Department of Economics and Business Economics

Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions

Research output: Working paper/Preprint Working paper

Standard

Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions. / Bollerslev, Tim; Patton, Andrew J.; Quaedvlieg, Rogier.

Aarhus : Institut for Økonomi, Aarhus Universitet, 2016.

Research output: Working paper/Preprint Working paper

Harvard

Bollerslev, T, Patton, AJ & Quaedvlieg, R 2016 'Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions' Institut for Økonomi, Aarhus Universitet, Aarhus.

APA

Bollerslev, T., Patton, A. J., & Quaedvlieg, R. (2016). Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions. Institut for Økonomi, Aarhus Universitet. CREATES Research Papers No. 2016-10

CBE

Bollerslev T, Patton AJ, Quaedvlieg R. 2016. Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions. Aarhus: Institut for Økonomi, Aarhus Universitet.

MLA

Bollerslev, Tim, Andrew J. Patton and Rogier Quaedvlieg Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions. Aarhus: Institut for Økonomi, Aarhus Universitet. (CREATES Research Papers; Journal number 2016-10). 2016., 38 p.

Vancouver

Bollerslev T, Patton AJ, Quaedvlieg R. Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions. Aarhus: Institut for Økonomi, Aarhus Universitet. 2016 Apr 6.

Author

Bollerslev, Tim ; Patton, Andrew J. ; Quaedvlieg, Rogier. / Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions. Aarhus : Institut for Økonomi, Aarhus Universitet, 2016. (CREATES Research Papers; No. 2016-10).

Bibtex

@techreport{754d060946c240cba1107a470ee6f4f2,
title = "Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions",
abstract = "We propose a new framework for modeling and forecasting common financial risks based on (un)reliable realized covariance measures constructed from high-frequency intraday data. Our new approach explicitly incorporates the effect of measurement errors and time-varying attenuation biases into the covariance forecasts, by allowing the ex-ante predictions to respond more (less) aggressively to changes in the ex-post realized covariance measures when they are more (less) reliable. Applying the new procedures in the construction of minimum variance and minimum tracking error portfolios results in reduced turnover and statistically superior positions compared to existing procedures. Translating these statistical improvements into economic gains, we find that under empirically realistic assumptions a risk-averse investor would be willing to pay up to 170 basis points per year to shift to using the new class of forecasting models.",
keywords = "Common risks; realized covariances; forecasting; asset allocation; portfolio construction",
author = "Tim Bollerslev and Patton, {Andrew J.} and Rogier Quaedvlieg",
year = "2016",
month = apr,
day = "6",
language = "English",
series = "CREATES Research Papers",
publisher = "Institut for {\O}konomi, Aarhus Universitet",
number = "2016-10",
type = "WorkingPaper",
institution = "Institut for {\O}konomi, Aarhus Universitet",

}

RIS

TY - UNPB

T1 - Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions

AU - Bollerslev, Tim

AU - Patton, Andrew J.

AU - Quaedvlieg, Rogier

PY - 2016/4/6

Y1 - 2016/4/6

N2 - We propose a new framework for modeling and forecasting common financial risks based on (un)reliable realized covariance measures constructed from high-frequency intraday data. Our new approach explicitly incorporates the effect of measurement errors and time-varying attenuation biases into the covariance forecasts, by allowing the ex-ante predictions to respond more (less) aggressively to changes in the ex-post realized covariance measures when they are more (less) reliable. Applying the new procedures in the construction of minimum variance and minimum tracking error portfolios results in reduced turnover and statistically superior positions compared to existing procedures. Translating these statistical improvements into economic gains, we find that under empirically realistic assumptions a risk-averse investor would be willing to pay up to 170 basis points per year to shift to using the new class of forecasting models.

AB - We propose a new framework for modeling and forecasting common financial risks based on (un)reliable realized covariance measures constructed from high-frequency intraday data. Our new approach explicitly incorporates the effect of measurement errors and time-varying attenuation biases into the covariance forecasts, by allowing the ex-ante predictions to respond more (less) aggressively to changes in the ex-post realized covariance measures when they are more (less) reliable. Applying the new procedures in the construction of minimum variance and minimum tracking error portfolios results in reduced turnover and statistically superior positions compared to existing procedures. Translating these statistical improvements into economic gains, we find that under empirically realistic assumptions a risk-averse investor would be willing to pay up to 170 basis points per year to shift to using the new class of forecasting models.

KW - Common risks; realized covariances; forecasting; asset allocation; portfolio construction

M3 - Working paper

T3 - CREATES Research Papers

BT - Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions

PB - Institut for Økonomi, Aarhus Universitet

CY - Aarhus

ER -