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Realized variances vs. correlations : unlocking the gains in multivariate volatility forecasting

Capera Romero, Laura; Opschoor, Anne - 2024

This paper disentangles the added value of using high-frequency-based (realized) covariance measures on multivariate volatility forecasting into two pillars: the realized variances and realized correlations and quantifies the corresponding economic gains using a broad set of portfolio...

Persistent link: https://www.econbiz.de/10015064180

Estimation and forecasting of large realized covariance matrices and portfolio choice

Callot, Laurent; Kock, Anders B.; Medeiros, Marcelo C. - 2014

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...

Persistent link: https://www.econbiz.de/10010433899

New HEAVY models for fat-tailed returns and realized covariance kernels

Janus, Paweł; Lucas, André; Opschoor, Anne - 2014

We develop a new model for the multivariate covariance matrix dynamics based on daily return observations and daily realized covariance matrix kernels based on intraday data. Both types of data may be fat-tailed. We account for this by assuming a matrix-F distribution for the realized kernels,...

Persistent link: https://www.econbiz.de/10010364103

Realized Wishart-GARCH : a score-driven multi-Asset volatility model

Hansen, Peter Reinhard; Janus, Paweł; Koopman, Siem Jan - 2016

We propose a novel multivariate GARCH model that incorporates realized measures for the variance matrix of returns. The key novelty is the joint formulation of a multivariate dynamic model for outer-products of returns, realized variances and realized covariances. The updating of the variance...

Persistent link: https://www.econbiz.de/10011520881

Spectrally-corrected estimation for high-dimensional Markowitz mean-variance optimization

Bai, Zhidong; Li, Hua; McAleer, Michael; Wong, Wing Keung - 2016

.We analyze the traditional Markowitz mean-variance (MV) portfolio by large dimension matrix theory, and find the spectral … findings are consistent with the theory developed in the paper. …

Persistent link: https://www.econbiz.de/10011456708

Dynamic correlation or tail dependence hedging for portfolio selection

Elkamhia, Redouane; Stefanova, Denitsa - 2010

We solve for the optimal portfolio allocation in a setting where both conditional correlation and theclustering of … when dynamic conditional correlation has been accounted for, andvice versa. Both effects have distinct portfolio … varying levels of average correlation and tail dependence coefficients. …

Persistent link: https://www.econbiz.de/10011383108

A one line derivation of DCC : application of a vector random coefficient moving average process

Hafner, Christian M.; McAleer, Michael - 2014 - Revised: July 2014

One of the most widely-used multivariate conditional volatility models is the dynamic conditional correlation (or DCC … rather than a dynamic conditional correlation model; (ii) provides the motivation, which is presently missing, for … standardization of the conditional covariance model to obtain the conditional correlation model; and (iii) shows that the appropriate …

Persistent link: https://www.econbiz.de/10010374571

Fractional integration and fat tails for realized covariance kernels and returns

Lucas, André; Opschoor, Anne - 2016 - This version: September 1, 2016

We introduce a new fractionally integrated model for covariance matrix dynamics based on the long-memory behavior of daily realized covariance matrix kernels and daily return observations. We account for fat tails in both types of data by appropriate distributional assumptions. The covariance...

Persistent link: https://www.econbiz.de/10011531139

Analytic decision rules for financial stochastic programs

Siegmann, Adriaan Hendrik; Lucas, André - 2000 - This version: May 1, 2000

Contemporary financial stochastic programs typically involve a trade-offbetween return and (downside)-risk. Using stochastic programming we characterize analytically (rather than numerically) the optimal decisions that follow from characteristic single-stage and multi-stage versions of such...

Persistent link: https://www.econbiz.de/10011303296

Analytic decision rules for financial stochastic programs

Siegmann, Arjen H.; Lucas, André - 2000

Persistent link: https://www.econbiz.de/10001477415

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