Extracting the global stochastic trend from non-synchronous data on the volatility of financial indices

Polina Pogorelova () and Anatoly Peresetsky
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Polina Pogorelova: National Research University Higher School of Economics (NRU HSE), Moscow, Russian Federation;
Authors registered in the RePEc Author Service: Полина Вячеславовна Погорелова

Applied Econometrics, 2020, vol. 57, 53-71

Abstract: In this paper, the Kalman linear filter method is used to decompose non-synchronous observations of the realized volatility of financial indices (NIKKEI 225, FTSE 100, S&P 500) into unobservable global and local components. It is shown that the volatility of the New York S&P 500 index is a global component, while the Tokyo NIKKEI 225 index, on the contrary, is more sensible to the local news. It is shown that the largest contribution to the global component comes from the observation interval from the closing of the London Exchange to the closing of the exchange in New York (16:30 and 21:00 UTC, respectively). Starting from about 2012–2014, the contribution to the volatility of the global news market is growing from the interval from closing the exchange in New York to closing the exchange in Tokyo (from 21:00 to 6:00 UTC). This can be attributed to the recently increasing influence of the economies of Asian countries (China, Japan, Korea) on the world economy.

Keywords: global stochastic trend; Kalman filter; realized volatility; non-synchronous data; financial markets. (search for similar items in EconPapers)
JEL-codes: C49 C58 F36 F65 G15 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:ris:apltrx:0387

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