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on Forecasting |
| By: | Mawuli Segnon; Mark Trede |
| Abstract: | This paper proposes a new methodology for modeling and forecasting market risks of portfolios. It is based on a combination of copula functions and Markov switching multifractal (MSM) processes. We assess the performance of the copula-MSM model by computing the value at risk of a portfolio composed of the NASDAQ composite index and the S&P 500. Using the likelihood ratio (LR) test by Christofferrsen (1998), the GMM duration-based test by Candelon et al. (2011) and the superior predictive ability (SPA) test by Hansen (2005) we evaluate the predictive ability of the copula-MSM model and compare it to other common approaches such as historical simulation, variance-covariance, Risk-Metrics, copula-GARCH and constant conditional correlation GARCH (CCCGARCH) models. We find that the copula-MSM model is more robust, provides the best fit and outperforms the other models in terms of forecasting accuracy and VaR prediction. |
| Keywords: | Copula, Multifractal processes, GARCH, VaR, Backtesting, SPA |
| JEL: | G17 C02 |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:cqe:wpaper:6617&r=for |
| By: | Fossati, Sebastian (University of Alberta, Department of Economics) |
| Abstract: | This paper proposes a new test for comparing the out-of sample forecasting performance of two competing models for situations in which the predictive content may be state-dependent (for example, expansion and recession states or low and high volatility states). To apply this test the econometrician is not required to observe when the underlying states shift. The test is simple to implement and accommodates several different cases of interest. An out-of-sample forecasting exercise for US output growth using real-time data illustrates the improvement of this test over previous approaches to perform forecast comparison. |
| Keywords: | Forecast Evaluation; Testing; Regime Switching; Structural Change |
| JEL: | C22 C53 |
| Date: | 2017–09–06 |
| URL: | http://d.repec.org/n?u=RePEc:ris:albaec:2017_009&r=for |
| By: | Tingting Cheng; Jiti Gao; Oliver Linton |
| Abstract: | In this paper, we propose three new predictive models: the multi-step nonparametric predictive regression model and the multi-step additive predictive regression model, in which the predictive variables are locally stationary time series; and the multi-step time-varying coefficient predictive regression model, in which the predictive variables are stochastically nonstationary. We also establish the estimation theory and asymptotic properties for these models in the short horizon and long horizon case. To evaluate the effectiveness of these models, we investigate their capability of stock return prediction. The empirical results show that all of these models can substantially outperform the traditional linear predictive regression model in terms of both in-sample and out-of-sample performance. In addition, we find that these models can always beat the historical mean model in terms of in-sample fitting, and also for some cases in terms of the out-of-sample forecasting. |
| Keywords: | Kernel estimator, locally stationary process, series estimator, stock return prediction. |
| JEL: | C14 C22 G17 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2017-13&r=for |
| By: | Gloria Gonzalez-Rivera (Department of Economics, University of California Riverside); Joao Henrique Mazzeu (UC3M); Esther Ruiz (UC3M); Helena Veiga (UC3M) |
| Abstract: | We propose an extension of the Generalized Autocontour (G-ACR) tests for dynamic specification of in-sample conditional densities and for evaluation of out-of-sample forecast densities. The new tests are based on probability integral transforms (PITs) computed from bootstrap conditional densities that incorporate parameter uncertainty. Then, the parametric specification of the conditional moments can be tested without relying on any parametric error distribution yet exploiting distributional properties of the variable of interest. We show that the finite sample distribution of the bootstrapped G-ACR (BG-ACR) tests are well approximated using standard asymptotic distributions. Furthermore, the proposed tests are easy to implement and are accompanied by graphical tools that provide information about the potential sources of misspecification. We apply the BG-ACR tests to the Heterogeneous Autoregressive (HAR) model and the Multiplicative Error Model (MEM) of the U.S. volatility index VIX. We find strong evidence against the parametric assumptions of the conditional densities, i.e. normality in the HAR model and semi non-parametric Gamma (GSNP) in the MEM. In both cases, the true conditional density seems to be more skewed to the right and more peaked than either normal or GSNP densities, with location, variance and skewness changing over time. The preferred specification is the heteroscedastic HAR model with bootstrap conditional densities of the log-VIX. |
| Keywords: | Distribution Uncertainty; Model Evaluation; Parameter Uncertainty; PIT; VIX; HAR model; Multiplicative Error Model |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:ucr:wpaper:201709&r=for |
| By: | Alireza Ermagun; David Levinson (School of Civil Engineering, University of Sydney) |
| Abstract: | This study examines the dependency between traffic links using a three-dimensional data detrending algorithm to build a network weight matrix in a real-world example. The network weight matrix reveals how links are spatially dependent in a complex network and detects the competi- tive and complementary nature of traffic links. We model the traffic flow of 140 traffic links in a sub-network of the Minneapolis - St. Paul highway system for both rush hour and non-rush hour time intervals, and validate the extracted network weight matrix. The results of the modeling indi- cate: (1) the spatial weight matrix is unstable over time-of-day, while the network weight matrix is robust in all cases and (2) the performance of the network weight matrix in non-rush hour traffic regimes is significantly better than rush hour traffic regimes. The results of the validation show the network weight matrix outperforms the traditional way of capturing spatial dependency between traffic links. Averaging over all traffic links and time, this superiority is about 13.2% in rush hour and 15.3% in non-rush hour, when only the 1st -order neighboring links are embedded in modeling. Aside from the superiority in forecasting, a remarkable capability of the network weight matrix is its stability and robustness over time, which is not observed in spatial weight matrix. In addition, this study proposes a naïve two-step algorithm to search and identify the best look-back time win- dow for upstream links. We indicate the best look-back time window depends on the travel time between two study detectors, and it varies by time-of-day and traffic link. |
| Keywords: | Traffic Forecasting; Spatial correlation; Competitive links; Traffic Flow; Weight matrix |
| JEL: | R41 R48 C21 C31 C33 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:nex:wpaper:shorttermtrafficforecasting&r=for |
| By: | David Harris; Gael M. Martin; Indeewara Perera; Don S. Poskitt |
| Abstract: | The focus of this paper is on the quantification of sampling variation in frequentist probabilistic forecasts. We propose a method of constructing confidence sets that respects the functional nature of the forecast distribution, and use animated graphics to visualize the impact of parameter uncertainty on the location, dispersion and shape of the distribution. The confidence sets are derived via the inversion of a Wald test and are asymptotically uniformly most accurate and, hence, optimal in this sense. A wide range of linear and non-linear time series models - encompassing long memory, state space and mixture specifications - is used to demonstrate the procedure, based on artificially generated data. An empirical example in which distributional forecasts of both financial returns and its stochastic volatility are produced is then used to illustrate the practical importance of accommodating sampling variation in the manner proposed. |
| Keywords: | probabilistic forecasts, asymptotically uniformly most accurate confidence regions, time series models, animated graphics, realized volatility, heterogeneous autoregressive model. |
| JEL: | C13 C18 C53 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2017-9&r=for |
| By: | Alireza Ermagun; Snigdhansu Chatterjee; David Levinson (Nexus (Networks, Economics, and Urban Systems) Research Group, Department of Civil Engineering, University of Minnesota) |
| Abstract: | This empirical study sheds light on the spatial correlation of traffic links under different traffic regimes. We mimic the behavior of real traffic by pinpointing the spatial correlation between 140 freeway traffic links in a major sub-network of the Minneapolis—St. Paul freeway system with a grid-like network topology. This topology enables us to juxtapose the positive and negative correlation between links, which has been overlooked in short-term traffic forecasting models. To accurately and reliably measure the correlation between traffic links, we develop an algorithm that eliminates temporal trends in three dimensions: (1) hourly dimension, (2) weekly dimension, and (3) system dimension for each link. The spatial correlation of traffic links exhibits a stronger negative correlation in rush hours, when congestion affects route choice. Although this correlation occurs mostly in parallel links, it is also observed upstream, where travelers receive information and are able to switch to substitute paths. Irrespective of the time-of-day and day-of-week, a strong positive correlation is witnessed between upstream and downstream links. This correlation is stronger in uncongested regimes, as traffic flow passes through consecutive links more quickly and there is no congestion effect to shift or stall traffic. The extracted spatial correlation structure can augment the accuracy of short-term traffic forecasting models. |
| Keywords: | statistics, spatial weight matrix, traffic forecasting, network weight matrix Publication status: Published in PLoS One. 12(5): e0176853. |
| JEL: | R41 R48 C21 C31 C33 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:nex:wpaper:detrending&r=for |
| By: | George Kapetanios; Simon Price; Garry Young |
| Abstract: | A financial conditions index (FCI) is designed to summarise the state of financial markets. We construct two with UK data. The first is the first principal component (PC) of a set of financial indicators. The second comes from a new approach taking information from a large set of macroeconomic variables weighted by the joint covariance with a subset of the financial indicators (a set of spreads), using multivariate partial least squares, again using the first factor. The resulting FCIs are broadly similar. They both have some forecasting power for monthly GDP in a quasi-real-time recursive evaluation from 2011-2014 and outperform an FCI produced by Goldman Sachs. A second factor that may be interpreted as a monetary conditions index adds further forecast power, while third factors have a mixed effect on performance. The FCIs are used to improve identification of credit supply shocks in an SVAR. The main effects relative to an SVAR excluding an FCI of the (adverse) credit shock IRFs are to make the positive impact on inflation more precise and to reveal an increased positive impact on spreads. |
| Keywords: | Forecasting, Financial conditions index, Targeted data reduction, Multivariate partial least squares, Credit shocks |
| JEL: | C53 |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2017-58&r=for |