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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractMany empirical studies find a negative correlation between the returns on the nominal spot exchange rate and the lagged forward discount. This forward discount anomaly implies that the current forward rate is a biased predictor of the future spot rate. A large number of studies in the existing literature try to explain this anomaly, and recent work has tried to explain the anomaly as a statistical artifact based on (1) the long memory behavior of the forward discount; or (2) the existence of structural breaks in the forward discount. In this paper, we evaluate the evidence for long memory and structural change in the forward discount. Our approach is as follows. First, we nonparametrically estimate the long memory parameter for a number of forward discount series without allowing for structural breaks. Second, we test for and estimate a multiple mean break model and then adjust for the structural breaks in the forward discount. Finally, we re-estimate the long memory parameter on the mean-break adjusted data. We show that allowing for structural breaks drastically reduces the persistence of the forward discount. However, after removing the breaks, we still find evidence of stationary long memory in all of the forward discount series. Our results have important implications for understanding the statistical properties of the forward discount, because we confirm not only the presence of long memory behavior in the forward discount but also the importance of structural breaks.Key Words : Long Memory, Structural Changes, Forward Discount JEL code : C14, C22, F31 2 IntroductionMany empirical studies find a negative correlation between the returns on the nominal spot exchange rate and the lagged forward discount. This forward discount anomaly implies that the current forward rate is a biased predictor of the future spot rate. A large number of studies in the existing literature try to explain this anomaly. Engel (1996) summarized four explanations: (1) existence of a foreign exchange risk premium; (2) a peso problem, (3) irrational expectations; (4) international financial market inefficiency from various frictions. In two detailed studies, Baillie and Bollerslev (1994, 2000) focused on the time series properties of the spot rate and forward discount as an explanation for the forward discount anomaly. They argued that the forward discount anomaly is due to the statistical properties of the data, because the forw...

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We explore the possibility of structural breaks in the realized volatility with the observed long-memory property for the daily Deutschemark/Dollar, Yen/Dollar and Yen/Deutschemark spot exchange rate realized volatility. The paper finds that the structural breaks can partly explain the persistence of realized volatility. We propose a VAR-RV-Break model that provides a superior predictive ability compared to most of the forecasting models when the future break is known. With unknown break dates and sizes, we find that the VAR-RV-I(d) long memory model, however, is a very robust forecasting method even when the true financial volatility series are generated by structural breaks.JEL classification: C32, C52, C53, G10

We test for the presence of long memory in daily oil and refined products prices' absolute return, squared return and conditional volatility, using several parametric and semiparametric methods. This study finds strong evidence of long memory (LM) in the daily absolute and squared spot and futures returns for crude oil, gasoline and heating oil but at different degrees. The FIGARCH model also demonstrates strong evidence of LM for volatility for most of oil and products prices' returns, with also different resilience levels. Structural breaks have only the partial effects of slightly reducing persistence for just absolute and squared returns. Examining the forecasting behavior of two competing models, the less parsimonious ARFIMA which satisfies the LM property, and the parsimonious ARMA with short-term processes, the ARFIMA model provides significantly better out-of-sample forecasts at all forecasting horizons for all three petroleum types.