Financialization, Crisis and Commodity Correlation Dynamics

We study bi-variate conditional volatility and correlation dynamics for individual commodity futures and financial assets from May 1990-July 2009 using DSTCC-GARCH (Silvennoinen and Terasvirta 2009). These models allow correlation to vary smoothly between extreme states via transition functions driven by indicators of market conditions. Expected stock volatility and money manager open interest in futures markets are relevant transition variables. Results point to increasing integration between commodities and financial markets. Higher commodity returns volatility is predicted by lower interest rates and corporate bond spreads, US dollar depreciations, higher expected stock volatility and financial traders open positions. We observe higher and more variable correlations between commodity futures and financial asset returns, particularly from mid-sample, often predicted by higher expected stock volatility. For many pairings, we observe a structural break in the conditional correlation processes from the late 1990s.commodity futures; double smooth transition; conditional correlation; financialization

Portfolio allocation: Getting the most out of realised volatility

Recent advances in the measurement of volatility have utilized high frequency intraday data to produce what are generally known as realised volatility estimates. It has been shown that forecasts generated from such estimates are of positive economic value in the context of portfolio allocation. This paper considers the link between the value of such forecasts and the loss function under which models of realised volatility are estimated. It is found that employing a utility based estimation criteria is preferred over likelihood estimation, however a simple mean squared error criteria performs in a similar manner. These findings have obvious implications for the manner in which volatility models based on realised volatility are estimated when one wishes to inform the portfolio allocation decision.Volatility, utility, portfolio allocation, realized volatility, MIDAS

Parameterizing Unconditional Skewness in Models for Financial Time Series

In this paper we consider the third-moment structure of a class of nonlinear time series models. Empirically it is often found that the marginal distribution of financial time series is skewed. Therefore it is of importance to know what properties a model should possess if it is to accommodate for unconditional skewness. We consider modelling the unconditional mean and variance using models which respond nonlinearly or asymmetrically to shocks. We investigate the implications these models have on the third moment structure of the marginal distribution and different conditions under which the unconditional distribution exhibits skewness as well as nonzero third-order autocovariance structure. With this respect, the asymmetric or nonlinear specification of the conditional mean is found to be of greater importance than the properties of the conditional variance. Several examples are discussed and, whenever possible, explicit analytical expressions are provided for all third order moments and cross-moments. Finally, we introduce a new tool, shock impact curve, that can be used to investigate the impact of shocks on the conditional mean squared error of the return.asymmetry; GARCH; nonlinearity; stock impact curve; time series; unconditional skewness

Forecasting multivariate volatility in larger dimensions: some practical issues

The importance of covariance modelling has long been recognised in the field of portfolio management and large dimensional multivariate problems are increasingly becoming the focus of research. This paper provides a straightforward and commonsense approach toward investigating whether simpler moving average based correlation forecasting methods have equal predictive accuracy as their more complex multivariate GARCH counterparts for large dimensional problems. We find simpler forecasting techniques do provide equal (and often superior) predictive accuracy in a minimum variance sense. A portfolio allocation problem is used to compare forecasting methods. The global minimum variance portfolio and Model Confidence Set (Hansen, Lunde, and Nason (2003)) are used to compare methods, whilst portfolio weight stability and computational time are also considered.Volatility, multivariate GARCH, portfolio allocation

Multivariate Autoregressive Conditional Heteroskedasticity with Smooth Transitions in Conditional Correlations

In this paper we propose a new multivariate GARCH model with time-varying conditional correlation structure. The approach adopted here is based on the decomposition of the covariances into correlations and standard deviations. The time-varying conditional correlations change smoothly between two extreme states of constant correlations according to an endogenous or exogenous transition variable. An LM test is derived to test the constancy of correlations and LM and Wald tests to test the hypothesis of partially constant correlations. Analytical expressions for the test statistics and the required derivatives are provided to make computations feasible. An empirical example based on daily return series of five frequently traded stocks in the Standard & Poor 500 stock index completes the paper. The model is estimated for the full five-dimensional system as well as several subsystems and the results discussed in detail.multivariate GARCH; constant conditional correlation; dynamic conditional correlation; return comovement; variable correlation GARCH model; volatility model evaluation

Modelling and forecasting WIG20 daily returns

The purpose of this paper is to model daily returns of the WIG20 index. The idea is to consider a model that explicitly takes changes in the amplitude of the clusters of volatility into account. This variation is modelled by a positive-valued deterministic component. A novelty in specification of the model is that the deterministic component is specified before estimating the multiplicative conditional variance component. The resulting model is subjected to misspecification tests and its forecasting performance is compared with that of commonly applied models of conditional heteroskedasticity.COMPETE 2020, Portugal 2020, FEDER, FCTinfo:eu-repo/semantics/publishedVersio

Building Multivariate Time-Varying Smooth Transition Correlation GARCH Models, with an Application to the Four Largest Australian Banks

This paper proposes a methodology for building Multivariate Time-Varying STCC–GARCH models. The novel contributions in this area are the specification tests related to the correlation component, the extension of the general model to allow for additional correlation regimes, and a detailed exposition of the systematic, improved modelling cycle required for such nonlinear models. There is an R-package that includes the steps in the modelling cycle. Simulations demonstrate the robustness of the recommended model building approach. The modelling cycle is illustrated using daily return series for Australia’s four largest banks.Peer Reviewe

Models with multiplicative decomposition of conditional variances and correlations

Univariate and multivariate GARCH type models with multiplicative decomposition of the variance to short and long run components are surveyed. The latter component can be either deterministic or stochastic. Examples of both types are studied.Fundação para a Ciência e Tecnologia (FCT)info:eu-repo/semantics/publishedVersio

A Parsimonious Test of Constancy of a Positive Definite Correlation Matrix in a Multivariate Time-Varying GARCH Model

We construct a parsimonious test of constancy of the correlation matrix in the multivariate conditional correlation GARCH model, where the GARCH equations are time-varying. The alternative to constancy is that the correlations change deterministically as a function of time. The alternative is a covariance matrix, not a correlation matrix, so the test may be viewed as a general test of stability of a constant correlation matrix. The size of the test in finite samples is studied by simulation. An empirical example involving daily returns of 26 stocks included in the Dow Jones stock index is given.Peer Reviewe