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on Forecasting |
| By: | Eickmeier, Sandra; Ziegler, Christina |
| Abstract: | This paper surveys existing factor forecast applications for real economic activity and inflation by means of a meta-analysis and contributes to the current debate on the determinants of the forecast performance of large-scale dynamic factor models relative to other models. We find that, on average, factor forecasts are slightly better than other models’ forecasts. In particular, factor models tend to outperform small-scale models, whereas they perform slightly worse than alternative methods which are also able to exploit large datasets. Our results further suggest that factor forecasts are better for US than for UK macroeconomic variables, and that they are better for US than for euro-area output; however, there are no significant differences between the relative factor forecast performance for US and euro-area inflation. There is also some evidence that factor models are better suited to predict output at shorter forecast horizons than at longer horizons. These findings all relate to the forecasting environment (which cannot be influenced by the forecasters). Among the variables capturing the forecasting design (which can, by contrast, be influenced by the forecasters), the size of the dataset from which factors are extracted seems to positively affect the relative factor forecast performance. There is some evidence that quarterly data lend themselves better to factor forecasts than monthly data. Rolling forecasts are preferable to recursive forecasts. The factor estimation technique seems to matter as well. Other potential determinants - namely whether forecasters rely on a balanced or an unbalanced panel, whether restrictions implied by the factor structure are imposed in the forecasting equation or not and whether an iterated or a direct multi-step forecast is made - are found to be rather irrelevant. Moreover, we find no evidence that pre-selecting the variables to be included in the panel from which factors are extracted helped to improve factor forecasts in the past. |
| Keywords: | Factor models, forecasting, meta-analysis |
| JEL: | C2 C3 E37 |
| Date: | 2006 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdp1:5170&r=for |
| By: | Naohito Abe; Yessica C.Y. Chung |
| Abstract: | This study investigates the determinants of companies' voluntary information disclosure. Employing a large and unique dataset on the companies' own earnings forecasts and their frequencies, we conducted an empirical analysis of the effects of a firm's ownership, board, and capital structures on information disclosure. Our finding is consistent with the hypothesis that the custom of cross-holding among companies strengthens entrenchment by managers. We also find that bank directors force managers to disclose information more frequently. In addition, our results show the borrowing ratio is positively associated with information frequency, suggesting that the manager is likely to reveal more when his or her firm borrows money from financial institutions. However, additional borrowings beyond the minimum level of effective borrowings decrease the management's disclosing incentive. |
| Keywords: | Voluntary information Disclosure, Corporate Governance, management earnings forecast |
| JEL: | G10 G14 G18 |
| Date: | 2007–01 |
| URL: | http://d.repec.org/n?u=RePEc:hst:hstdps:d06-203&r=for |
| By: | Stanislav Anatolyev (New Economic School); Nikolay Gospodinov (Concordia University) |
| Abstract: | While the predictability of excess stock returns is statistically small, their sign and volatility exhibit a substantially larger degree of dependence over time. We capitalize on this observation and consider prediction of excess stock returns by decomposing the equity premium into a product of sign and absolute value components and carefully modeling the marginal predictive densities of the two parts. Then we construct the joint density of a positively valued (absolute returns) random variable and a discrete binary (sign) random variable by copula methods and discuss computation of the conditional mean predictor. Our empirical analysis of US stock return data shows among other interesting ndings that despite the large unconditional correlation between the two multiplicative components they are conditionally very weakly dependent. |
| Keywords: | Stock returns predictability; Directional forecasting; Absolute returns; Joint predictive distribution; Copulas. |
| Date: | 2007–01 |
| URL: | http://d.repec.org/n?u=RePEc:cfr:cefirw:w0095&r=for |
| By: | Massimiliano Caporin (Department of Economics, Università di Padova); Domenico Sartore (Department of Economics, University Of Venice Ca’ Foscari) |
| Abstract: | This paper provides the theoretical and operational framework for estimating past values of relevant time series starting from a (limited) information set. We consider a general approach that includes as special cases time series aggregation and temporal and/or spatial disaggregation problems. Furthermore, we explore the relevant problems and the possible solutions associated with a retropolation exercise, evidencing that linear models could be the preferred representation for the production of the needed data. The methodology is designed with a focus on economic time series but it could be considered even for other statistical areas. An empirical example is presented: we analyze the back-calculation of Eu15 Industrial Production Index comparing our approach with the Eurostat official one. |
| Keywords: | benchmarking,retropolation, historical reconstruction, back-forecasting, missing past values, aggregation, disaggregation. |
| JEL: | C10 C82 C50 |
| Date: | 2006 |
| URL: | http://d.repec.org/n?u=RePEc:ven:wpaper:56_06&r=for |
| By: | Francois-Éric Racicot (Département des sciences administratives, Université du Québec (Outaouais) et LRSP); Raymond Théoret (Département de stratégie des affaires, Université du Québec (Montréal)) |
| Abstract: | Markets makers quote many option categories in terms of implicit volatility. In doing so, they can reactivate the Black and Scholes model which assumes that the volatility of an option underlying is constant while it is highly variable. First of all, this article, whose purpose is very empirical, presents a simulation of stochastic volatility programmed in Visual Basic (Excel) whose aim is to compute the price of an European option written on a zero coupon bond. We compare this computed price with this one resulting from Black analytical solution and we also show how to compute an interest rate forecast with the help of the simulation model. Then we write many Visual Basic and Matlab programs for the purpose of computing the implicit volatility surface, a three-dimensional surface which can be plotted by using graphical capacities of Excel and Matlab. It remains that the concept of implicit volatility is very criticised because it is computed with the exercise price of an option and not with the price of the underlying, as it should be. Therefore, there are biases in the estimation of the «greeks» computed with implicit volatility. |
| Keywords: | Financial engineering, Monte Carlo simulation, stochastic volatility, implicit volatility. |
| JEL: | G12 G13 G33 |
| Date: | 2007–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:pqs:wpaper:012007&r=for |