Do mixed-data sampling models help forecast liquidity and volatility?

Barbara Będowska-Sójka; Agata Kliber

doi:10.5604/01.3001.0016.0363

Barbara Będowska-Sójka Poznań University of Economics and Business, Institute of Informatics and Quantitative Economics, Department of Econometrics, al. Niepodległości 10, 61-875 Poznań ORCID:https://orcid.org/0000-0001-5193-8304 , Agata Kliber Poznań University of Economics and Business, Institute of Informatics and Quantitative Economics, Department of Applied Mathematics, al. Niepodległości 10, 61-875 Poznań ORCID:https://orcid.org/0000-0003-1996-5550 Przegląd Statystyczny. Statistical Review, vol. 69, 2022, 2, pages: 1-19 Published online: 31 October 2022 DOI 10.5604/01.3001.0016.0363 Citation: Będowska-Sójka, B., & Kliber, A. (2022). Do mixed-data sampling models help forecast liquidity and volatility?. Przegląd Statystyczny. Statistical Review, 69(2), 1–19. https://doi.org/10.5604/01.3001.0016.0363.

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ARTICLE

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ABSTRACT

This paper aims to contribute to the existing studies on the Granger-causal relationship between volatility and liquidity in the stock market. We examine whether liquidity improves volatility forecasts and whether volatility allows the improvement of liquidity forecasts. The forecasts based on the mixed-data sampling models, MIDAS, are compared to those obtained from models based on daily data. Our results show that volatility and liquidity forecasts from MIDAS models outperform naive forecasts. On the other hand, the application of mixed-data sampling models does not significantly improve the performance of the forecasts of either liquidity or volatility based on a univariate autoregressive model or a vectorautoregressive one. We found that in terms of the forecasting ability, the VAR models and the AR models seem to perform equally well, as the differences in forecasting errors generated by these two types of models are not statistically significant.

KEYWORDS

liquidity, volatility, effective spread estimator, MIDAS

JEL

G12, G15

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