin methods on stochastic volatility estimation

Stochastic Volatility in Mean. Empirical Evidence from Stock Latin American Markets

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