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The modeling of earnings per share of Polish companies for the post-financial crisis period using random walk and ARIMA models
Kuryłek, Wojciech - In: Journal of banking and financial economics 19 (2023) 1, pp. 26-43
The proper forecasting of listed companies' earnings is crucial for their appropriate pricing. This paper compares forecast errors of different univariate time-series models applied for the earnings per share (EPS) data for Polish companies from the period between the last financial crisis of...
Persistent link: https://www.econbiz.de/10014285928
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Quantile sieve estimates for time series
Franke, Jürgen; Stockis, Jean-Pierre; Tadjuidje, Joseph - 2007
We consider the problem of estimating the conditional quantile of a time series at time t given observations of the same and perhaps other time series available at time t - 1. We discuss sieve estimates which are a nonparametric versions of the Koenker-Bassett regression quantiles and do not...
Persistent link: https://www.econbiz.de/10010263674
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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
, fat tails and trading time, was developed as an alternative to the ARCH family models. Empirical analysis of the study …
Persistent link: https://www.econbiz.de/10011474619
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Forecasting the realized variance in the presence of intraday periodicity
Dumitru, Ana-Maria H.; Hizmeri, Rodrigo; Izzeldin, Marwan - 2019
This paper examines the impact of intraday periodicity on forecasting realized volatility using a heterogeneous autoregressive model (HAR) framework. We show that periodicity inflates the variance of the realized volatility and biases jump estimators. This combined effect adversely affects...
Persistent link: https://www.econbiz.de/10012063222
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Conditional variance forecasts for long-term stock returns
Mammen, Enno; Nielsen, Jens Perch; Scholz, Michael; … - In: Risks : open access journal 7 (2019) 4/113, pp. 1-22
In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, considering the short- and long-term interest rate, the earnings-by-price ratio, and the inflation rate. In particular, we apply in a two-step...
Persistent link: https://www.econbiz.de/10012127861
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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 of volatility in finance for portfolio allocation,...
Persistent link: https://www.econbiz.de/10011568279
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Fractal structure of the stock markets of leading asian countries
Günay, Samet - In: Journal of East Asian economic integration 18 (2014) 4, pp. 367-394
Index of Singapore. Empirical analysis was performed via non-parametric, semi-parametric long memory tests and also fractal … dimension calculations. In order to avoid spurious long memory features, besides the Detrended Fluctuations Analysis (DFA), we … DFA or modified GPH test. Fractal dimension analysis also demonstrated that all raw index prices have fractal structure …
Persistent link: https://www.econbiz.de/10011568388
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Multi-step non- and semi-parametric predictive regressions for short and long horizon stock return prediction
Cheng, Tingting; Gao, Jiti; Linton, Oliver - 2018
In this paper, we propose three new predictive models: the multi-step nonparametric predictive regression model and the multi-step additive predictive regression model, in which the predictive variables are locally stationary time series; and the multi-step time-varying coefficient predictive...
Persistent link: https://www.econbiz.de/10011775136
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Spurious seasonality detection : a non-parametric test proposal
Bariviera, Aurelio Fernández; Plastino, Angelo; Judge, … - In: Econometrics : open access journal 6 (2018) 1, pp. 1-15
symbolic analysis in economic time series studies. …
Persistent link: https://www.econbiz.de/10011822333
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Localized realized volatility modelling
Chen, Ying; Härdle, Wolfgang; Pigorsch, Uta - 2009
With the recent availability of high-frequency Financial data the long range dependence of volatility regained researchers' interest and has lead to the consideration of long memory models for realized volatility. The long range diagnosis of volatility, however, is usually stated for long sample...
Persistent link: https://www.econbiz.de/10003796151