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on All new papers |
| By: | Sofiane Aboura; Julien Chevallier |
| Abstract: | This article adopts the asymmetric DCC with one exogenous variable (ADCCX) model developed by Vargas (2008), by updating the concept of ‘volatility surprise’ to capture cross-market relationships. Current methods for measuring spillovers do not focus on volatility interactions, and neglect cross-effects between the conditional variances. This paper aims to fill this gap. The dataset includes four aggregate indices representing equities, bonds, foreign exchange rates and commodities from 1983 to 2013. The results provide strong evidence of spillover effects coming from the ‘volatility surprise’ component across markets. Against the background of the recent financial crisis, the aim is to contribute to the literature on the interdependencies of financial markets, both in conditional means and (co)variances. In addition, asset management implications are derived. |
| Keywords: | Cross-market relationships, Volatility surprise, Volatility spillover, ADCCX, Asset management. |
| JEL: | C32 C4 G15 |
| Date: | 2014–08–29 |
| URL: | http://d.repec.org/n?u=RePEc:ipg:wpaper:2014-469&r=ets |
| By: | Hossein Hassani; Emmanuel Sirimal Silva; Rangan Gupta |
| Abstract: | This paper seeks to evaluate the appropriateness of a variety of existing forecasting techniques (17 methods) at providing accurate, and statistically significant forecasts for gold price. We report the results from the 9 most competitive techniques. Special consideration is given to the ability of these techniques at providing forecasts which outperforms the random walk as we noticed that certain multivariate models (which included prices of silver, platinum, palladium and rhodium, besides gold) were also unable to outperform the random walk in this case. Interestingly, the results show that none of the forecasting techniques are able to outperform the random walk at horizons of 1 and 9 steps ahead, and on average the Exponential Smoothing model is seen providing the best forecasts in terms of the lowest root mean squared error over the 24 months forecasting horizons. Moreover, we find that the univariate models used in this paper are able to outperform the Bayesian autoregression, and Bayesian vector autoregressive models, with exponential smoothing (ETS) reporting statistically significant results in comparison to the former models, and classical autoregressive and the vector autoregressive models in most cases. |
| Keywords: | ARIMA; ETS; TBATS; ARFIMA; AR; VAR; BAR; BVAR; Random Walk; Gold; Forecast; Multivariate; Univariate. |
| Date: | 2014–08–29 |
| URL: | http://d.repec.org/n?u=RePEc:ipg:wpaper:2014-480&r=ets |
| By: | Anandamayee Majumdar (Center of Advanced Statistics and Econometrics, Soochow University, China); Rangan Gupta (Department of Economics, University of Pretoria) |
| Abstract: | Given the existence of non-normality and nonlinearity in the data generating process of real house price returns over the period of 1831-2013, this paper compares the ability of various univariate copula models, relative to standard benchmarks (naive and autoregressive models) in forecasting real US house price over the annual out-of-sample period of 1859-2013, based on an in-sample of 1831-1858. Overall, our results provide overwhelming evidence in favor of the copula models (Normal, Student’s t, Clayton, Frank, Gumbel, Joe and Ali-Mikhail-Huq) relative to linear benchmarks, and especially for the Student’s t copula, which outperforms all other models both in terms of in-sample and out-of-sample predictability results. Our results highlight the importance of accounting for non-normality and nonlinearity in the data generating process of real house price returns for the US economy for nearly two centuries of data. |
| Keywords: | House Price, Copula Models, Forecasting |
| JEL: | C22 C53 R3 |
| Date: | 2014–08 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201444&r=ets |
| By: | Su, EnDer |
| Abstract: | This paper aims to study the structural tail dependences and risk magnitude of contagion risk during high risk state between domestic and foreign banks. Empirically, volatility of stock returns has the properties of persistence, clustering, heteroscedasticity, and regime switchs. Thus, the threshold regression model having piecewise regression capability is used to classify the volatility index of influential foreign banks as “high” and “low” of two volatility states to further estimate Kendall taus i.e. structural tail dependences between banks using three models: Gaussian, t, and Clay copula GARCH. Using fuzzy c-means method, both domestic and foreign banks are categorized into 10 groups. Through the groups, 5 domestic and 7 foreign representative banks are identified as the research objects. Then, the in-sample data of daily banks’ stock prices covering 01/03/2003 ~06/30/2006 are used to estimate the parameters of threshold copula GARCH model and Kendall taus. The out-of-sample data covering 07/01/2006~03/25/2014 are used to estimate the Kendall taus gradually using rolling window technique. Several research findings are described as follows. In high state, the tail dependences are two times much larger than in low state and most of them have up-trend property after sub-prime crisis and reach the peak during Greek debt. It implies that the volatility is high in risk event and the contagion is high after risk event. In high state, HNC has the highest tail dependences with foreign banks but its value at risk is the lowest. It can be considered as an international attribute bank with lower risk. On the contrary, YCB and FCB have the lower tail dependences with foreign banks but their value at risks are quite high. They are viewed as a local attribute bank with higher risk. The Bank of American, Citigroup, and UBS AG have the relatively higher value at risk. Citigroup has been tested to Granger cause ANZ and all domestic banks. It is necessary to beware the contagion risk from Citigroup. Among three models, in low state, Gaussian and t copula models have the better significance of estimation than Clay copula model. However in high state, Clay copula model has the same acceptable estimation and more capability to uncover the instant nonlinear jumps of tail dependences while Gaussian and t copula models reveal the linear changes of tail dependences as a curve. |
| Keywords: | Contagion Risk, Threshold GARCH, Copula, Tail Dependences |
| JEL: | C00 G10 |
| Date: | 2014–08–26 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:58161&r=ets |
| By: | Götz T.B.; Hecq A.W. (GSBE) |
| Abstract: | In this paper we analyze Granger causality testing in a mixed-frequency VAR, originally proposed by Ghysels 2012, where the difference in sampling frequencies of the variables is large. In particular, we investigate whether past information on a low-frequency variable help in forecasting a high-frequency one and vice versa. Given a realistic sample size, the number of high-frequency observations per low-frequency period leads to parameter proliferation problems in case we attempt to estimate the model unrestrictedly. We propose two approaches to solve this problem, reduced rank restrictions and a Bayesian mixed-frequency VAR. For the latter, we extend the approach in Banbura et al. 2010 to a mixed-frequency setup, which presents an alternative to classical Bayesian estimation techniques. We compare these methods to a common aggregated low-frequency model as well as to the unrestricted VAR in terms of their Granger non-causality testing behavior using Monte Carlo simulations. The techniques are illustrated in an empirical application involving dailyrealized volatility and monthly business cycle fluctuations. |
| Keywords: | Hypothesis Testing: General; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; |
| JEL: | C12 C32 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:unm:umagsb:2014028&r=ets |
| By: | Tsangyao Chang; Goodness C. Aye; Rangan Gupta |
| Abstract: | In this study, we apply a new recursive test proposed by Philips et al (2013) to investigate whether there exist multiple bubbles in the BRICS (Brazil, Russia, India, China and South Africa) stock markets, using monthly data on stock price-dividend ratio. Our empirical results, the first of its kind for these economies, indicate that there did exist multiple bubbles in the stock markets of the BRICS. Further, the dates of the bubbles also corresponded to specific events in the stocks markets of these economies. This finding has important economic and policy implications. |
| Keywords: | Multiple bubbles; BRICS stock markets; GSADF test |
| JEL: | C12 C15 G12 G15 |
| Date: | 2014–08–29 |
| URL: | http://d.repec.org/n?u=RePEc:ipg:wpaper:2014-462&r=ets |