We reveal that in the estimation of univariate GARCH or multivariate generalized orthogonal GARCH (GO-GARCH) models, maximizing the likelihood is equivalent to making the standardized residuals as independent as possible. Based on that, we propose three factor GARCH models in the framework of GO-GARCH: independent-factor GARCH exploits factors that are statistically as independent as possible; ...

We document asymmetric and time-varying features of dependence between the credit risks of global systemically important banks (G-SIBs) in the UK banking industry using a CDS dataset. We model the dependence of CDS spreads using a dynamic asymmetric copula. Comparing our model with traditional copula models, we find that they usually underestimate the probability of joint (or conditional) defau...

We investigate how Global Economic Policy Uncertainty (GEPU) drives the long-run components of volatilities and correlations in crude oil and U.S. industry-level stock markets. Using the modified generalized autoregressive conditional heteroskedasticity mixed data sampling (GARCH-MIDAS) and dynamic conditional correlation mixed data sampling (DCC-MIDAS) specifications, we find that GEPU is posi...

Correlations between U.S. stocks and the aggregate U.S. market are much greater for downside moves, especially for extreme downside moves, than for upside moves. We develop a new statistic for measuring, comparing, and testing asymmetries in conditional correlations. Conditional on the downside, correlations in the data differ from the conditional correlations implied by a normal distribution b...

Examinations of the dynamics of daily returns and volatility in stock markets of the U.S., Hong Kong and mainland China (Shanghai and Shenzhen) over 2 January 2001 to 8 February 2013 suggest: (1) evidence of unidirectional return spillovers from the U.S. to the other three markets; but no spillover between Hong Kong and either of the two mainland China markets; (2) evidence of unidirectional AR...

Molecular dynamic simulation is a powerful tool for finding dependencies in biological molecules. Correlation matrices can map out the nature of correlated movements performed by different regions within the molecule during their solution state simulations and provide a critical tool in understanding the interactions between different residues in a structure. Here we present a methodology to ex...

Batch and semi-batch modes of production are common in a number of high value-added industries including specialty chemicals, pharmaceuticals, and biologics. Online monitoring of such processes seeks to detect run-to-run deviations, anomalies or faulty conditions by analyzing real-time measurements so that they can be corrected quickly and the batch recovered. Multivariate statistical methods h...

This paper considers a multivariate t version of the Gaussian dynamic conditional correlation (DCC) model proposed by Engle (2002), and suggests the use of devolatized returns computed as returns standardized by realized volatilities rather than by GARCH type volatility estimates. The t-DCC estimation procedure is applied to a portfolio of daily returns on currency futures, government bonds and...

Conditional heteroskedasticity is a common feature of financial and macroeconomic time series data. When such data are used to estimate dynamic regression models, standard checks for serial correlation are inappropriate. In such circumstances, it is obviously important to have valid tests that are reliable in finite samples. Generalizations of the standard Lagrange multiplier test and a Hausman...

This research analyzes the dynamic properties of the difference equation that arises when markets exhibit serial correlation and mean reversion. We identify the correlation and reversion parameters for which prices will overshoot equilibrium (“cycles”) and/or diverge permanently from equilibrium. We then estimate the serial correlation and mean reversion coefficients from a large panel data set...