The Distribution of Exchange Rate Volatility (original) (raw)
Related papers
The Distribution of Realized Exchange Rate Volatility
Journal of the American Statistical Association, 2001
Using high-frequency data on Deutschemark and Yen returns against the dollar, we construct model-free estimates of daily exchange rate volatility and correlation, covering an entire decade. In addition to being model-free, our estimates are also approximately free of measurement error under general conditions, which we delineate. Hence, for all practical purposes, we can treat the exchange rate volatilities and correlations as observed rather than latent. We do so, and we characterize their joint distribution, both unconditionally and conditionally. Noteworthy results include a simple normality-inducing volatility transformation, high contemporaneous correlation across volatilities, high correlation between correlation and volatilities, pronounced and highly persistent temporal variation in both volatilities and correlation, clear evidence of long-memory dynamics in both volatilities and correlation, and remarkably precise scaling laws under temporal aggregation.
Time variation in the correlation structure of exchange rates: high‐frequency analyses
Journal of Futures Markets, 2001
The correlation structure of asset returns is a crucial parameter in risk management as well as in theoretical finance. In practice, however, the true correlation structure between the returns of assets can easily become obscured by time variation in the observed correlation structure and in the liquidity of the assets. We employed a timestamped high-frequency data set of exchange rates, namely, the US$deutsche mark and the US$-yen exchange rates, to calibrate the observed time variation in the correlation structure between their
Eurasian Economic Review, 2022
This research aims to detect cross-border volatility linkages among various currencies within the foreign exchange market with respect to different sampling frequencies. Eleven currency pairs are included in the sample, which covers a period from 2009 to 2020. Volatility linkages among these selected exchange rates were tested by utilizing a multivariate VAR-BEKK-GARCH model. Results indicate that volatility linkages among currencies sampled are far stronger in higher frequency terms. Strikingly, the results denote that the major currencies do not play a strong leading role in volatility transmission. This finding is more apparent when daily and intraday results are compared.
Volatility of High-Frequency Returns on Foreign Exchange
Many models in finance are often based on the assumption that the random variables follow a Gaussian distribution. It is now well known that empirical data have frequently occurring extreme values and cannot be modeled with the Gaussian distribution. The stable distributions, a class of probability distributions that allow skewness and heavy tails, have received great interest in the last decade because of their success in modeling financial data that depart from the Gaussian distribution. This study examines the statistical distributions of intra-daily TRY/USD foreign exchange changes. The volatility of the return series are calculated using the Stable GARCH models. It is found that the GARCH model with stable innovations fits returns better than the Normal distribution.
Measuring non-linearity, long memory and self-similarity in high-frequency European exchange rates
2004
This paper combines analysis of deterministic non-linear dynamics and stochastic models of long memory volatility processes. Measures of non-linear dependency, due to Savit and Green, and originally used in the physical sciences, are applied to high-frequency foreign exchange rate returns. The analysis is applied before and after long memory characteristics are removed from the volatility process of returns, with the standard errors of the procedure being bootstrapped. The study finds that high-frequency currency returns volatility is well represented by a FIGARCH model. The estimates of the long memory parameter are remarkably consistent across time aggregations and currencies and are suggestive of self-similarity. While there is some evidence of a small remaining amount of non-linear temporal dependence; it is found to be too weak to be exploitable for forecasting purposes.
Long Memory and Volatility Dynamics in the US Dollar Exchange Rate
SSRN Electronic Journal, 2000
This paper focuses on nominal exchange rates, specifically the US dollar rate vis-à-vis the Euro and the Japanese Yen at a daily frequency. We model both absolute values of returns and squared returns using long-memory techniques, being particularly interested in volatility modelling and forecasting given their importance for FOREX dealers. Compared with previous studies using a standard fractional integration framework such as Granger and Ding (1996), we estimate a more general model which allows for dependence not only at the zero but also at other frequencies. The results show differences in the behaviour of the two series: a long-memory cyclical model and a standard I(d) model seem to be the most appropriate for the US dollar rate vis-à-vis the Euro and the Japanese Yen respectively.
Asymmetric volatility in the foreign exchange markets
Journal of International Financial Markets, Institutions and Money, 2009
We examine the presence or absence of asymmetric volatility in the exchange rates of Australian dollar (AUD), Euro (EUR), British pound (GBP) and Japanese yen (JPY), all against US dollar. Our investigation is based on a variant of the heterogeneous autoregressive realized volatility model, using daily realized variance and return series from 1996 to 2004. We find that a depreciation against USD leads to significantly greater volatility than an appreciation for AUD and GBP, whereas the opposite is true for JPY. Relative to volatility on days following a positive one-standard-deviation return, volatility on days following a negative one-standard-deviation return is higher by 6.6% for AUD, 6.1% for GBP, and 21.2% for JPY. The realized volatility of EUR appears to be symmetric. These results are robust to the removal of jump component from realized volatility and the sub-samplings defined by structural-changes. The asymmetry in AUD, GBP and JPY appears to be embedded in the continuous ...
Intraday volatility and scaling in high frequency foreign exchange markets
International Review of Financial Analysis, 2011
Recent reports suggest that the stochastic process underlying financial time series is nonstationary with nonstationary increments. Therefore, time averaging techniques through sliding intervals are inappropriate and ensemble methods have been proposed. Using daily ensemble averages we analyze two different measures of intraday volatility, trading frequency and the mean square fluctuation of increments for the three most active FX markets; we find that both measures indicate that the underlying stochastic dynamics exhibits nonstationary increments. We show that the two volatility measures are equivalent. In each market we find three time intervals during the day where the mean square fluctuation of increments can be fit by power law scaling in time. The scaling indices in the intervals are different, but independent of the FX market under study. We also find that the fluctuations in return in these intervals lie on exponential distributions.
2012
We investigate quotation and transaction activities in the foreign exchange market for every week during the period of June 2007 to December 2010. A scaling relationship between the mean values of number of quotations (or number of transactions) for various currency pairs and the corresponding standard deviations holds for a majority of the weeks. However, the scaling breaks in some time intervals, which is related to the emergence of market shocks. There is a monotonous relationship between values of scaling indices and global averages of currency pair cross-correlations when both quantities are observed for various window lengths Deltat\Delta tDeltat.
The statistical distribution of daily exchange rate price changes: dependent vs independent models
1999
This study evaluates recently reported, conflicting, models for the probability distributions of daily exchange rate price changes. The conflicting conclusions arise from differing data sets, noncomparable evaluation criteria, and failures to directly compare the candidate models. This study evaluates the mixed jump diffusion model, a discrete mixture of normals distribution model, and four alternative forms of the generalized autoregressive conditional heteroscedastic (GARCH) model. We estimate parameters for each model using maximum likelihood techniques; the goodness-of-fit for the models is measured using Schwarz's criteria. In contrast to some recently published results, none of our autoregressive conditional variance models dominated the others; also none of these models consistently dominated the two models that assume returns are independent. In the cases where there is significant first-order heteroscedasticity in the data set, the GARCH models are superior only 50% of the time. In the most recent subperiod (Jan 88 -Dec 92) tests show that for three of the four currencies the first-order heteroscedasticities are less pronounced than in prior periods. Curiously, first-order GARCH parameters are significant in cases where tests for first-order heteroscedasticity are not significant; this result suggests that our models may be misspecified. Results indicate that independence should not be overlooked, and future research should not focus on the search for the perfect GARCH model, but attempt to develop models that incorporate the pronounced volatility clustering found in exchange rate price series and the independent behavior that exists in the data. These conclusions are consistent with recent findings related to high frequency (intra-daily) returns.