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SV mixture : classification using EM algorithm

Hachicha, Ahmed; Hachicha, Fatma; Masmoudi, Afif - 2013

with MCMC algorithm” cited, Rev Quant Finan Acc (2012) 38:479-493 DOI 10.1007/s11156-011-0236-1 where we propose initially … estimate the parameter of a mixture stochastic volatility model, we first use the Expectation-Maximisation (EM) algorithm. The …

Persistent link: https://www.econbiz.de/10009755511

Particle filters for Markov switching stochastic volatility models

Yun, Bao; Chiarella, Carl; Kang, Boda - 2012

Persistent link: https://www.econbiz.de/10009564477

Optimal portfolio selection with volatility information for a high frequency rebalancing algorithm

Bağcı, Mahmut; Soylu, Pınar Kaya - In: Financial innovation : FIN 10 (2024), pp. 1-28

We propose a high-frequency rebalancing algorithm (HFRA) and compare its performance with periodic rebalancing (PR) and …

Persistent link: https://www.econbiz.de/10014541693

On forecasting realized volatility for bitcoin based on deep learning PSO-GRU model

Tang, Xiaolong; Song, Yuping; Jiao, Xingrui; Sun, Yankun - In: Computational economics 63 (2024) 5, pp. 2011-2033

Persistent link: https://www.econbiz.de/10014550858

Intraday Volatility : An Integer Autoregressive Model

Tapiero, Oren - 2015

Intraday and high frequency time series are mostly defined by a non-continuous prices process. This paper introduces an integer based ARMA model found to be a better predictor for absolute intraday price changes than continuous time estimators (such as GARCH or multiplicative error models)....

Persistent link: https://www.econbiz.de/10013016057