07350015%2E2012%2E663261
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This article investigates model characteristics that are consistent with variation in the shape of return distributions using a stochastic volatility model with a
The new model is applied to the daily returns of the S&P 500, FTSE 100, and EUROSTOXX 50 indices and is compared with GARCH, stochastic volatility, and other Bayesian
This article adopts the idea of principal component analysis (PCA) to model multivariate volatility, and the principal volatil- ity component (PVC) analysis is then proposed to
In this article, we proposed a PT estimator of integrated volatility in the simultaneous presence of microstructure noise and jumps. The method is based on two steps, namely, the
The empirical results with two realized volatility measures and daily returns for five stock indices show the feasibility of the realized BCSV model and demonstrate that the
This article proposes a novel stochastic volatility (SV) model that draws from the existing literature on autoregressive SV models, aggregation of autoregressive processes, and
(2022) explored the dynamic behavior based on this mechanistic model, here we would like to extend the process identification via the Auto Regressive eXogenous
The execution of the SutteARIMA predictive model used in this analysis was compared with the established ARIMA, Neural Network Auto-Regressive NNAR, and Holt-Winters models, which have
