Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/239213 
Year of Publication: 
2020
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 13 [Issue:] 6 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-19
Publisher: 
MDPI, Basel
Abstract: 
We studied (i) the volatility feedback effect, defined as the relationship between contemporaneous returns and the market-based volatility, and (ii) the leverage effect, defined as the relationship between lagged returns and the current market-based volatility. For our analysis, we used daily measures of volatility estimated from high frequency data to explain volatility changes over time for both the S&P500 and FTSE100 indices. The period of analysis spanned from January 2000 to June 2017 incorporating various market phases, such as booms and crashes. Based on the estimated regressions, we found evidence that the returns of S&P500 and FTSE100 indices were well explained by a specific group of realized measure estimators, and the returns negatively affected realized volatility. These results are highly recommended to financial analysts dealing with high frequency data and volatility modelling.
Subjects: 
volatility
realized measures
high frequency data
statistical properties
FTSE100
S& P500
JEL: 
F65
G11
G12
G24
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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