Drift Independent Volatility Estimation Based on High, Low, Open, and Close Prices

By D. Yang, Qiang Zhang. 2000

478 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Finance

Citations in the corpus

Using deep learning to detect price change indications in financial markets (2017, methodology): “Methods utilizing this data often use subsampling techniques, such as the OHLC (Open-High-Low-Close) resampling [13], to limit the number of values exist for each timeframe, e.”
Investor Flows and the 2008 Boom/Bust in Oil Prices (2014, methodology): “Using daily data on the generic near futures contract from Bloomberg, I computed a rolling monthly (20 trading day) series of volatilities using the Garman and Klass (1980) and Yang and Zhang (2000) estimators based on open/close/high/low price information.”
Intra- and inter-regional return and volatility spillovers across emerging and developed markets: Evidence from stock indices and stock index futures (2016, methodology): “…over classical estimators of volatility are widely discussed in literature (Garman & Klass, 1980; Parkinson, 1980; Rogers & Satchell, 1991, Yang & Zhang, 2000).5 The Rogers and Satchell (1991) volatility estimator has been used because it is more efficient than classical volatility Ta bl…”
Volatility Forecasting With Range-Based EGARCH Models (2006, methodology): “Finally, the literature on range-based volatility estimation includes work of Parkinson (1980), Garman and Klass (1980), Schwert (1990), Gallant et al. (1999), Yang and Zhang (2000) , Alizadeh et al. (2002), Brandt and Diebold (2006), and Chou (2005), among others.”
Forecasting time-varying covariance with a range-based dynamic conditional correlation model (2009, background): “…include Parkinson (1980), Garman and Klass (1980), Wiggins (1991), Rogers and Satchell (1991), Kunitomo (1992) and, more recently, Gallant, Hsu, and Tauchen (1999), Yang and Zhang (2000), Alizadeh, Brandt, and Diebold (2002), Brandt and Jones (2005), Chou (2005, 2006) and Chou, Wu, and Liu (2004).”
Measuring volatility with the realized range (2006, background): “Kunitomo (1992) and Yang and Zhang (2000) extended the daily high-low range to allow for non-zero drift in the stock price, essentially by considering a transformed price process to eliminate the drift.”
Demystifying Time‐Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations (2020, unknown): “The parameter k is chosen so that the variance of the estimator is minimised and is shown by Yang and Zhang (2000) to be a function of the number of days used in the estimation.”
Estimating correlation from high, low, opening and closing prices (2008, background): “The idea of using information on the daily high and low prices, as well as the opening and closing prices, goes back a long way, to (Parkinson, 1980) and (Garman and Klass, 1980) at least, with further contributions by (Beckers, 1983), (Ball and Torous, 1984), (Rogers and Satchell, 1991), (Kunitomo,”
Properties of Range-Based Volatility Estimators (2012, unknown): “Yang and Zhang (2000) derive another drift-independent estimator.”
Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks (2017, methodology): “Methods that utilize this data often subsample them using re-sampling techniques, such as the OHLC (OpenHigh-Low-Close) resampling [13], to ensure that a specific number of values exist for each timeframe, e.”
Nonparametric regression for locally stationary time series (2012, methodology): “It has been employed for example in the studies of Rogers & Satchell [24], Yang & Zhang [28], Alizadeh et al.”
"Momentum Strategies in Futures Markets and Trend-following Funds" (2013, unknown): “(13) where k is chosen so that the variance of the estimator is minimised (Yang and Zhang (2000) show that this is in practice achieved for k = 0.341.34+(D+1)/(D−1) ) and σ2STDEV (t”
Forecasting Financial Volatilities with Extreme Values: The Conditional Autoregressive Range (CARR) Model (2005, background): “Other references include Garman and Klass (1980), Wiggins (1991), Rogers and Satchell (1991), Kunitomo (1992), and more recently Yang and Zhang (2000).”