Abstract
This paper incorporates conditional heteroskedasticity properties in the long-memory model and applies the model on squared returns of BRICS (Brazil, Russia, India, China, and South Africa) and the United States equity markets to capture the volatility of the stock return. The conditional first- and second-order moments are provided. The CLS, FGLS, and QML are discussed and 2SQML estimator is proposed employing a nonstationary mean function. The simulation study suggests that the proposed 2SQML estimator performs better than the other three estimators. Both in simulation and empirical studies, we find that the proposed model FIMACH outperforms FIGARCH in terms of eliminating serial correlations.
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Notes
- 1.
This is the reaction to macroeconomic news/rumors in the (ujt) sequence, we use the mean lag \( \sum\nolimits_{i = 0}^{{q_{j} }} {i\alpha_{ji} /w} \), where \( w = \sum\nolimits_{i = 0}^{{q_{j} }} {\alpha_{ji} } \) and \( \alpha_{j0} = 1 \) (see Quoreshi 2012).
- 2.
Data source: Datastream.
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Quoreshi, A.M.M.S., Mollah, S. (2019). Conditional Heteroskedasticity in Long-Memory Model “FIMACH” for Return Volatilities in Equity Markets. In: Valenzuela, O., Rojas, F., Pomares, H., Rojas, I. (eds) Theory and Applications of Time Series Analysis. ITISE 2018. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-26036-1_11
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