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Rate Series (rate + series)
Kinds of Rate Series Selected AbstractsModelling volatility clustering in electricity price return series for forecasting value at riskEUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 1 2009R. G. Karandikar Abstract Modelling of non-stationary time series using regression methodology is challenging. The wavelet transforms can be used to model non-stationary time series having volatility clustering. The traditional risk measure is variance and now a days Value at Risk (VaR) is widely used in finance. In competitive environment, the prices are volatile and price risk forecasting is necessary for the market participants. The forecasting period may be 1 week or higher depending upon the requirement. In this paper, a model is developed for volatility clustering in electricity price return series and its application for forecasting VaR is demonstrated. The first model is using GARCH (1, 1). The VaR of variance rate series, that is worst-case volatility is calculated using variance method using wavelet transform. The model is used to forecast variance rate (volatility) for a sample case of 1-week half-hourly price return series. The second model developed is for forecasting VaR for price return series of 440 days. This model is developed using wavelets via multi-resolution analysis and uses regime-switching technique. The historical data of daily average prices is obtained from 100% pool type New South Wales (NSW), a zonal market of National Electricity Market (NEM), Australia. Copyright © 2007 John Wiley & Sons, Ltd. [source] Mean and Variance Causality between Official and Parallel Currency Markets: Evidence from Four Latin American CountriesFINANCIAL REVIEW, Issue 2 2002Angelos Kanas This paper examines the issue of mean and variance causality across four Latin American official and black markets for foreign currency using monthly data for the period 1976,1993. We apply a recent test developed by Cheung and Ng (1996) in order to test for mean and variance spillovers. The main findings are: (1) In contrast to the findings of previous studies, EGARCH-M processes characterize each bilateral exchange rate series in both markets; (2) There is substantial evidence of causality in both mean and variance with the causality in mean largely being driven by the causality in variance; and (3) The results indicate that the major exporter of causality is the Mexican black market with the black market of Argentina and the black and official markets of Brazil being the smallest contributors. [source] Non-linear interest rate dynamics and forecasting: evidence for US and Australian interest ratesINTERNATIONAL JOURNAL OF FINANCE & ECONOMICS, Issue 2 2009David G. McMillan Abstract Recent empirical finance research has suggested the potential for interest rate series to exhibit non-linear adjustment to equilibrium. This paper examines a variety of models designed to capture these effects and compares both their in-sample and out-of-sample performance with a linear alternative. Using short- and long-term interest rates we report evidence that a logistic smooth-transition error-correction model is able to best characterize the data and provide superior out-of-sample forecasts, especially for the short rate, over both linear and non-linear alternatives. This model suggests that market dynamics differ depending on whether the deviations from long-run equilibrium are above or below the threshold value. Copyright © 2007 John Wiley & Sons, Ltd. [source] Black and official exchange rate volatility and foreign exchange controls: evidence from GreeceINTERNATIONAL JOURNAL OF FINANCE & ECONOMICS, Issue 1 2001Angelos Kanas F31; F32; C22; C52 Abstract This paper examines the issue of volatility and capital controls to the official and black market exchange rates of the Greek Drachma using the monthly exchange rate against the US dollar for the period 1975,1993. Specifically, we apply a GARCH(1,,1) model to study the behaviour of the official and black market drachma/dollar exhange rate. The main findings of the analysis are: (i) in contrast to the findings of previous studies using monthly rates, GARCH processes characterize the drachma/dollar exchange rate series in both markets; (ii) the relaxation of foreign exchange controls increased the volatility of the exchange rate in the official market as implied by theory; (iii) the persistence of volatility is reduced when account is taken of the liberalization process of capital movements; and (iv) The forecasts of volatility are improved when the GARCH forecasts are used against traditional measures. Copyright © 2001 John Wiley & Sons, Ltd. [source] Daily volatility forecasts: reassessing the performance of GARCH modelsJOURNAL OF FORECASTING, Issue 6 2004David G. McMillan Abstract Volatility plays a key role in asset and portfolio management and derivatives pricing. As such, accurate measures and good forecasts of volatility are crucial for the implementation and evaluation of asset and derivative pricing models in addition to trading and hedging strategies. However, whilst GARCH models are able to capture the observed clustering effect in asset price volatility in-sample, they appear to provide relatively poor out-of-sample forecasts. Recent research has suggested that this relative failure of GARCH models arises not from a failure of the model but a failure to specify correctly the ,true volatility' measure against which forecasting performance is measured. It is argued that the standard approach of using ex post daily squared returns as the measure of ,true volatility' includes a large noisy component. An alternative measure for ,true volatility' has therefore been suggested, based upon the cumulative squared returns from intra-day data. This paper implements that technique and reports that, in a dataset of 17 daily exchange rate series, the GARCH model outperforms smoothing and moving average techniques which have been previously identified as providing superior volatility forecasts. Copyright © 2004 John Wiley & Sons, Ltd. [source] Can cointegration-based forecasting outperform univariate models?JOURNAL OF FORECASTING, Issue 5 2002An application to Asian exchange rates Abstract Conventional wisdom holds that restrictions on low-frequency dynamics among cointegrated variables should provide more accurate short- to medium-term forecasts than univariate techniques that contain no such information; even though, on standard accuracy measures, the information may not improve long-term forecasting. But inconclusive empirical evidence is complicated by confusion about an appropriate accuracy criterion and the role of integration and cointegration in forecasting accuracy. We evaluate the short- and medium-term forecasting accuracy of univariate Box,Jenkins type ARIMA techniques that imply only integration against multivariate cointegration models that contain both integration and cointegration for a system of five cointegrated Asian exchange rate time series. We use a rolling-window technique to make multiple out of sample forecasts from one to forty steps ahead. Relative forecasting accuracy for individual exchange rates appears to be sensitive to the behaviour of the exchange rate series and the forecast horizon length. Over short horizons, ARIMA model forecasts are more accurate for series with moving-average terms of order >1. ECMs perform better over medium-term time horizons for series with no moving average terms. The results suggest a need to distinguish between ,sequential' and ,synchronous' forecasting ability in such comparisons. Copyright © 2002 John Wiley & Sons, Ltd. [source] An outlier robust GARCH model and forecasting volatility of exchange rate returnsJOURNAL OF FORECASTING, Issue 5 2002Beum-Jo Park Abstract Since volatility is perceived as an explicit measure of risk, financial economists have long been concerned with accurate measures and forecasts of future volatility and, undoubtedly, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model has been widely used for doing so. It appears, however, from some empirical studies that the GARCH model tends to provide poor volatility forecasts in the presence of additive outliers. To overcome the forecasting limitation, this paper proposes a robust GARCH model (RGARCH) using least absolute deviation estimation and introduces a valuable estimation method from a practical point of view. Extensive Monte Carlo experiments substantiate our conjectures. As the magnitude of the outliers increases, the one-step-ahead forecasting performance of the RGARCH model has a more significant improvement in two forecast evaluation criteria over both the standard GARCH and random walk models. Strong evidence in favour of the RGARCH model over other competitive models is based on empirical application. By using a sample of two daily exchange rate series, we find that the out-of-sample volatility forecasts of the RGARCH model are apparently superior to those of other competitive models. Copyright © 2002 John Wiley & Sons, Ltd. [source] Bootstrap-based bandwidth choice for log-periodogram regressionJOURNAL OF TIME SERIES ANALYSIS, Issue 6 2009Josu Arteche Abstract., The choice of the bandwidth in the local log-periodogram regression is of crucial importance for estimation of the memory parameter of a long memory time series. Different choices may give rise to completely different estimates, which may lead to contradictory conclusions, for example about the stationarity of the series. We propose here a data-driven bandwidth selection strategy that is based on minimizing a bootstrap approximation of the mean-squared error (MSE). Its behaviour is compared with other existing techniques for optimal bandwidth selection in a MSE sense, revealing its better performance in a wider class of models. The empirical applicability of the proposed strategy is shown with two examples: the widely analysed in a long memory context Nile river annual minimum levels and the input gas rate series of Box and Jenkins. [source] Range Unit-Root (RUR) Tests: Robust against Nonlinearities, Error Distributions, Structural Breaks and OutliersJOURNAL OF TIME SERIES ANALYSIS, Issue 4 2006Felipe Aparicio Abstract., Since the seminal paper by Dickey and Fuller in 1979, unit-root tests have conditioned the standard approaches to analysing time series with strong serial dependence in mean behaviour, the focus being placed on the detection of eventual unit roots in an autoregressive model fitted to the series. In this paper, we propose a completely different method to test for the type of long-wave patterns observed not only in unit-root time series but also in series following more complex data-generating mechanisms. To this end, our testing device analyses the unit-root persistence exhibited by the data while imposing very few constraints on the generating mechanism. We call our device the range unit-root (RUR) test since it is constructed from the running ranges of the series from which we derive its limit distribution. These nonparametric statistics endow the test with a number of desirable properties, the invariance to monotonic transformations of the series and the robustness to the presence of important parameter shifts. Moreover, the RUR test outperforms the power of standard unit-root tests on near-unit-root stationary time series; it is invariant with respect to the innovations distribution and asymptotically immune to noise. An extension of the RUR test, called the forward,backward range unit-root (FB-RUR) improves the check in the presence of additive outliers. Finally, we illustrate the performances of both range tests and their discrepancies with the Dickey,Fuller unit-root test on exchange rate series. [source] Empirical modelling of the DEM/USD and DEM/JPY foreign exchange rate: Structural shifts in GARCH-models and their implicationsAPPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 1 2002Helmut Herwartz Abstract We analyse daily changes of two log foreign exchange (FX) rates involving the Deutsche Mark (DEM) for the period 1975,1998, namely FX-rates measured against the US dollar (USD) and the Japanese yen (JPY). To account for volatility clustering we fit a GARCH(1,1)-model with leptokurtic innovations. Its parameters are not stable over the sample period and two separate variance regimes are selected for both exchange rate series. The identified points of structural change are close to a change of the monetary policies in the US and Japan, the latter of which is followed by a long period of decreasing asset prices. Having identified subperiods of homogeneous volatility dynamics we concentrate on stylized facts to distinguish these volatility regimes. The bottom level of estimated volatility turns out be considerably higher during the second part of the sample period for both exchange rates. A similar result holds for the average level of volatility and for implied volatility of heavily traded at the money options. Copyright © 2002 John Wiley & Sons, Ltd. [source] | |||||||||||||