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Non-linearities and persistence in US long-run interest rates
Caporale, Guglielmo Maria; Gil-Alaña, Luis A.; … - 2020
fractional integration model suitable to capture both persistence and non-linearities, these being two important properties of …-August 2020, the other from the ECB reporting average monthly values over the period January 1900-August 2020. The estimation …
Persistent link: https://www.econbiz.de/10012383724
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Forecasting Stock Returns : Does Switching Between Models Help?
McMillan, David G. - 2009
Research examining the usefulness of non-linear models for stock market returns has almost reached an impasse. While there is general recognition of the superior ability of non-linear models to describe the data, there is less certainty about their ability to forecast the data. As such simple...
Persistent link: https://www.econbiz.de/10013158958
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Dividend Yields, Dividend Growth, and Return Predictability in the Cross-Section of Stocks
Maio, Paulo F. - 2015
There is a generalized conviction that variation in dividend yields is exclusively related to expected returns and not to expected dividend growth - e.g. Cochrane's presidential address (Cochrane (2011)). We show that this pattern, although valid for the aggregate stock market, is not true for...
Persistent link: https://www.econbiz.de/10013036406
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Forecasting under long memory
Hassler, Uwe; Pohle, Marc-Oliver - In: Journal of financial econometrics 21 (2023) 3, pp. 742-778
Persistent link: https://www.econbiz.de/10014314818
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Stock Return Predictability and Variance Risk Premia : Statistical Inference and International Evidence
Bollerslev, Tim - 2012
Recent empirical evidence suggests that the variance risk premium predicts aggregate stock market returns. We demonstrate that statistical finite sample biases cannot “explain” this apparent predictability. Further corroborating the existing evidence of the U.S., we show that country...
Persistent link: https://www.econbiz.de/10013115149
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Stock Return Predictability and Variance Risk Premia : Statistical Inference and International Evidence
Bollerslev, Tim - 2012
Recent empirical evidence suggests that the variance risk premium predicts aggregate stock market returns. We demonstrate that statistical finite sample biases cannot “explain” this apparent predictability. Further corroborating the existing evidence of the U.S., we show that country...
Persistent link: https://www.econbiz.de/10013109053
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Performance of the multifractal model of asset returns (MMAR) : evidence from emerging stock markets
Günay, Samet - In: International Journal of Financial Studies : open … 4 (2016) 2, pp. 1-17
In this study, the performance of the Multifractal Model of Asset Returns (MMAR) was examined for stock index returns of four emerging markets. The MMAR, which takes into account stylized facts of financial time series, such as long memory, fat tails and trading time, was developed as an...
Persistent link: https://www.econbiz.de/10011474619
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Volatility Estimation and Jump Testing via Realized Information Variation
Liu, Weiyi - 2016
. We also extend our results to integrated quarticity and higher-order variation estimation, and then propose a new jump …, comparing with alternative methods. The simulations support our theoretical results on volatility estimation and demonstrate …
Persistent link: https://www.econbiz.de/10012986881
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Long Memory in Stock Returns : An Analysis Using a Wavelet Based Semi-Parametric Estimator
Bhandari, Avishek - 2018
The estimation and the analysis of long memory parameters have mainly focused on the analysis of long-range dependence … memory requires major revisions in the standard estimation procedures without which the estimated results can be seriously …
Persistent link: https://www.econbiz.de/10012920334
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Jump variation estimation with noisy high frequency financial data via wavelets
Zhang, Xin; Kim, Donggyu; Wang, Yazhen - In: Econometrics : open access journal 4 (2016) 3, pp. 1-26
This paper develops a method to improve the estimation of jump variation using high frequency data with the existence … of market microstructure noises. Accurate estimation of jump variation is in high demand, as it is an important component …-step procedure with detection and estimation. In Step 1, we detect the jump locations by performing wavelet transformation on the …
Persistent link: https://www.econbiz.de/10011568279