• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.713-722
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• 2020

Contrasted with the standard symmetric GARCH models, we consider a broad class of threshold-asymmetric models to analyse financial time series exhibiting asymmetric volatility. By further introducing power transformations, we add more flexibilities to the asymmetric class, thereby leading to power transformed and asymmetric volatility models. In particular, the paper is concerned with the nonstationary volatilities in which conditions for integrated volatility and explosive volatility are separately discussed. Dow Jones Industrial Average is analysed for illustration.

• Communications for Statistical Applications and Methods
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• v.20 no.3
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• pp.207-215
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• 2013

In this paper, we implement an empirical study to test whether the time series of daily returns in stock and Won/USD exchange markets is strictly stationary or explosive. The results indicate that only a few series show nonstationary volatility when dramatic events erupted; in addition, this nonstationary behavior occurs more often in the Won/USD exchange market than in the stock market.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.49-62
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• 2022

Realized volatility is well known to have long memory, strong association with other global financial markets and interdependences among macroeconomic indices such as exchange rate, oil price and interest rates. This paper proposes autoencoder factor-augmented heterogeneous autoregressive (AE-FAHAR) model for realized volatility forecasting. AE-FAHAR incorporates long memory using HAR structure, and exogenous variables into few factors summarized by autoencoder. Autoencoder requires intensive calculation due to its nonlinear structure, however, it is more suitable to summarize complex, possibly nonstationary high-dimensional time series. Our AE-FAHAR model is shown to have smaller out-of-sample forecasting error in empirical analysis. We also discuss pre-training, ensemble in autoencoder to reduce computational cost and estimation errors.