High Frequency Data and Volatility in Foreign Exchange Rates

By Bin Zhou. 2013

328 citations recorded by the source corpus.

Exchange rates, like many other financial time series, display substantial heteroscedasticity. This poses obstacles in detecting trends and changes. Understanding volatility becomes extremely important in studying financial time series. Unfortunately, estimating volatility from low-frequency data, such as daily, weekly, or monthly observations, is very difficult. The recent availability of ultra-high-frequency observations, such as tick-by-tick data, to large financial institutions creates a new possibility for the analysis of volatile time series. This article uses tick-by-tick foreign-exchange rates to explore this new type of data. Unlike low-frequency data, high-frequency data have extremely high negative first-order autocorrelation in their return. In this article, I propose a model that can explain the negative autocorrelation and a volatility estimator for high-frequency data. The daily and hourly volatility estimates of exchange rate show some interesting patterns.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/1ca8c7d46ca88e8d889fe19e39e6ff6ed5079f8d

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Citations in the corpus

Estimating spot volatility with high-frequency financial data (2014, methodology): “…Realized Variance (MSRV) estimator by Zhang (2006); the Realized Kernel (RK) estimator by Barndorff-Nielsen et al. (2008), which is based on Zhou (1996)’s first order moving average correction; the pre-averaging method by Podolskij and Vetter (2009), and Jacod et al. (2009); and the…”
Realized volatility forecasting and market microstructure noise (2011, methodology): “27) holds for the sparse and Zhou estimators, and for any kernel estimator including the nonflat top kernels introduced by Barndorff-Nielsen et al. (2008b). It is not satisfied for the average and two scale estimators at the edges of the trading day, although it will be close to valid for these aswe”
Covariance measurement in the presence of non-synchronous trading and market microstructure noise (2011, unknown): “(7) is similar in spirit to the bias-corrected realized variance measure of Zhou (1996) or the kernel based realized variance measure of Barndorff-Nielsen, Hansen, Lunde, and Shephard (2006). Thus, a natural extension of the estimator would take the form: RCLLM = M ∑”
Market microstructure noise, integrated variance estimators, and the accuracy of asymptotic approximations (2011, background): “Hansen and Lunde (2006) study the …nite sample MSE properties of b V for the case q = 1, w 0 = 1, and w 1 = m m1 (see Zhou, 1996, for an introduction to this estimator).”
Forecasting Volatility Using High-Frequency Data (2011, unknown): “Specific models for U have been suggested in this context by, for example, Zhou (1996), Hansen and Lunde (2006), Li and Mykland (2007) and Diebold and Strasser (2008).”
Do High-Frequency Measures of Volatility Improve Forecasts of Return Distributions? (2011, unknown): “Zhang (2005, 2006), Bandi and Russell (2006), Barndorfi-Nielsen, Hansen, Lunde, and Shephard (2004, 2006), Curci and Corsi (2006), Oomen (2005), Zhang (2004) and Zhou (1996) .”
Data-based ranking of realised volatility estimators (2011, methodology): “In addition to the particular estimator to use, the performance of RV estimators is generally affected by the frequency used to sample the price process (for example, every 5 minutes or every 30 seconds), see Zhou (1996) and Bandi and Russell (2008) for example, and may also be affected by the decis”
Edgeworth Expansions for Realized Volatility and Related Estimators (2011, unknown): “Further strategies include Zhou (1996, 1998) , Hansen and Lunde (2006), and Bandi and Russell (2008).”
Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes (2015, unknown): “All realized measures require a choice of sampling frequency (e.g., 1-second or 5-minute sampling), sampling scheme (calendar time or tick time), whether to use transaction prices or mid-quotes, when both are available.”
Improving Portfolio Selection Using Option-Implied Volatility and Skewness (2013, unknown): “Not knowing the distribution of returns, we cannot use the shrinkage results of Ledoit andWolf (2004a,b), and so we use instead the regularisation approach of Zumbach (2009). Details of the regularisation method we use are given in Appendix B.”