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Financial Time Series: Methods and Models

1
Dipartimento di Scienze Statistiche, Università di Padova, 35121 Padova, Italy
2
Dipartimento di Scienze Economiche e Statistiche, Università di Salerno, 84084 Fisciano (SA), Italy
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2020, 13(5), 86; https://doi.org/10.3390/jrfm13050086
Received: 24 April 2020 / Accepted: 24 April 2020 / Published: 28 April 2020
(This article belongs to the Special Issue Financial Time Series: Methods & Models)
The statistical analysis of financial time series is a rich and diversified research field whose inherent complexity requires an interdisciplinary approach, gathering together several disciplines, such as statistics, economics, and computational sciences. This special issue of the Journal of Risk and Financial Management on “Financial Time Series: Methods & Models” contributes to the evolution of research on the analysis of financial time series by presenting a diversified collection of scientific contributions exploring different lines of research within this field. View Full-Text
Keywords: financial time series; GARCH models; capital markets; emerging markets; realized volatility; dynamic conditional correlation models; cointegration; model-based clustering; structural breaks; market efficiency; misery index financial time series; GARCH models; capital markets; emerging markets; realized volatility; dynamic conditional correlation models; cointegration; model-based clustering; structural breaks; market efficiency; misery index
MDPI and ACS Style

Caporin, M.; Storti, G. Financial Time Series: Methods and Models. J. Risk Financial Manag. 2020, 13, 86.

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