High-dimensional copula-based distributions with mixed frequency data

Oh, Dong Hwan; Patton, Andrew J.

doi:10.1016/j.jeconom.2016.04.011

Cited by 63 publications

(37 citation statements)

References 35 publications

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“…Second, we estimate copula models by maximum composite likelihood estimation (MCLE). MCLE is employed because it yields consistent estimates for the true parameters in large scale problems, while the ordinary maximum likelihood estimation (MLE) may estimate the parameters driving the dynamic process with bias, as discussed in Engle, Shephard, and Shepphard (2008), Christoffersen et al (2012), and Oh and Patton (2015).…”

Section: The Dynamic Skewed Copula Modelmentioning

confidence: 99%

Modeling High Dimensional Asset Pricing Returns Using a Dynamic Skewed Copula Model

Gong¹,

Liang²,

Zhu³

2019

BEMP

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We propose a dynamic skewed copula to model multivariate dependence in asset returns in a flexible yet parsimonious way. We then apply the model to 50 exchange traded funds. The new copula is shown to have better in-sample and out-of-sample performance than existing copulas. In particular, the dynamic model is able to capture increasing dependence patterns during the fixnancial crisis periods. It is crucial for investors to take dynamic dependence structure into account when modeling high dimensional returns.

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Section: The Dynamic Skewed Copula Modelmentioning

confidence: 99%

Modeling High Dimensional Asset Pricing Returns Using a Dynamic Skewed Copula Model

Gong¹,

Liang²,

Zhu³

2019

BEMP

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“…We consider three popular baseline models: the vech-HAR (Chiriac and Voev, 2010), the HAR-DRD model (Oh and Patton, 2015), and the HEAVY model (Noureldin, Shephard, and Sheppard, 2012). For comparison purposes, we also consider a simple Exponentially Weighted Moving Average (EWMA) filter, in which we allow the filter weights to vary with the estimation error.…”

Section: Dynamic Attenuation Modelsmentioning

confidence: 99%

“…8 separately. The HAR-DRD model of Oh and Patton (2015), is based on the decomposition of a covariance matrix into…”

Section: Harq-drd Modelsmentioning

confidence: 99%

Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions

2016

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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. School and Nova SBE Lisbon for helpful comments. Bingzhi Zhao kindly provided us with the cleaned high-frequency data underlying our empirical investigations.Modeling and forecasting of dynamically varying covariances have received much attention in the literature, with numerous multivariate ARCH, GARCH and stochastic volatility specifications been proposed for the job. All of these procedures effectively treat the covariances as latent. More recently, however, the increased availability of reliable high-frequency intraday asset prices have has spurred somewhat of a paradigm shift based on the idea of directly modeling and forecasting expost realized covariance measures constructed from intraday data (see, e.g., Andersen, Bollerslev, Christoffersen, and Diebold, 2013, for a discussion of both the earlier parametric models and the more recent realized volatility literature). The benefits of high-frequency-based realized volatility procedures for practical investment and portfolio allocation decisions have also been extensively documented in the literature (e.g., Fleming, Kirby, and Ostdiek, 2003;Bandi, Russell, and Zhu, 2008;Pooter, Martens, and Dijk, 2008;Liu, 2009;Varneskov and Voev, 2013; Hautsch, Kyj, and Malec, 2015, among others).Even though the use of high-frequency intraday data generally allows for the construction of more accurate realized covariance measures than lower frequency (e.g., daily) data, they are still estimates and as such subject to estimation error. Correspondingly, the use of these covariance estimates in dynamic forecasting models leads to a classical errors-in-variables problem and an attenuation of the parameters towards zero relative to a forecasting model based on the true (latent) covariances. If the measurement errors were homoskedastic, this attenuation would be time invariant, and from a practical forecasting perspective inconsequential. If, however, the measurement errors are heteroskedastic, as we show is the case with realized covariance estimates, all observations are "not equal," and the presence of such heteroskedastic measurement errors can indeed have ...

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“…25 Recently, the copula theory developed from the¯elds of mathematics and statistics has many advantages in analyzing the correlation structure between multiple variables, 26 and it also contributes to resolve the above-mentioned defects. Copula theory studied and applied in¯nance 27 and neuroscience 16,[28][29][30][31][32][33][34] yields some important results. Hu et al 34 have proposed an e®ective model-free, copula-based GC (copula-GC) measure that can be used to reveal nonlinear and high-order moment causality, and obtained better performance than GC in the cause-e®ect assessment of neural time series, such as local¯eld potential (LFP) and neural spike trains (NST).…”

Section: Introductionmentioning

confidence: 99%

Time-Frequency-Domain Copula-Based Granger Causality and Application to Corticomuscular Coupling in Stroke

She

Zheng

Tan

et al. 2019

Int. J. Human. Robot.

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The corticomuscular coupling (CMC) characterization between the motor cortex and muscles during motion control is a valid biomarker of motor system function after stroke, which can improve clinical decision-making. However, traditional CMC analysis is mainly based on the coherence method that can’t determine the coupling direction, whereas Granger Causality (GC) is limited in identifying linear cause–effect relationship. In this paper, a time-frequency domain copula-based GC (copula-GC) method is proposed to assess CMC characteristic. The 32-channel electroencephalogram (EEG) signals over brain scalp and electromyography (EMG) signals from upper limb were recorded during controlling and maintaining steady-state force output for five stroke patients and five healthy controls. Then, the time-frequency copula-GC analysis was applied to evaluate the CMC strength in both directions. Experimental results show that the CMC strength of descending direction is greater than that of ascending direction in the time domain for healthy controls. With the increase of grip strength, the bi-directional CMC strength has an increasing trend. Meanwhile, the bi-directional CMC strength of right hand is larger than that of left hand. In addition, the bi-directional CMC strength of stroke patients is lower than that of healthy controls. In the frequency domain, the strongest CMC is observed at the beta frequency band. Additionally, the CMC strength of descending direction is slightly larger than that of ascending direction in healthy controls, while the CMC strength of descending direction is lower than that of ascending direction in stroke patients. We suggest that the proposed time-frequency domain analysis approach based on copula-GC can effectively detect complex functional coupling between cortical oscillations and muscle activities, and provide a potential quantitative analysis measure for motion control and rehabilitation evaluation.

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High-dimensional copula-based distributions with mixed frequency data

Cited by 63 publications

References 35 publications

Modeling High Dimensional Asset Pricing Returns Using a Dynamic Skewed Copula Model

Modeling High Dimensional Asset Pricing Returns Using a Dynamic Skewed Copula Model

Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions

Time-Frequency-Domain Copula-Based Granger Causality and Application to Corticomuscular Coupling in Stroke

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