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on Forecasting |
| By: | Benjamin Beckers; Helmut Herwartz; Moritz Seidel |
| Abstract: | The GARCH(1,1) model and its extensions have become a standard econometric tool for modeling volatility dynamics of financial returns and port-folio risk. In this paper, we propose an adjustment of GARCH implied conditional value-at-risk and expected shortfall forecasts that exploits the predictive content of uncorrelated, yet dependent model innovations. The adjustment is motivated by non-Gaussian characteristics of model residuals, and is implemented in a semiparametric fashion by means of conditional moments of simulated bivariate standardized copula distributions. We conduct in-sample forecasting comparisons for a set of 18 stock market indices. In total, four competing copula-GARCH models are contrasted against each other on the basis of their one-step ahead forecasting performance. With regard to forecast unbiasedness and precision, especially the Frank-GARCH models provide most conservative risk forecasts and out-perform all rival models. |
| Keywords: | copula distributions, expected shortfall, GARCH, model selection, non-Gaussian innovations, risk forecasting, value-at-risk |
| JEL: | C22 C51 C52 C53 G32 |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:diw:diwwpp:dp1282&r=for |
| By: | Eran Raviv (Erasmus University Rotterdam) |
| Abstract: | When the yield curve is modelled using an affine factor model, residuals may still contain relevant information and do not adhere to the familiar white noise assumption. This paper proposes a pragmatic way to improve out of sample performance for yield curve forecasting. The proposed adjustment is illustrated via a pseudo out-of-sample forecasting exercise implementing the widely used Dynamic Nelson Siegel model. Large improvement in forecasting performance is achieved throughout the curve for different forecasting horizons. Results are robust to different time periods, as well as to different model specifications. |
| Keywords: | Yield curve; Nelson Siegel; Time varying loadings; Factor models |
| JEL: | E43 E47 G17 |
| Date: | 2013–03–07 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:20130041&r=for |
| By: | Abounoori, Abbas Ali; Naderi, Esmaeil; Gandali Alikhani, Nadiya; Amiri, Ashkan |
| Abstract: | The design of models for time series forecasting has found a solid foundation on statistics and mathematics. On this basis, in recent years, using intelligence-based techniques for forecasting has proved to be extremely successful and also is an appropriate choice as approximators to model and forecast time series, but designing a neural network model which provides a desirable forecasting is the main concern of researchers. For this purpose, the present study tries to examine the capabilities of two sets of models, i.e., those based on artificial intelligence and regressive models. In addition, fractal markets hypothesis investigates in daily data of the Tehran Stock Exchange (TSE) index. Finally, in order to introduce a complete design of a neural network for modeling and forecasting of stock return series, the long memory feature and dynamic neural network model were combined. Our results showed that fractal markets hypothesis was confirmed in TSE; therefore, it can be concluded that the fractal structure exists in the return of the TSE series. The results further indicate that although dynamic artificial neural network model have a stronger performance compared to ARFIMA model, taking into consideration the inherent features of a market and combining it with neural network models can yield much better results. |
| Keywords: | Stock Return, Long Memory, NNAR, ARFIMA, Hybrid Models |
| JEL: | C22 C45 C53 G10 |
| Date: | 2013–01–17 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:45615&r=for |
| By: | Bent Flyvbjerg; Mette Skamris Holm; S{\o}ren L. Buhl |
| Abstract: | This article presents results from the first statistically significant study of traffic forecasts in transportation infrastructure projects. The sample used is the largest of its kind, covering 210 projects in 14 nations worth US$59 billion. The study shows with very high statistical significance that forecasters generally do a poor job of estimating the demand for transportation infrastructure projects. The result is substantial downside financial and economic risks. Such risks are typically ignored or downplayed by planners and decision makers, to the detriment of social and economic welfare. For nine out of ten rail projects passenger forecasts are overestimated; average overestimation is 106 percent. This results in large benefit shortfalls for rail projects. For half of all road projects the difference between actual and forecasted traffic is more than plus/minus 20 percent. Forecasts have not become more accurate over the 30-year period studied. If techniques and skills for arriving at accurate demand forecasts have improved over time, as often claimed by forecasters, this does not show in the data. The causes of inaccuracy in forecasts are different for rail and road projects, with political causes playing a larger role for rail than for road. The cure is transparency, accountability, and new forecasting methods. The challenge is to change the governance structures for forecasting and project development. The article shows how planners may help achieve this. |
| Date: | 2013–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1303.6654&r=for |
| By: | David Ardia (University Lavalle, Quebec, Canada); Lennart Hoogerheide (VU University Amsterdam) |
| Abstract: | We analyze the impact of the estimation frequency - updating parameter estimates on a daily, weekly, monthly or quarterly basis - for commonly used GARCH models in a large-scale study, using more than twelve years (2000-2012) of daily returns for constituents of the S&P 500 index. We assess the implication for one-day ahead 95% and 99% Value-at-Risk (VaR) forecasts with the test for correct conditional coverage of Christoffersen (1998) and for Expected Shortfall (ES) forecasts with the block-bootstrap test of ES violations of Jalal and Rockinger (2008). Using the false discovery rate methodology of Storey (2002) to estimate the percentage of stocks for which the model yields correct VaR and ES forecasts, we reach the following conclusions. First, updating the parameter estimates of the GARCH equation on a daily frequency improves only marginally the performance of the model, compared with weekly, monthly or even quarterly updates. The 90% confidence bands overlap, reflecting that the performance is not significantly different. Second, the asymmetric GARCH model with non-parametric kernel density estimate performs well; it yields correct VaR and ES forecasts for an estimated 90% to 95% of the S&P 500 constituents. Third, specifying a Student-<I>t</I> (or Gaussian) innovations' density yields substantially and significantly worse forecasts, especially for ES. In sum, the somewhat more advanced model with infrequently updated parameter estimates yields much better VaR and ES forecasts than simpler models with daily updated parameter estimates. |
| Keywords: | GARCH; Value-at-Risk; Expected Shortfall; equity; frequency; false discovery rate |
| JEL: | C12 C22 C58 G17 G32 |
| Date: | 2013–03–21 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:20130047&r=for |
| By: | Bruce Abramson |
| Abstract: | Belief networks are a new, potentially important, class of knowledge-based models. ARCO1, currently under development at the Atlantic Richfield Company (ARCO) and the University of Southern California (USC), is the most advanced reported implementation of these models in a financial forecasting setting. ARCO1's underlying belief network models the variables believed to have an impact on the crude oil market. A pictorial market model-developed on a MAC II- facilitates consensus among the members of the forecasting team. The system forecasts crude oil prices via Monte Carlo analyses of the network. Several different models of the oil market have been developed; the system's ability to be updated quickly highlights its flexibility. |
| Date: | 2013–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1303.5703&r=for |
| By: | Natalia Nehrebecka; Aneta Maria Dzik (Faculty of Economic Sciences, University of Warsaw) |
| Abstract: | The paper presents a model assigning a bankruptcy probability to a company, developed on the basis of individual data from balance sheets and income statements of Polish companies, collected by Central Statistical Office of Poland in the 2001 – 2010 period. Determinants for warning signals for bankruptcies were examined together with the possibilities of early identification of such signals. The research was based on a logistic regression performed on categorized variables transformed using a weight of evidence approach. Scoring methods were used to create an indicator for grading the companies in the case of bankruptcies. In the forecasting model of a possible bankruptcy in a year's horizon the highest weight was assigned to the indicator for the ability to cover financial costs which explained the company's ability to meet the interest payments and capital costs. Indebtedness, share of cash reserves in assets and sales’ revenues were considered in forecasting bankruptcies information regarding liquidity. Taking into account the direction of sales, the specialized exporters were least probable to go bankrupt. In the more generalized model which accounts for the macroeconomic situation the most important was the indicator for the ability to pay off debt. In the model forecasting bankruptcies three-years in advance - the early warning model - no dominant indicator was found. Weights of 20% were assigned to the indicators of liquidity, current assets turnover and the return on sales. |
| Keywords: | firm survival, micro-data, Polish companies, scoring methods |
| JEL: | L11 L25 G33 M13 |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:war:wpaper:2013-08&r=for |
| By: | P. Manasse; R. Savona; M. Vezzoli |
| Abstract: | This paper employs a recent statistical algorithm (CRAGGING) in order to build an early warning model for banking crises in emerging markets. We perturb our data set many times and create “artificial” samples from which we estimated our model, so that, by construction, it is flexible enough to be applied to new data for out-of-sample prediction. We find that, out of a large number (540) of candidate explanatory variables, from macroeconomic to balance sheet indicators of the countries’ financial sector, we can accurately predict banking crises by just a handful of variables. Using data over the period from 1980 to 2010, the model identifies two basic types of banking crises in emerging markets: a “Latin American type”, resulting from the combination of a (past) credit boom, a flight from domestic assets, and high levels of interest rates on deposits; and an “Asian type”, which is characterized by an investment boom financed by banks’ foreign debt. We compare our model to other models obtained using more traditional techniques, a Stepwise Logit, a Classification Tree, and an “Average” model, and we find that our model strongly dominates the others in terms of out-of-sample predictive power. |
| JEL: | E44 G01 G21 |
| Date: | 2013–03 |
| URL: | http://d.repec.org/n?u=RePEc:bol:bodewp:wp872&r=for |