Do High-Frequency Data Improve High-Dimensional Portfolio Allocations?

Hautsch, Nikolaus; Kyj, Lada M.; Malec, Peter

doi:10.2139/ssrn.2228772

Cited by 26 publications

(45 citation statements)

References 79 publications

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“…To most directly highlight the benefits of the high-frequency-based procedures and the new dynamic attenuation models, we focus our analysis on the intraday realized covariances and corresponding open-to-close returns. This also mirrors a number of studies in the recent literature (see, e.g., Lunde, Shephard, and Sheppard, 2015;Hautsch, Kyj, and Malec, 2015;De Lira Salvatierra and Patton, 2015, among others). 13 Table 1 provides summary statistics for the resulting daily MK covariance estimates.…”

Section: Datasupporting

confidence: 88%

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Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions

Bollerslev

Patton

Quaedvlieg

2018

Journal of Econometrics

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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.

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Section: Datasupporting

confidence: 88%

“…Our implementation of the shrinkage procedures, is based on the traditional lower frequency estimators and theoretical set-up of Ledoit and Wolf (2003), as recently adapted to the highfrequency setting by Hautsch, Kyj, and Malec (2015). Specifically,…”

Section: Alternative Ex-post Shrinkage Proceduresmentioning

confidence: 99%

Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions

Bollerslev

Patton

Quaedvlieg

2018

Journal of Econometrics

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“…A smaller number of papers focus in the economic applications (Liu, 2009;Hautsch et al, 2013;Wink Junior and Pereira, 2013). As pointed out by Granger and Pesaran (2000), Pesaran and Skouras (2002) and Granger and Machina (2006), when forecasts are used in decision making, it is important to consider the decision process in the ex post evaluation of these forecasts, allowing the interaction between the forecasting model and the objective of the decision making.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, with an applied approach, Hautsch et al (2013) focus on the forecasting of the covariance matrix in high-dimensional portfolio allocation. The analysis considered 400 assets sampled in the daily and intraday frequencies.…”

Section: Introductionmentioning

confidence: 99%

Selection of Minimum Variance Portfolio Using Intraday Data: An Empirical Comparison Among Different Realized Measures for BM&FBovespa Data

Ziegelmann¹,

Borges²,

Caldeira³

2015

Brazilian Review of Econometrics

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This paper explores different covariance matrix estimators; both the conditional and the unconditional versions, obtained via intraday data and named realized measures, to the minimum variance portfolio selection problem. Intraday data are sampled in a synchronized manner as well as in an unsynchronized version. For sake of comparison, we also use daily data estimators. The major contribution of this work is of empirical nature, focused on the Brazilian scenario. We evaluate some out-of-sample performance indexes of the obtained portfolios for a set of 30 stocks traded on the São Paulo stock exchange (BM&FBovespa). The results show that the estimator of the conditional covariance matrix of returns using a scalar vt-VECH model based on higher frequency data leads to substantial earnings, reducing portfolio risk, increasing the average adjusted-by-risk return and decreasing the turnover.

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“…However, their analysis is restricted to small sets of assets. Hautsch et al (2011) introduce the Multi-Scale Spectral Components (MSSC) model, which is a kind of factor specification, for forecasting covariance matrices and they show that high-frequency data models can translate into better portfolio allocation decisions over longer investment horizons than previously believed. Although, these authors consider the same problem as we do here, their modelling approach is quite different.…”

Section: Introductionmentioning

confidence: 99%

Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice

2014

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in ANDERS B. KOCKAarhus University, CREATES, Denmark. MARCELO C. MEDEIROSDepartment of Economics, Pontifical Catholic University of Rio de Janeiro, Brazil.Abstract. In this paper we consider modeling and forecasting of large realized covariance matrices by penalized vector autoregressive models. We propose using Lasso-type estimators to reduce the dimensionality to a manageable one and provide strong theoretical performance guarantees on the forecast capability of our procedure. To be precise, we show that we can forecast future realized covariance matrices almost as precisely as if we had known the true driving dynamics of these in advance. We next investigate the sources of these driving dynamics for the realized covariance matrices of the 30 Dow Jones stocks and find that these dynamics are not stable as the data is aggregated from the daily to the weekly and monthly frequency. The theoretical performance guarantees on our forecasts are illustrated on the Dow Jones index. In particular, we can beat our benchmark by a wide margin at the longer forecast horizons. Finally, we investigate the economic value of our forecasts in a portfolio selection exercise and find that in certain cases an investor is willing to pay a considerable amount in order get access to our forecasts.

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Do High-Frequency Data Improve High-Dimensional Portfolio Allocations?

Cited by 26 publications

References 79 publications

Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions

Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions

Selection of Minimum Variance Portfolio Using Intraday Data: An Empirical Comparison Among Different Realized Measures for BM&FBovespa Data

Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice

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