Forecasting exchange rate volatility

Vilasuso, Jon

doi:10.1016/s0165-1765(02)00036-8

Cited by 89 publications

(48 citation statements)

References 9 publications

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“…When browsing the literature on volatility forecasting, it comes as a certain surprise that these candidate models have received relatively scant attention so far. Basically, only two papers with a direct comparison between GARCH and FIGARCH forecasts appear to be available at present, Vilasuso (2002) and Zumbach (2004), both considering volatility forecasts in foreign exchange markets. Vilasuso reports relatively large reductions of both mean squared errors and mean absolute errors over forecasting horizons of 1 to 10 days with FIGARCH compared to GARCH.…”

Section: Introductionmentioning

confidence: 99%

Forecasting volatility and volume in the Tokyo Stock Market: Long memory, fractality and regime switching

Lux

Kaizoji

2007

Journal of Economic Dynamics and Control

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Abstract:We investigate the predictability of both volatility and volume for a large sample of Japanese stocks. The particular emphasis of this paper is on assessing the performance of long memory time series models in comparison to their short-memory counterparts. Since long memory models should have a particular advantage over long forecasting horizons, we consider predictions of up to 100 days ahead. In most respects, the long memory models (ARFIMA, FIGARCH and the recently introduced multifractal model) dominate over GARCH and ARMA models. However, while FIGARCH and ARFIMA also have quite a number of cases with dramatic failures of their forecasts, the multifractal model does not suffer from this shortcoming and its performance practically always improves upon the naïve forecast provided by historical volatility. As a somewhat surprising result, we also find that, for FIGARCH and ARFIMA models, pooled estimates (i.e. averages of parameter estimates from a sample of time series) give much better results than individually estimated models.

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Section: Introductionmentioning

confidence: 99%

Forecasting volatility and volume in the Tokyo Stock Market: Long memory, fractality and regime switching

Lux

Kaizoji

2007

Journal of Economic Dynamics and Control

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“…Bollerslev et al (2003) indicated that the normality assumption is at odds when price changes exhibit the fat-tailedness (leptokurtosis behavior). it has been evidenced recently by a number of authors (Brooks and Persand (2003), Vilasuso (2002), and hansen and Launda (2003), the standard gArch models which use the normality assumption has an inferior forecasting performance compared to the models that reflect skewness and kurtosis in innovations.…”

Section: Modelování a Prognózování Volatility Globálních Cen Potravinmentioning

confidence: 98%

Modeling and forecasting volatility in global food commodity prices

Onour¹,

Sergi²

2011

AGR ECON-CZECH

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Abstract:To capture the volatility in the global food commodity prices, we employed two competing models, the thin tailed the normal distribution, and the fat-tailed Student t-distribution models. results based on wheat, rice, sugar, beef, coffee, and groundnut prices, during the sample period from october 1984 to September 2009, show the t-distribution model outperforms the normal distribution model, suggesting that the normality assumption of residuals which are often taken for granted for its simplicity may lead to unreliable results of the conditional volatility estimates. The paper also shows that the volatility of food commodity prices characterized with the intermediate and short memory behavior, implying that the volatility of food commodity prices is mean reverting. Key words: volatility, forecast, fat-tail distribution, food commoditiesAbstrakt: Pro zkoumání volatility globálních cen potravinářských komodit bylo použito dvou vzájemně si konkurujících modelů, tzv. "thin-tail" normální distribuce a "fat-tail" Student-t distribuce. Výsledky založené na zkoumání cen pšenice, rýže, cukru, hovězího masa, kávy a podzemnice olejné s využitím dat za období říjen 1984-září 2009 ukazují, že model t-distribuce dociluje lepších výsledků než model normální distribuce, což naznačuje, že předpoklad normálního rozložení reziduí, jenž je často považován za samozřejmý pro svou jednoduchost, může vést k nespolehlivým výsledkům podmí-něných odhadů volatility. Práce rovněž ukazuje, že volatilita cen potravinářských komodit je charakterizována chováním střednědobé a krátkodobé paměti a osciluje tedy kolem průměru.

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“…Many in the literature (see, inter alia, Bandi and Perron, 2006, Vilasuso, 2002, and Baillie et al 1996 have suggested that asset price volatility is neither an I(1) nor an I(0) process but rather a fractionally integrated or I(d) process. The introduction of the autoregressive fractionally integrated moving average (ARFIMA) model by Granger and Joyeux (1980) and Hosking (1981) allows the modeling of persistence or long memory where The most popular, due to its semi-parametric nature, is the log periodogram estimator (Geweke and Porter-Hudak, 1983;Robinson, 1995a) Robinson (1995aRobinson ( , 1995b demonstrated that the GPH estimate is consistent and asymptotically normally distributed.…”

Section: A Fractional Integrationmentioning

confidence: 99%

Foreign Exchange, Fractional Cointegration and the Implied-Realized Volatility Relation

2007

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Almost all relevant literature has characterized implied volatility as a biased predictor of realized volatility. This paper provides new time series techniques to assess the validity of this finding within a foreign exchange market context. We begin with the empirical observation that the fractional order of volatility is often found to have confidence intervals that span the stationary/non-stationary boundary. However, no existing fractional cointegration test has been shown to be robust to both regions. Therefore, a new test for fractional cointegration is developed and shown to be robust to the relevant orders of integration. Secondly, employing a dataset that includes the relatively new Euro markets, it is shown that implied and realized volatility are fractionally cointegrated with a slope coefficient of unity. Moreover, the non-standard asymptotic distribution of estimators when using fractionally integrated data is overcome by employing a bootstrap procedure in the frequency domain. Strikingly, tests then show that implied volatility is an unbiased predictor of realized volatility! JEL classification: C14, C22, C32, F31, G14.

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Forecasting exchange rate volatility

Cited by 89 publications

References 9 publications

Forecasting volatility and volume in the Tokyo Stock Market: Long memory, fractality and regime switching

Forecasting volatility and volume in the Tokyo Stock Market: Long memory, fractality and regime switching

Modeling and forecasting volatility in global food commodity prices

Foreign Exchange, Fractional Cointegration and the Implied-Realized Volatility Relation

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