Modeling and Forecasting Large Realized Covariance Matrices and Portfolio Choice

Callot, Laurent; Kock, Anders; Medeiros, Marcelo C.

doi:10.1002/jae.2512

Cited by 86 publications

(50 citation statements)

References 28 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Owning to developments of computer techniques, it is today possible to store and analyze huge data sets with the aim of improving the performance of holding portfolio. As a result, the usage of the realized covariance matrix in portfolio theory has become a popular topic in finance (Hautsch et al 2015;Callot et al 2017).…”

Section: Resultsmentioning

confidence: 99%

Goodness-of-fit tests for centralized Wishart processes

Alfelt

Bodnar

Tyrcha

2019

Communications in Statistics - Theory and Methods

View full text Add to dashboard Cite

In this paper we present several goodness-of-fit tests for the centralized Wishart process, a popular matrix-variate time series model used to capture the stochastic properties of realized covariance matrices. The new test procedures are based on the extended Bartlett decomposition derived from the properties of the Wishart distribution and allows to obtain sets of independently and standard normally distributed random variables under the null hypothesis. Several tests for normality and independence are then applied to these variables in order to support or to reject the underlying assumption of a centralized Wishart process. In order to investigate the influence of estimated parameters on the suggested testing procedures in the finite-sample case, a simulation study is conducted. Finally, the new test methods are applied to real data consisting of realized covariance matrices computed for the returns on six assets traded on the New York Stock Exchange. ARTICLE HISTORY

show abstract

Section: Resultsmentioning

confidence: 99%

Goodness-of-fit tests for centralized Wishart processes

Alfelt

Bodnar

Tyrcha

2019

Communications in Statistics - Theory and Methods

View full text Add to dashboard Cite

show abstract

“…There have been a few attempts in the literature to model daily realized volatility matrices using parsimonious parametric models and to predict future realized measure. Callot et al [12] proposed to use a vector autoregressive process to model the vast conditional covariance E( Σ τ +1 |I τ ) where the parameters are estimated via LASSO. Other existing work on multivariate volatility modeling and forecasting includes Chiriac and Voev [13], Bauer and Vorkink [7], Hansen et al [26], among others.…”

Section: Daily Covariance Matrix Forecastmentioning

confidence: 99%

Large portfolio allocation using high-frequency financial data

Zou

Wang

2018

Statistics and Its Interface

View full text Add to dashboard Cite

Asset allocation strategy involves dividing an investment portfolio among different assets according to their risk levels. In recent decades, estimating volatilities of asset returns based on high-frequency data has emerged as a topic of interest in financial econometrics. However, most available methods are not directly applicable when the number of assets involved is large, since small component-wise estimation errors could accumulate to large matrix-wise errors. In this paper, we introduce a method to carry out efficient asset allocation using sparsity-inducing regularization on the realized volatility matrix obtained from intraday high-frequency data. We illustrate the new method with the high-frequency price data on stocks traded in New York Stock Exchange over a period of six months in 2013. Simulation studies based on popular volatility models are also presented. The proposed methodology is theoretically justified. Numerical results also show that our approach performs well in portfolio allocation by pooling together the strengths of regularization and estimation from a high-frequency finance perspective.

show abstract

“…We take the logarithmic transformation of the volatilities, which ensures the positivity of the volatility forecasts (e.g. Callot et al, 2017). We consider a stationary VAR of order P for the log volatilities…”

Section: Models and Estimatorsmentioning

confidence: 99%

Volatility Spillovers and Heavy Tails: A Large T-Vector Autoregressive Approach

2017

View full text Add to dashboard Cite

Abstract. Volatility is a key measure of risk in financial analysis. The high volatility of one financial asset today could affect the volatility of another asset tomorrow. These lagged effects among volatilitieswhich we call volatility spillovers -are studied using the Vector AutoRegressive (VAR) model. We account for the possible fat-tailed distribution of the VAR model errors using a VAR model with errors following a multivariate Student t-distribution with unknown degrees of freedom. Moreover, we study volatility spillovers among a large number of assets. To this end, we use penalized estimation of the VAR model with tdistributed errors. We study volatility spillovers among energy, biofuel and agricultural commodities and reveal bidirectional volatility spillovers between energy and biofuel, and between energy and agricultural commodities.

show abstract

Modeling and Forecasting Large Realized Covariance Matrices and Portfolio Choice

Cited by 86 publications

References 28 publications

Goodness-of-fit tests for centralized Wishart processes

Goodness-of-fit tests for centralized Wishart processes

Large portfolio allocation using high-frequency financial data

Volatility Spillovers and Heavy Tails: A Large T-Vector Autoregressive Approach

Contact Info

Product

Resources

About