Risk Assessment and Spurious Seasonality

Center for Quantitative Risk Analysis (CEQURA), Working Paper Number 19, 2021

31 Pages Posted: 22 Jun 2017 Last revised: 4 Jun 2021

See all articles by Malte S. Kurz

Malte S. Kurz

University of Hamburg - Hamburg Business School; University of Hamburg

Stefan Mittnik

University of Kiel - Institute of Statistics & Econometrics; Ludwig Maximilian University of Munich (LMU) - Faculty of Economics; CESifo (Center for Economic Studies and Ifo Institute)

Date Written: June 4, 2021

Abstract

To determine the appropriate level of risk capital financial institutions are required to empirically estimate and predict specific risk measures. Although regulation commonly prescribes the forecasting horizon and the frequency with which risk assessments have to be reported, the scheme with which the underlying data are sampled typically remains unspecified. We show that, given assessment frequency and forecasting horizon, the choice of the sampling scheme can greatly affect the results of risk assessment procedures. Specifically, sequences of variance estimates are prone to exhibit spurious seasonality when the assessment frequency is higher than the sampling frequency of non-overlapping asset return data. We derive the autocorrelation function of such sequences for a general class of weak white noise processes and for a general class of variance estimators. The problem of spurious seasonality can be overcome by using overlapping return data for estimation of risk measures.

Keywords: autocorrelation function, Basel III, GARCH, overlapping data, temporal aggregation

JEL Classification: C18, C58, G17, G28

Suggested Citation

Kurz, Malte S. and Kurz, Malte S. and Mittnik, Stefan, Risk Assessment and Spurious Seasonality (June 4, 2021). Center for Quantitative Risk Analysis (CEQURA), Working Paper Number 19, 2021, Available at SSRN: https://ssrn.com/abstract=2990772 or http://dx.doi.org/10.2139/ssrn.2990772

Malte S. Kurz (Contact Author)

University of Hamburg - Hamburg Business School ( email )

Moorweidenstr. 18
Hamburg, 20148
Germany

University of Hamburg ( email )

Hamburg

Stefan Mittnik

University of Kiel - Institute of Statistics & Econometrics ( email )

Olshausenstr. 40
Kiel, Schleswig-Holstein 24118
Germany

Ludwig Maximilian University of Munich (LMU) - Faculty of Economics ( email )

Akademiestr.1/III
Munich, D-80539
Germany

CESifo (Center for Economic Studies and Ifo Institute)

Poschinger Str. 5
Munich, DE-81679
Germany

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