Out of sample forecasts of quadratic variation

Yacine Aït-Sahalia; Loriano Mancini

doi:10.1016/j.jeconom.2008.09.015

Out of sample forecasts of quadratic variation

Yacine Aït-Sahalia
, Loriano Mancini

Research output: Contribution to journal › Article › peer-review

98 Scopus citations

Abstract

We compare the forecasts of Quadratic Variation given by the Realized Volatility (RV) and the Two Scales Realized Volatility (TSRV) computed from high frequency data in the presence of market microstructure noise, under several different dynamics for the volatility process and assumptions on the noise. We show that TSRV largely outperforms RV, whether looking at bias, variance, RMSE or out-of-sample forecasting ability. An empirical application to all DJIA stocks confirms the simulation results.

Original language	English (US)
Pages (from-to)	17-33
Number of pages	17
Journal	Journal of Econometrics
Volume	147
Issue number	1
DOIs	https://doi.org/10.1016/j.jeconom.2008.09.015
State	Published - Nov 2008

All Science Journal Classification (ASJC) codes

Economics and Econometrics

Keywords

High frequency data
Market microstructure noise
Measurement error
Out of sample forecasts
Realized volatility
Two scales realized volatility

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10.1016/j.jeconom.2008.09.015

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@article{24e995f3d80241fa84708586925bbf46,

title = "Out of sample forecasts of quadratic variation",

abstract = "We compare the forecasts of Quadratic Variation given by the Realized Volatility (RV) and the Two Scales Realized Volatility (TSRV) computed from high frequency data in the presence of market microstructure noise, under several different dynamics for the volatility process and assumptions on the noise. We show that TSRV largely outperforms RV, whether looking at bias, variance, RMSE or out-of-sample forecasting ability. An empirical application to all DJIA stocks confirms the simulation results.",

keywords = "High frequency data, Market microstructure noise, Measurement error, Out of sample forecasts, Realized volatility, Two scales realized volatility",

author = "Yacine A{\"i}t-Sahalia and Loriano Mancini",

note = "Funding Information: Financial support from the NSF under grants SES-0350772 and DMS-0532370 (A{\"i}t-Sahalia) and from the University Research Priority Program “Finance and Financial Markets” University of Zurich and the NCCR-FinRisk Swiss National Science Foundation (Mancini) is gratefully acknowledged. For helpful comments, we thank two anonymous referees, Patrick Cheridito, Fulvio Corsi and seminar participants at the conferences “Risk Measures \& Risk Management for High-Frequency Data”, EURANDOM, 2006, “Microstructure of Financial and Money Markets”, CREST, 2006, and the 33rd Annual Meeting of the European Finance Association, EFA, 2006. This research was undertaken while Mancini visited the Department of Operations Research and Financial Engineering at Princeton University. ",

year = "2008",

month = nov,

doi = "10.1016/j.jeconom.2008.09.015",

language = "English (US)",

volume = "147",

pages = "17--33",

journal = "Journal of Econometrics",

issn = "0304-4076",

publisher = "Elsevier B.V.",

number = "1",

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TY - JOUR

T1 - Out of sample forecasts of quadratic variation

AU - Aït-Sahalia, Yacine

AU - Mancini, Loriano

N1 - Funding Information: Financial support from the NSF under grants SES-0350772 and DMS-0532370 (Aït-Sahalia) and from the University Research Priority Program “Finance and Financial Markets” University of Zurich and the NCCR-FinRisk Swiss National Science Foundation (Mancini) is gratefully acknowledged. For helpful comments, we thank two anonymous referees, Patrick Cheridito, Fulvio Corsi and seminar participants at the conferences “Risk Measures & Risk Management for High-Frequency Data”, EURANDOM, 2006, “Microstructure of Financial and Money Markets”, CREST, 2006, and the 33rd Annual Meeting of the European Finance Association, EFA, 2006. This research was undertaken while Mancini visited the Department of Operations Research and Financial Engineering at Princeton University.

PY - 2008/11

Y1 - 2008/11

N2 - We compare the forecasts of Quadratic Variation given by the Realized Volatility (RV) and the Two Scales Realized Volatility (TSRV) computed from high frequency data in the presence of market microstructure noise, under several different dynamics for the volatility process and assumptions on the noise. We show that TSRV largely outperforms RV, whether looking at bias, variance, RMSE or out-of-sample forecasting ability. An empirical application to all DJIA stocks confirms the simulation results.

AB - We compare the forecasts of Quadratic Variation given by the Realized Volatility (RV) and the Two Scales Realized Volatility (TSRV) computed from high frequency data in the presence of market microstructure noise, under several different dynamics for the volatility process and assumptions on the noise. We show that TSRV largely outperforms RV, whether looking at bias, variance, RMSE or out-of-sample forecasting ability. An empirical application to all DJIA stocks confirms the simulation results.

KW - High frequency data

KW - Market microstructure noise

KW - Measurement error

KW - Out of sample forecasts

KW - Realized volatility

KW - Two scales realized volatility

UR - https://www.scopus.com/pages/publications/55349147645

UR - https://www.scopus.com/pages/publications/55349147645#tab=citedBy

U2 - 10.1016/j.jeconom.2008.09.015

DO - 10.1016/j.jeconom.2008.09.015

M3 - Article

AN - SCOPUS:55349147645

SN - 0304-4076

VL - 147

SP - 17

EP - 33

JO - Journal of Econometrics

JF - Journal of Econometrics

IS - 1

ER -

Out of sample forecasts of quadratic variation

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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