On the Estimation of Security Price Volatilities from Historical Data

By Mark B. Garman., M. Klass. 1980

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Long short‐term memory networks for CSI300 volatility prediction with Baidu search volume (2018, methodology): “We remark that this definition is the best among all quadratic combination under some criteria [5].”
Deep Learning Stock Volatility with Google Domestic Trends (2015, neutral): “2 [11].”
Forecasting time-varying covariance with a range-based dynamic conditional correlation model (2009, background): “Studies that provide supporting evidence 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 (20”
Forecasting volatility in the New Zealand stock market (2002, methodology): “Although the last method provides a more eA cient volatility estimator in terms of approximating the diVusion term in a small sample, it is subject to more biases (for example, due to the closure of the stock exchange overnight; see Garman and Klass, 1980 ).”
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.”
Information leadership in the advanced Asia-Pacific stock markets: Return, volatility and volume information spillovers from the US and Japan (2005, unknown): “squared or absolute price changes ( Garman and Klass, 1980 ).”
Properties of Range-Based Volatility Estimators (2012, unknown): “…p = 1 E (d) = √ 8πσ (11) and for p = 2 E ( d2 ) = 4 ln (2)σ2 (12) Based on formula (12), he proposes a new volatility estimator: σ̂2P = (h− l)2 4 ln 2 (13) Garman and Klass (1980) realize that this estimator is based solely on quantity h−l and therefore an estimator which utilizes all the…”
A Simple Way to Estimate Bid-Ask Spreads from Daily High and Low Prices (2012, methodology): “Specifically, under the assumptions that stock prices follow the usual geometric Brownian motion and the price is observed continuously, Parkinson (1980) and Garman and Klass (1980) show that a powerful and unbiased variance estimator can be defined as”
Analyzing Stock Market Movements Using Twitter Sentiment Analysis (2012, methodology): “We estimate daily volatility based on intra-day highs and lows using Garman and Klass volatility measures [12] given by the formula:”
Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review (2004, background): “O t h e r M e t h o d s o f V o l a t i l i t y M o d e l i n g “Stochastic volatility” models (Barndorff-Nielsen et al. (2001), Chib et al. (1998), Ghysels et al. (1996), Harvey and Shephard (1993), Jacquier et al. (1994), Shephard (1996), Taylor (1994)), “implied volatility” models (Day and Lewis ”
Realized range-based estimation of integrated variance (2007, unknown): “Building on the simulation evidence of Garman and Klass (1980), Rogers and Satchell (1991) proposed a technique for bias correcting the range that largely removed the error from a numerical perspective.”
Does herding affect volatility? Implications for the Spanish stock market (2012, methodology): “Thirdly we use the historical volatility measures proposed by Garman and Klass (1980) and Parkinson (1980).”
Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets* (2008, unknown): “Then, following Garman and Klass (1980) and Alizadeh et al. (2002), we can use weekly high, low, opening and closing prices obtained from underlying daily high/low/open/close data to estimate weekly stock return volatility:”
Precious Metals-Exchange Rate Volatility Transmissions and Hedging Strategies (2009, unknown): “They used the univariate GARCH model to examine the volatility properties of the futures returns and the alternative non-parametric Garman–Klass volatility range statistic (Garman and Klass, 1980) to provide further insights in intraday and interday volatility dynamics of gold.”