In this issue we have Integration, Cointegration and Long-Horizont Forecasting of Credit-Default-Cycles ; Nonlinearities in Exchange rates: Double EGARCH Threshold Models for Forecasting Volatility ; The Monetary Model Strikes Back: Evidence from the World.
|Summary: This paper examines the longterm forecast performance of cointegrated systems relative to forecast performance of comparable VAR that fails to recognize that the system is characterized by cointegration. I use Monte Carlo simulation, real data sets, and multi-step-ahead forecasts to study this question. The cointegrated system I examine is composed of six vectors, five macoreconomic variables, and a credit-default-cycle. The forecasts produced by the vector error correction modell associated with this system are compared with those obtained from a corresponding differenced vector autoregression, as well as a vector autoregression based upon the levels of the data. Alternative measures of forecast accuracy (full-system) are discussed. My findings suggest that selective forecast performance improvement may be observed by incorporating knowledge of cointegration rank. Furthermore the results indicate that a ! cointegration modeling of credit risk should be favored against the prevalent level or differenced estimation.|
|Keywords:||Integration; Kointegration; Langzeitprognose; Kreditausfallzyklus; Integration; Cointegration; Forecasting; Credit-default-cycle|
|This paper illustrates how to specify and test a Double Threshold EGARCH Model for some important exchange rates. The analysis is monthly and refers to the period 1990.01-2007.06. The procedure involves testing for Threshold effects the residuals of a linear autoregressive model of the exchange rate that is taken as the starting point. If this preliminary testing is favourable to the hypothesis off nonlinearity one then specifies and estimates a threshold model using Tong (1983,1990) algorithm, Tong algorithm allows to specify separately two AR regimes and helps locating both the delay and the parameters of the regimes using a search procedure based on the AIC. Residual for the SETAR model are then further tested for conditional heteroskedasticity. If it is present then a Double symmetric EGARCH is fitted to the data by maximum likelihood. The result is compared with an AR GARCH model both in sample and out of sam! ple to asses whether there is any forecasting superiority of the more complex model. Reported results favour this outcome. In the text of the paper we report explicitly the results for the Japanese yen and the British pound exchange rates vis a vis the US dollar, but the same procedure has been applied to many other exchange rate series with results favourable to the double variance model in more than 50% of the cases. We report the complete results in the appendix. We conclude that the proposed model is both feasible and of wide applicability to the analysis of volatility of exchange rates. We add two provisos: data are monthly and the period of estimation reflects only the most recent experience.|
|Keywords:||non linearity; forecasting volatility; exchange rates|
Sweta Chaman Saxena
|We revisit the dramatic failure of monetary models in explaining exchange rate movements. Using the information content from 98 countries, we find strong evidence for cointegration between nominal exchange rates and monetary fundamentals. We also find fundamentalsbased models very successful in beating a random walk in out-of-sample prediction.|
Taken from the NEP-FOR mailing list edited by Rob Hyndman.