9,768 research outputs found
Bert Hurst
No full text"Bert. Hurst. 12229 RAAF 6RSU. Fenton 24. Liberator. Sqdn's 1945."Bert Hurst. 12229. Royal Australian Air Force. 6 Repair and Servicing Unit, Fenton. 24 Liberator Squadrons 1945.Date:199
R/S analysis and DFA: finite sample properties and confidence intervals
No full textWe focus on finite sample properties of two mostly used methods of Hurst exponent H estimation – R/S analysis and DFA. Even though both methods have been widely applied on different types of financial assets, only several papers have dealt with finite sample properties which are crucial as the properties differ significantly from the asymptotic ones. Recently, R/S analysis has been shown to overestimate H when compared with DFA. However, we show on the random time series with lengths from 2^9 to 2^17 that even though the estimates of R/S are truly significantly higher than an asymptotic limit of 0.5, they remain very close to the estimates proposed by Anis & Lloyd and the estimated standard deviations are lower than the ones of DFA. On the other hand, DFA estimates are very close to 0.5. The results propose that R/S still remains useful and robust method even when compared to newer method of DFA which is usually preferred in recent literature.rescaled range analysis, detrended fluctuation analysis, Hurst exponent, long-range dependence, confidence intervals
Robust estimation of the Hurst parameter and selection of an onset scaling.
No full textWe consider the problem of estimating the Hurst parameter for long-range dependent processes using wavelets. Wavelet techniques have been shown to effectively exploit the asymptotic linear relationship that forms the basis of constructing an estimator. However, it has been noticed that the commonly adopted standard wavelet estimator is vulnerable to various non-stationary phenomena that increasingly occur in practice, and thus leads to unreliable results. In this paper, we propose a new wavelet method for estimating the Hurst parameter that is robust to such non-stationarities as peaks, valleys, and trends. We point out that the new estimator arises as a simple alternative to the standard estimator and does not require an additional correction term that is subject to distributional assumptions. Additionally, we address the issue of selecting scales for the wavelet estimator, which is critical to properly exploiting the asymptotic relationship. We propose a new method based on standard regression diagnostic tools, which is easy to implement, and useful for providing informative goodness-of-fit measures. Several simulated examples are used for illustration and comparison. The proposed method is also applied to the estimation of the Hurst parameter of Internet traffic packet counts data
Hurst, R E, NX35228
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/394117Surname: HURST. Given Name(s) or Initials: R E. Military Service Number or Last Known Location: NX35228. Missing, Wounded and Prisoner of War Enquiry Card Index Number: 34043.216886
Item: [2016.0049.26410] "Hurst, R E, NX35228
Are Pound and Euro the Same Currency? - Updated
No full textBased on long range dependence, some analysts claim that the exchange rate time series of the pound sterling and of an artificially extended euro have been locked together for years despite daily changes [1, 9]. They conclude that pound and euro are in practice the same currency. We assess the long range dependence over time through Hurst exponents of pound-dollar and extended euro-dollar exchange rates employing three alternative techniques, namely rescaled range analysis, detrended fluctuation analysis, and detrended moving average. We find the result above (which is based on detrended fluctuation analysis) not to be robust to the changing techniques and parameterizing.False euro; exchange rates; financial efficiency; Hurst exponent; R/S analysis; detrended fluctuation analysis; detrending moving average
Hurst analysis of electricity price dynamics
No full textThe price of electricity is extremely volatile, because electric power cannot be economically stored, end user demand is largely weather dependent, and the reliability of the grid is paramount. However, underlying the process of price returns is a strong mean-reverting mechanism. We study this feature of electricity returns by means of Hurst R/S analysis.Econophysics; Electricity price; Mean-reversion; Hurst analysis;
Distinguishing between short and long range dependence: Finite sample properties of rescaled range and modified rescaled range
No full textMostly used estimators of Hurst exponent for detection of long-range dependence are biased by presence of short-range dependence in the underlying time series. We present confidence intervals estimates for rescaled range and modified rescaled range. We show that the difference in expected values and confidence intervals enables us to use both methods together to clearly distinguish between the two types of processes. The estimates are further applied on Dow Jones Industrial Average between 1944 and 2009 and show that returns do not show any long-range dependence whereas volatility shows both short-range and long-range dependence in the underlying process.rescaled range, modified rescaled range, Hurst exponent, long-range dependence, confidence intervals
HURST: MATLAB function to compute the Hurst exponent using R/S Analysis.
No full textH = HURST(X) calculates the Hurst exponent of time series X using the R/S analysis of Hurst [2], corrected for small sample bias [1,3,4]. If a vector of increasing natural numbers is given as the second input parameter, i.e. HURST(X,D), then it defines the box sizes that the sample is divided into (the values in D have to be divisors of the length of series X). If D is a scalar (default value D = 50) it is treated as the smallest box size that the sample can be divided into. In this case the optimal sample size OptN and the vector of divisors for this size are automatically computed. OptN is defined as the length that possesses the most divisors among series shorter than X by no more than 1%. The input series X is truncated at the OptN-th value. [H,HE,HT] = HURST(X) returns the uncorrected empirical and theoretical Hurst exponents. [H,HE,HT,PV95] = HURST(X) returns the empirical 95% confidence intervals PV95 (see [4]).R/S Analysis, Rescaled range, Hurst exponent, p-value, Long range dependence.
Portfolio Optimization and Long-Term Dependence
No full textWhilst emphasis has been given to short-term dependence of financial returns, long-term dependence remains overlooked. Despite financial literature provides evidence of long-term’s memory existence, serial-independence assumption prevails. This document’s long-term dependence assessment relies on rescaled range analysis (R/S), a popular and robust methodology designed for Geophysics but extensively used in financial literature. Results correspond to most of the previous evidence of significant long-term dependence, particularly for small and illiquid markets, where persistence is its most common kind. Persistence conveys that the range of possible future values of the variable will be wider than the range of purely random and independent variables. Ahead of R/S financial literature, authors estimate an adjusted Hurst exponent in order to properly estimate the covariance matrix at higher investment horizons, avoiding the traditional -independence reliant- square-root-of-time rule. Ignoring long-term dependence within the mean-variance portfolio optimization results in concealed risk taking; conversely, by adjusting for long-term dependence the weight of high (low) persistence risk factors decreases (increases) as the investment horizon widens. This alleviates some well-known shortcomings of conventional portfolio optimization for long-term investors (e.g. central banks, pension funds and sovereign wealth managers), such as excessive risk taking in long-term portfolios, extreme weights, home bias, and reluctance to hold foreign currency-denominated assets.Portfolio optimization, Hurst exponent, long-term dependence, biased random walk, rescaled range analysis. Classification JEL: G11, G32, G20, C14.
Rescaled Range Analysis and Detrended Fluctuation Analysis: Finite Sample Properties and Confidence Intervals
No full textWe focus on finite sample properties of two mostly used methods of Hurst exponent H estimation—rescaled range analysis (R/S) and detrended fluctuation analysis (DFA). Even though both methods have been widely applied on different types of financial assets, only several papers have dealt with the finite sample properties which are crucial as the properties differ significantly from the asymptotic ones. Recently, R/S analysis has been shown to overestimate H when compared to DFA. However, we show that even though the estimates of R/S are truly significantly higher than an asymptotic limit of 0.5, for random time series with lengths from 2^9 to 2^17, they remain very close to the estimates proposed by Anis & Lloyd and the estimated standard deviations are lower than the ones of DFA. On the other hand, DFA estimates are very close to 0.5. The results propose that R/S still remains useful and robust method even when compared to newer method of DFA which is usually preferred in recent literature.Rescaled range analysis, detrended fluctuation analysis, Hurst exponent, long-range dependence, confidence intervals
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