Robustness of power properties of non-linearity tests

The paper examines the robustness of the size and power properties of the standard non-linearity tests under different conditions such as moment failure and asymmetry of innovations. Our results reveal the following. First, there seems not to be a direct link between moment condition failure and the power variation of non-linearity tests. Second, the power of the tests is very sensitive to asymmetry of innovations compared to moment condition failure. Third, although we evaluate 9 non-linear time series models using 8 standard non-linearity tests, some non-linear models remain completely undetected

The Sign RCA Models: Comparing Predictive Accuracy of VaR Measures

Evaluating Value at Risk (VaR) methods of predictive accuracy in an objective and effective framework is important for both efficient capital allocation and loss prediction. From this reasons, finding an adequate method of estimating and backtesting is crucial for both the regulators and the risk managers’. The Sign RCA models may be useful to obtain the accurate forecasts of VaR. In this research one briefly describes the Sign RCA models, the Value at Risk and backtesting. We compare the predictive accuracy of alternative VaR forecasts obtained from different models. Empirical example is mainly related to the PBG Capital Group shares on the Warsaw Stock Exchange.Family of Sign RCA Models, Value at Risk, backtesting, loss function.

The random coefficient autoregressive model with seasonal volatility innovations (RCA-SGARCH)

This paper dealt with the autoregressive model when the coefficient is random. The residuals series of the model exhibit two behaviors, kurtosis and volatility. These volatilities are usually seasonal in the real financial data, which always uses GARCH models. So the use of RCA and GARCH models together will provide an appropriate framework to study and analysis of time-varying volatility as well as the presence of seasonal effects in financial series. Applying copper's daily economic close prices when the errors series are distributed, as usual, t_((3)) and t_((7)) distributions are achieved. Therefore, the RCA(1) model, when residuals follow the GARCH(1, 0)x(0, 1)_5 model together, is the appropriate model

Do We Really Need Both BEKK and DCC? A Tale of Two Covariance Models

Large and very large portfolios of financial assets are routine for many individuals and organizations. The two most widely used models of conditional covariances and correlations are BEKK and DCC. BEKK suffers from the archetypal "curse of dimensionality" whereas DCC does not. This is a misleading interpretation of the suitability of the two models to be used in practice. The primary purposes of the paper are to define targeting as an aid in estimating matrices associated with large numbers of financial assets, analyze the similarities and dissimilarities between BEKK and DCC, both with and without targeting, on the basis of structural derivation, the analytical forms of the sufficient conditions for the existence of moments, and the sufficient conditions for consistency and asymptotic normality, and computational tractability for very large (that is, ultra high) numbers of financial assets, to present a consistent two step estimation method for the DCC model, and to determine whether BEKK or DCC should be preferred in practical applications.Conditional correlations, Conditional covariances, Diagonal models, Forecasting, Generalized models, Hadamard models, Scalar models, Targeting.

Do We Really Need Both BEKK and DCC? A Tale of Two Covariance Models

Large and very large portfolios of financial assets are routine for many individuals and organizations. The two most widely used models of conditional covariances and correlations are BEKK and DCC. BEKK suffers from the archetypal "curse of dimensionality" whereas DCC does not. This is a misleading interpretation of the suitability of the two models to be used in practice. The primary purposes of the paper are to define targeting as an aid in estimating matrices associated with large numbers of financial assets, analyze the similarities and dissimilarities between BEKK and DCC, both with and without targeting, on the basis of structural derivation, the analytical forms of the sufficient conditions for the existence of moments, and the sufficient conditions for consistency and asymptotic normality, and computational tractability for very large (that is, ultra high) numbers of financial assets, to present a consistent two step estimation method for the DCC model, and to determine whether BEKK or DCC should be preferred in practical applications.

Assessing Volatility Forecasting Models: Why GARCH Models Take the Lead

The paper provides a critical assessment of the main forecasting techniques and an evaluation of the superiority of the more advanced and complex models. Ultimately, its scope is to offer support for the rationale behind of an idea: GARCH is the most appropriate model to use when one has to evaluate the volatility of the returns of groups of stocks with large amounts (thousands) of observations. The appropriateness of the model is seen through a unidirectional perspective of the quality of volatility forecast provided by GARCH when compared to any other alternative model, without considering any cost component.volatility, GARCH, forecast, correlation, risk, heteroskedasticity

Option Pricing under Sign RCA-GARCH Models

After Black and Scholes’s groundbreaking work, the literature concerning pricing options has become a very important area of research. Numerous option valuation methods have been developed. This paper shows how one can compute option prices using Sign RCA-GARCH models for the dynamics of the volatility. Option pricing obtained from Sign RCA-GARCH models, the Black and Scholes’s valuation and other selected GARCH option pricing models are compared with the market prices. This approach was illustrated by the valuation of the European call options on the WIG20 index. The empirical results indicated that RCA-GARCH and Sign RCA-GARCH models can be successfully used for pricing options. However none of the models can be indicated as the best one for the option valuations for every period and every time to maturity of the options

Do We Really Need Both BEKK and DCC? A Tale of Two Multivariate GARCH Models

The management and monitoring of very large portfolios of financial assets are routine for many individuals and organizations. The two most widely used models of conditional covariances and correlations in the class of multivariate GARCH models are BEKK and DCC. It is well known that BEKK suffers from the archetypal “curse of dimensionalityâ€, whereas DCC does not. It is argued in this paper that this is a misleading interpretation of the suitability of the two models for use in practice. The primary purpose of this paper is to analyze the similarities and dissimilarities between BEKK and DCC, both with and without targeting, on the basis of the structural derivation of the models, the availability of analytical forms for the sufficient conditions for existence of moments, sufficient conditions for consistency and asymptotic normality of the appropriate estimators, and computational tractability for ultra large numbers of financial assets. Based on theoretical considerations, the paper sheds light on how to discriminate between BEKK and DCC in practical applications.forecasting;conditional correlations;Hadamard models;conditional covariances;diagonal models;generalized models;scalar models;targeting