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on Econometric Time Series |
| By: | Paulo M.M. Rodrigues; Nazarii Salish |
| Abstract: | Over recent years several methods to deal with high-frequency data (economic, financial and other) have been proposed in the literature. An interesting example is for instance interval valued time series described by the temporal evolution of high and low prices of an asset. In this paper a new class of threshold models capable of capturing asymmetric e¤ects in interval-valued data is introduced as well as new forecast loss functions and descriptive statistics of the forecast quality proposed. Least squares estimates of the threshold parameter and the regression slopes are obtained; and forecasts based on the proposed threshold model computed. A new forecast procedure based on the combination of this model with the k nearest neighbors method is introduced. To illustrate this approach, we report an application to a weekly sample of S&P500 index returns. The results obtained are encouraging and compare very favorably to available procedures.<br> |
| JEL: | C12 C22 C52 C53 |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:ptu:wpaper:w201128&r=ets |
| By: | Bruno Feunou; Roméo Tedongap |
| Abstract: | We develop a discrete-time affine stochastic volatility model with time-varying conditional skewness (SVS). Importantly, we disentangle the dynamics of conditional volatility and conditional skewness in a coherent way. Our approach allows current asset returns to be asymmetric conditional on current factors and past information, what we term contemporaneous asymmetry. Conditional skewness is an explicit combination of the conditional leverage effect and contemporaneous asymmetry. We derive analytical formulas for various return moments that are used for generalized method of moments estimation. Applying our approach to S&P500 index daily returns and option data, we show that one- and two-factor SVS models provide a better fit for both the historical and the risk-neutral distribution of returns, compared to existing affine generalized autoregressive conditional heteroskedasticity (GARCH) models. Our results are not due to an overparameterization of the model: the one-factor SVS models have the same number of parameters as their one-factor GARCH competitors. |
| Keywords: | Asset Pricing; Econometric and statistical methods |
| JEL: | C1 C5 G1 G12 |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:11-20&r=ets |
| By: | Legerstee, R.; Franses, Ph.H.B.F.; Paap, R. |
| Abstract: | Experts can rely on statistical model forecasts when creating their own forecasts.Usually it is not known what experts actually do. In this paper we focus on threequestions, which we try to answer given the availability of expert forecasts andmodel forecasts. First, is the expert forecast related to the model forecast andhow? Second, how is this potential relation influenced by other factors? Third,how does this relation influence forecast accuracy?We propose a new and innovative two-level Hierarchical Bayes model to answerthese questions. We apply our proposed methodology to a large data set offorecasts and realizations of SKU-level sales data from a pharmaceutical company.We find that expert forecasts can depend on model forecasts in a variety ofways. Average sales levels, sales volatility, and the forecast horizon influence thisdependence. We also demonstrate that theoretical implications of expert behavioron forecast accuracy are reflected in the empirical data. |
| Keywords: | endogeneity;Bayesian analysis;expert forecasts;model forecasts;forecast adjustment |
| Date: | 2011–09–30 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:eureir:1765026660&r=ets |
| By: | Lorenzo Pascual; Esther Ruiz; Diego Fresoli |
| Abstract: | In this paper, we show how to simplify the construction of bootstrap prediction densities in multivariate VAR models by avoiding the backward representation. Bootstrap prediction densities are attractive because they incorporate the parameter uncertainty a any particular assumption about the error distribution. What is more, the construction of densities for more than one-step unknown asymptotically. The main advantage of the new simple without loosing the good performance of bootstrap procedures. Furthermore, by avoiding a backward representation, its asymptotic validity can be proved without relying on the assumption of Gaussian errors as proposed in this paper can be implemented to obtain prediction densities in models without a backward representation as, for example, models with MA components or GARCH disturbances. By comparing the finite sample performance of the proposed procedure with those of alternatives, we show that nothing is lost when using it. Finally, we implement the procedure to obtain prediction regions for US quarterly future inflation, unemployment and GDP growth |
| Keywords: | Non-Gaussian VAR models, Prediction cubes, Prediction density, Prediction regions, Prediction ellipsoids, Resampling methods |
| Date: | 2011–10 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:ws113426&r=ets |
| By: | Yonghui Zhang (School of Economics, Singapore Management University); Liangjun Su (School of Economics, Singapore Management University); Peter C.B. Phillips (Cowles Foundation, Yale University) |
| Abstract: | This paper proposes a nonparametric test for common trends in semiparametric panel data models with fixed effects based on a measure of nonparametric goodness-of-fit (R^2). We first estimate the model under the null hypothesis of common trends by the method of profile least squares, and obtain the augmented residual which consistently estimates the sum of the fixed effect and the disturbance under the null. Then we run a local linear regression of the augmented residuals on a time trend and calculate the nonparametric R^2 for each cross section unit. The proposed test statistic is obtained by averaging all cross sectional nonparametric R^2's, which is close to zero under the null and deviates from zero under the alternative. We show that after appropriate standardization the test statistic is asymptotically normally distributed under both the null hypothesis and a sequence of Pitman local alternatives. We prove test consistency and propose a bootstrap procedure to obtain p-values. Monte Carlo simulations indicate that the test performs well in finite samples. Empirical applications are conducted exploring the commonality of spatial trends in UK climate change data and idiosyncratic trends in OECD real GDP growth data. Both applications reveal the fragility of the widely adopted common trends assumption. |
| Keywords: | Common trends, Local polynomial estimation, Nonparametric goodness-of-fit, Panel data, Profile least squares |
| JEL: | C12 C14 C23 |
| Date: | 2011–10 |
| URL: | http://d.repec.org/n?u=RePEc:cwl:cwldpp:1832&r=ets |
| By: | Thomas Lux; Leonardo Morales-Arias; Cristina Sattarhoff |
| Abstract: | The volatility specification of the Markov-switching Multifractal (MSM) model is proposed as an alternative mechanism for realized volatility (RV). We estimate the RV-MSM model via Generalized Method of Moments and perform forecasting by means of best linear forecasts derived via the Levinson-Durbin algorithm. The out-of-sample performance of the RV-MSM is compared against other popular time series specfications usually employed to model the dynamics of RV as well as other standard volatility models of asset returns. An intra-day data set for five major international stock market indices is used to evaluate the various models out-of-sample. We find that the RV-MSM seems to improve upon forecasts of its baseline MSM counterparts and many other volatility models in terms of mean squared errors (MSE). While the more conventional RV-ARFIMA model comes out as the most successful model (in terms of the number of cases in which it has the best forecasts for all combinations of forecast horizons and criteria), the new RV-MSM model seems often very close in its performance and in a non-negligible number of cases even dominates over the RV-ARFIMA model |
| Keywords: | Realized volatility, multiplicative volatility models, long memory, international volatility forecasting |
| JEL: | C20 G12 |
| Date: | 2011–10 |
| URL: | http://d.repec.org/n?u=RePEc:kie:kieliw:1737&r=ets |
| By: | Pesaran, M.H.; Pick, A.; Pranovich, M. |
| Abstract: | This paper considers the problem of forecasting under continuous and discrete structural breaks and proposes weighting observations to obtain optimal forecasts in the MSFE sense. We derive optimal weights for continuous and discrete break processes. Under continuous breaks, our approach recovers exponential smoothing weights. Under discrete breaks, we provide analytical expressions for the weights in models with a single regressor and asympotically for larger models. It is shown that in these cases the value of the optimal weight is the same across observations within a given regime and differs only across regimes. In practice, where information on structural breaks is uncertain a forecasting procedure based on robust weights is proposed. Monte Carlo experiments and an empirical application to the predictive power of the yield curve analyze the performance of our approach relative to other forecasting methods. |
| JEL: | C22 C53 |
| Date: | 2011–10–31 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:1163&r=ets |