http://repub.eur.nl/res/concept/jel-C14/ Recent publications classified by JEL Code C14 en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/39848/ 2013-04-19T00:00:00Z This paper develops a testing framework for comparing the predictive accuracy of copula-based multivariate density forecasts, focusing on a specific part of the joint distribution. The test is framed in the context of the Kullback-Leibler Information Criterion, but using (out-of-sample) conditional likelihood and censored likelihood in order to focus the evaluation on the region of interest. Monte Carlo simulations document that the resulting test statistics have satisfactory size and power properties in small samples. In an empirical application to daily exchange rate returns we find evidence that the dependence structure varies with the sign and magnitude of returns, such that different parametric copula models achieve superior forecasting performance in different regions of the support. Our analysis highlights the importance of allowing for lower and upper tail dependence for accurate forecasting of common extreme appreciation and depreciation of different currencies. Diks, C.G.H. Panchenko, V. Sokolinskiy, O. Dijk, D.J.C. van http://repub.eur.nl/res/pub/38773/ 2013-01-18T00:00:00Z The purpose of this paper is to examine the asymmetric relationship between price and implied volatility and the associated extreme quantile dependence using linear and non linear quantile regression approach. Our goal in this paper is to demonstrate that the relationship between the volatility and market return as quantified by Ordinary Least Square (OLS) regression is not uniform across the distribution of the volatility-price return pairs using quantile regressions. We examine the bivariate relationship of six volatility-return pairs, viz. CBOE-VIX and S&P-500, FTSE-100 Volatility and FTSE-100, NASDAQ-100 Volatility (VXN) and NASDAQ, DAX Volatility (VDAX) and DAX-30, CAC Volatility (VCAC) and CAC-40 and STOXX Volatility (VSTOXX) and STOXX. The assumption of a normal distribution in the return series is not appropriate when the distribution is skewed and hence OLS does not capture the complete picture of the relationship. Quantile regression on the other hand can be set up with various loss functions, both parametric and non-parametric (linear case) and can be evaluated with skewed marginal based copulas (for the non linear case). Which is helpful in evaluating the non-normal and non-linear nature of the relationship between price and volatility. In the empirical analysis we compare the results from linear quantile regression (LQR) and copula based non linear quantile regression known as copula quantile regression (CQR). The discussion of the properties of the volatility series and empirical findings in this paper have significance for portfolio optimization, hedging strategies, trading strategies and risk management in general. Allen, D.E. Singh, A.K. Powell, R.J. McAleer, M.J. Taylor, J. Thomas, L. http://repub.eur.nl/res/pub/38750/ 2013-01-17T00:00:00Z This paper features an analysis of the relationship between the S&P 500 Index and the VIX using daily data obtained from both the CBOE website and SIRCA (The Securities Industry Research Centre of the Asia Pacific). We explore the relationship between the S&P 500 daily continuously compounded return series and a similar series for the VIX in terms of a long sample drawn from the CBOE running from 1990 to mid 2011 and a set of returns from SIRCA's TRTH datasets running from March 2005 to-date. We divide this shorter sample, which captures the behaviour of the new VIX, introduced in 2003, into four roughly equivalent sub-samples which permit the exploration of the impact of the Global Financial Crisis. We apply to our data sets a series of non-parametric based tests utilising entropy based metrics. These suggest that the PDFs and CDFs of these two return distributions change shape in various subsample periods. The entropy and MI statistics suggest that the degree of uncertainty attached to these distributions changes through time and using the S&P 500 return as the dependent variable, that the amount of information obtained from the VIX also changes with time and reaches a relative maximum in the most recent period from 2011 to 2012. The entropy based non-parametric tests of the equivalence of the two distributions and their symmetry all strongly reject their respective nulls. The results suggest that parametric techniques do not adequately capture the complexities displayed in the behaviour of these series. This has practical implications for hedging utilising derivatives written on the VIX, which will be the focus of a subsequent study. Allen, D.E. McAleer, M.J. Powell, R.J. Singh, A.K. http://repub.eur.nl/res/pub/38710/ 2012-12-01T00:00:00Z Regression analyses of cross-country economic growth data are complicated by two main forms of model uncertainty: the uncertainty in selecting explanatory variables and the uncertainty in specifying the functional form of the regression function. Most discussions in the literature address these problems independently, yet a joint treatment is essential. We present a new framework that makes such a joint treatment possible, using flexible nonlinear models specified by Gaussian process priors and addressing the variable selection problem by means of Bayesian model averaging. Using this framework, we extend the linear model to allow for parameter heterogeneity of the type suggested by new growth theory, while taking into account the uncertainty in selecting explanatory variables. Controlling for variable selection uncertainty, we confirm the evidence in favor of parameter heterogeneity presented in several earlier studies. However, controlling for functional form uncertainty, we find that the effects of many of the explanatory variables identified in the literature are not robust across countries and variable selections. Salimans, T. http://repub.eur.nl/res/pub/25640/ 2011-08-01T00:00:00Z We consider the problem of testing for seasonal unit roots in monthly panel data. To this aim, we generalize the quarterly cross-sectionally augmented Hylleberg-Engle-Granger-Yoo (CHEGY) test to the monthly case. This parametric test is contrasted with a new non-parametric test, which is the panel counterpart to the univariate record unit-root seasonal (RURS) test that relies on counting extrema in time series. All methods are applied to an empirical data set on tourism in Austrian provinces. The power properties of the tests are evaluated in simulation experiments that are tuned to the tourism data. Kunst, R.M. Franses, Ph.H.B.F. http://repub.eur.nl/res/pub/22337/ 2011-01-01T00:00:00Z Regression analyses of cross-country economic growth data are complicated by two main forms of model uncertainty: the uncertainty in selecting explanatory variables and the uncertainty in specifying the functional form of the regression function. Most discussions in the literature address these problems independently, yet a joint treatment is essential. We perform this joint treatment by extending the linear model to allow for multiple-regime parameter heterogeneity of the type suggested by new growth theory, while addressing the variable selection problem by means of Bayesian model averaging. Controlling for variable selection uncertainty, we confirm the evidence in favor of new growth theory presented in several earlier studies. However, controlling for functional form uncertainty, we find that the effects of many of the explanatory variables identified in the literature are not robust across countries and variable selections. Salimans, T. http://repub.eur.nl/res/pub/21723/ 2010-12-07T00:00:00Z This paper develops a new test, the trinomial test, for pairwise ordinal data samples to improve the power of the sign test by modifying its treatment of zero di®erences between observations, thereby increasing the use of sample information. Simulations demonstrate the power superiority of the proposed trinomial test statis- tic over the sign test in small samples in the presence of tie observations. We also show that the proposed trinomial test has substantially higher power than the sign test in large samples and also in the presence of tie observations, as the sign test ignores information from observations resulting in ties. Bian, G. McAleer, M.J. Wong, W-K. http://repub.eur.nl/res/pub/21727/ 2010-12-07T00:00:00Z This paper develops a new test, the trinomial test, for pairwise ordinal data samples to improve the power of the sign test by modifying its treatment of zero differences between observations, thereby increasing the use of sample information. Simulations demonstrate the power superiority of the proposed trinomial test statistic over the sign test in small samples in the presence of tie observations. We also show that the proposed trinomial test has substantially higher power than the sign test in large samples and also in the presence of tie observations, as the sign test ignores information from observations resulting in ties. Bian, G. McAleer, M.J. Wong, W-K. http://repub.eur.nl/res/pub/21926/ 2010-09-01T00:00:00Z We propose to estimate the parameters of the Market Share Attraction Model (Cooper and Nakanishi, 1988; Fok and Franses, 2004) in a novel way by using a nonparametric technique for function estimation called Support Vector Regressions (SVR) (Smola, 1996; Vapnik, 1995). Traditionally, the parameters of the Market Share Attraction Model are estimated via a Maximum Likelihood (ML) procedure, assuming that the data are drawn from a conditional Gaussian distribution. However, if the distribution is unknown, Ordinary Least Squares (OLS) estimation may seriously fail (Vapnik, 1982). One way to tackle this problem is to introduce a linear loss function over the errors and a penalty on the magnitude of model coefficients. This leads to qualities such as robustness to outliers and avoidance of the problem of overfitting. This kind of estimation forms the basis of the SVR technique, which, as we will argue, makes it a good candidate for estimating the Market Share Attraction Model. We test the SVR approach to predict (the evolution of) the market shares of 36 car brands simultaneously and report promising results. Nalbantov, G.I. Franses, Ph.H.B.F. Groenen, P.J.F. Bioch, J.C. http://repub.eur.nl/res/pub/18038/ 2010-02-08T00:00:00Z This paper examines the market efficiency of oil spot and futures prices by using a stochastic dominance (SD) approach. As there is no evidence of an SD relationship between oil spot and futures, we conclude that there is no arbitrage opportunity between these two markets, and that both market efficiency and market rationality are not rejected in the oil spot and futures markets. Lean, H.H. McAleer, M.J. Wong, W-K. http://repub.eur.nl/res/pub/17525/ 2009-12-10T00:00:00Z Bubbles can persist because investors are better off riding bubbles. We define bubbles in a natural way as significant, prolonged deviations from fundamental values measured by the well-known asset pricing models. Our real-time bubble detection system shows that –using US industry returns– periods of both higher volatility and higher abnormal returns follow noisy positive bubble signals. However, for the typical investor the risk-return trade-off improves. Riding bubbles generates annual abnormal returns of three to nine percent. These conclusions are robust to different assumptions and our system allows for alternative multifactor models as proxies for fundamental value. Günster, N.K. Kole, H.J.W.G. Jacobsen, B. http://repub.eur.nl/res/pub/17493/ 2009-11-01T00:00:00Z This paper describes a forecasting exercise of close-to-open returns on major global stock indices, based on price patterns from foreign markets that have become available overnight. As the close-to-open gap is a scalar response variable to a functional variable, it is natural to focus on functional data analysis. Both parametric and non-parametric modeling strategies are considered, and compared with a simple linear benchmark model. The overall best performing model is nonparametric, suggesting the presence of nonlinear relations between the overnight price patterns and the opening gaps. This effect is mainly due to the European and Asian markets. The North-American and Australian markets appear to be informationally more efficient in that linear models using only the last available information perform well. Gooijer, J.G. de Diks, C.G.H. Gatarek, L.T. http://repub.eur.nl/res/pub/19451/ 2009-11-01T00:00:00Z In this article, we put forward a generalization of the Dynamic Conditional Correlation (DCC) Model of Engle (2002). Our model allows for asset-specific correlation sensitivities, which is useful in particular if one aims to summarize a large number of asset returns. We propose two estimation methods, one based on a full likelihood maximization, the other on individual correlation estimates. The resultant generalized DCC (GDCC) model is considered for daily data on 39 U.K. stock returns in the FTSE. We find convincing evidence that the GDCC model improves on the DCC model and also on the CCC model of Bollerslev (1990). Hafner, C.M. Franses, Ph.H.B.F. http://repub.eur.nl/res/pub/14861/ 2009-02-19T00:00:00Z We consider the problem of testing for seasonal unit roots in monthly panel data. To this aim, we generalize the quarterly CHEGY test to the monthly case. This parametric test is contrasted with a new nonparametric test, which is the panel counterpart to the univariate RURS test that relies on counting extrema in time series. All methods are applied to an empirical data set on tourism in Austrian provinces. The power properties of the tests are evaluated in simulation experiments that are tuned to the tourism data. Kunst, R.M. Franses, Ph.H.B.F. http://repub.eur.nl/res/pub/1833/ 2009-01-28T00:00:00Z In this paper we examine the usefulness of multivariate semi-parametric GARCH models for evaluating the Value-at-Risk (VaR) of a portfolio with arbitrary weights. We specify and estimate several alternative multivariate GARCH models for daily returns on the S&P 500 and Nasdaq indexes. Examining the within sample VaRs of a set of given portfolios shows that the semi-parametric model performs uniformly well, while parametric models in several cases have unacceptable failure rates. Interestingly, distributional assumptions appear to have a much larger impact on the performance of the VaR estimates than the particular parametric specification chosen for the GARCH equations. Rombouts, J.V.K. Verbeek, M.J.C.M. http://repub.eur.nl/res/pub/14052/ 2008-11-01T00:00:00Z We introduce a statistical test for comparing the predictive accuracy of competing copula specifications in multivariate density forecasts, based on the Kullback-Leibler Information Criterion (KLIC). The test is valid under general conditions: in particular it allows for parameter estimation uncertainty and for the copulas to be nested or non-nested. Monte Carlo simulations demonstrate that the proposed test has satisfactory size and power properties in finite samples. Applying the test to daily exchange rate returns of several major currencies against the US dollar we find that the Student's t copula is favored over Gaussian, Gumbel and Clayton copulas. This suggests that these exchange rate returns are characterized by symmetric tail dependence. Diks, C.G.H. Panchenko, V. Dijk, D.J.C. van http://repub.eur.nl/res/pub/14051/ 2008-10-20T00:00:00Z We propose a quantification of the p-p plot that assigns equal weight to all distances between the respective distributions: the surface between the p-p plot and the diagonal. This surface is labelled the Harmonic Weighted Mass (HWM) index. We introduce the diagonal-deviation (d-d) plot that allows the index to be computed exactly under all circumstances. For two balanced samples absent ties the finite sample distribution of the HWM index is derived. Simulations show that in most cases unbalanced samples and ties have little effect on this distribution. The d-d plot allows for a straightforward extension to the K-sample HWM index. As we have not been able to derive the distribution of the index for K>2, we simulate significance tables for K=3,...,15. An example involving economic growth rates of the G7 countries illustrates that the HWM test can have better power than alternative Empirical Distribution Function tests. Hinloopen, J. Wagenvoort, R. Marrewijk, J.G.M. van http://repub.eur.nl/res/pub/10904/ 2008-01-15T00:00:00Z We introduce the realized co-range, utilizing intraday high-low price ranges to estimate asset return covariances. Using simulations we find that for plausible levels of bid-ask bounce and infrequent and non-synchronous trading the realized co-range improves upon the realized covariance, which uses cross-products of intraday returns. One advantage of the co-range is that in an ideal world it is five times more efficient than the realized covariance when sampling at the same frequency. The second advantage is that the upward bias due to bid-ask bounce and the downward bias due to infrequent and non-synchronous trading partially offset each other. In a volatility timing strategy for S\\&P500, bond and gold futures we find that the co-range estimates are less noisy as exemplified by lower transaction costs and also higher Sharpe ratios when using more weight on recent data for predicting covariances. Bannouh, K. Dijk, D.J.C. van Martens, M.P.E. http://repub.eur.nl/res/pub/11482/ 2008-01-01T00:00:00Z We propose an easy to use derivative based two-step estimation procedure for semiparametric index models. In the first step various functionals involving the derivatives of the unknown function are estimated using nonparametric kernel estimators. The functionals used provide moment conditions for the parameters of interest, which are used in the second step within a method-of-moments framework to estimate the parameters of interest. The estimator is shown to be root N consistent and asymptotically normal. We extend the procedure to multiple equation models. Our identification conditions and estimation framework provide natural tests for the number of indices in the model. In addition we discuss tests of separability, additivity, and linearity of the influence of the indices. Donkers, A.C.D. Schafgans, M. http://repub.eur.nl/res/pub/10751/ 2007-11-27T00:00:00Z The Dutch drinking water sector experienced two drastic changes over the last 10 years. Firstly, in 1997, the sector association started with a voluntary benchmarking aimed to increase the efficiency and effectiveness of the sector. Secondly, merger activity arose. This paper develops a tailored nonparametric model to dissect and distinguish the effects on efficiency of these two evolutions. In particular, we adapt Free Disposal Hull (FDH) to estimate robust and conditional non-oriented efficiency estimates. Parametric COLS (Fourier) tests show the robustness of the model with respect to the specification and its variables. We classify the merger economies into scale economies and increased incentives to fight inefficiencies. Although we detect a significant efficiency enhancing effect of benchmarking, we find insignificant merger economies due to the absence of scale economies and the absence of increased incentives to fight inefficiencies. Witte, K. de Dijkgraaf, E.