Exponential smoothing of realized portfolio weights

Kellermann

2022

Metrika

Self Cite

We focus on estimating daily integrated volatility (IV) by realized measures based on intraday returns following a discrete-time stochastic model with a pronounced intraday periodicity (IP). We demonstrate that neglecting the IP-impact on realized estimators may lead to invalid statistical inference concerning IV for a common finite number of intraday returns. For a given IP functional form, we analytically derive robust IP-correction factors for realized measures of IV as well as their asymptotic distributions. We show both in Monte Carlo simulations and empirically that the proposed bias corrections are the robust way to account for IP by computing realized estimators.

Section: Discussion and Further Extensionsmentioning

confidence: 99%

The effect of intraday periodicity on realized volatility measures

Dette

Kellermann

2022

Metrika

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“…These companies are categorized by the GICS classification (cf. References 28,29) into nine different industry sectors. We provide the companies' list in Table A1 of the Appendix, whereas the GICS classification is given in Table 2.…”

Section: Empirical Applicationmentioning

confidence: 99%

Testing for parameter changes in linear state space models

Appl Stoch Models Bus & Ind

Köhler

Schmid

et al. 2021

Self Cite

Linear state space models (LSSMs) provide a very general framework for multiple time series analysis. We propose a novel statistical procedure for testing validity of a LSSM which is focused on the detection of changes in parameters of the given LSSM. We derive the moments as well as the asymptotic distribution of the test statistic, and investigate the test size and the test power for changes in means, variances, and autoregressive coefficients. In the empirical application we test the validity of LSSMs applied to daily realized volatility time series.

“…For example, the constrained GMVP weights can be obtained by solving the following optimization problem (cf. Golosnoy et al, 2019) for a given covariance matrix estimator b Σ:…”

Section: Sample Estimators Of Gmvp Weightsmentioning

confidence: 99%

“…In order to illustrate the theoretical findings reviewed in the previous section, we now provide some empirical evidence concerning the stochastic properties of realized GMVP weights. Our empirical study is based on a data set with k = 100 risky assets selected by liquidity from the S&P500 index, the detailed list of stocks can be found in Golosnoy et al (2019). Hence, we consider a fairly broad GMVP setting which allows to diversify out nonsystematic risks and to achieve the desired portfolio diversification benefits.…”

Section: Empirical Illustrationmentioning

confidence: 99%

“…Next we make a quantitative comparison of the realized GMVP weights as in (i)-(iii) by computing for them several important descriptive measures which are provided in Table 1. Following Golosnoy et al (2019), we report time averages for the absolute positions and daily turnover which are of importance due to proportional transaction costs. We also provide time averages of daily maximum and minimum weights as well as time average of 25, 50, and 75% quantiles.…”

Section: Comparison Of Various Realized Gmvp Weightsmentioning

confidence: 99%

See 1 more Smart Citation

Sample and realized minimum variance portfolios: Estimation, statistical inference, and tests

WIREs Computational Stats

Gribisch

Seifert

2021

Self Cite

The global minimum variance portfolio (GMVP) is the starting point of the Markowitz mean‐variance efficient frontier. The estimation of the GMVP weights is therefore of much importance for financial investors. The GMVP weights depend only on the inverse covariance matrix of returns on financial risky assets, for this reason the estimated GMVP weights are subject to substantial estimation risk, especially in high‐dimensional portfolio settings. In this paper we review the recent literature on traditional sample estimators for the unconditional GMVP weights which are typically based on daily asset returns, as well as on modern realized estimators for the conditional GMVP weights based on intraday high‐frequency returns. We present various types of GMVP estimators with the corresponding stochastic results, discuss statistical tests and point on some directions for further research. Our empirical application illustrates selected properties of realized GMVP weights. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Multivariate Analysis Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data Statistical Models > Multivariate Models