Garman, M. and M. Klass (1980), "On the estimation of security price volatilities from historical data", Journal of Business, 53, 67-78.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....by geometric Brownian motion. The logarithm of such a process then follows a Brownian motion. Parkinson [Parkinson 1980] shows that expectation of the high minus the low squared is proportional to oe 2 , and thus constructs an estimate based on the high minus the low. Garman and Klass [Garman and Klass 1980] define a quadratic function of the high, low and close, and derive the parameters of such a function that result in the estimate being unbiased, their estimate is unbiased only in the case of zero drift) Rogers et al. [Rogers and Satchell 1991, Rogers et al. 1994, Rogers 1998] propose another ....

....of different volatility estimation methods. Our method not only remains superior to all the other methods, but the fact that needs to be estimated as well has not significantly worsened the performance. As one further comparison, we consider the special case of = 0. In this case, Garman Klass [Garman and Klass 1980] have constructed the optimal (in the least squares sense) quadratic estimator as oe GK = v u u t 1 NT N X i=1 0:511( h i Gamma l i ) 2 Gamma 0:019( c i ( h i l i ) Gamma 2 l i h i ) Gamma 0:383 c 2 i (1.10) The tilde over the symbols indicates that one ....

Garman, M. and M. Klass. 1980. On the estimation of security price volatilities from historical data. Journal of Business, 53(1):67--78.

....All previous studies have considered securities characterized by geometric Brownian motion or Brownian motion. Parkinson [4] shows that expectation of the high minus the low squared is proportional to 2 , and thus constructs an estimate based on the high minus the low. Garman and Klass [3] de ne a quadratic function of the high, low and close, and derive the parameters of 1 such a function that result in the estimate being unbiased, their estimate is unbiased only in the case of zero drift) Rogers et al. [6, 7, 5] propose another formula, and show that it is an unbiased estimate ....

....Volatility Estimators # Days used in Prediction RMS Prediction Error =0.02, s=0.5 Close High Low (Parkinson) High Low Close (Rogers) High Low Close (ML) Figure 1: Comparison of volatility prediction methods using the RMS prediction error when the drift parameter is known. Garman Klass [3] have constructed the optimal (in the least squares sense) quadratic estimator as GK = v u u t 1 N N X i=1 0:511( h i l i ) 2 0:019( c i ( h i l i ) 2 l i h i ) 0:383 c 2 i (17) The tilde over the symbols indicates that one normalizes the quantities by ....

M. Garman and M. Klass. On the estimation of security price volatilities from historical data. Journal of Business, 53(1):67-78, 1980.

.... period returns on the bond futures are calculated as the log difference between the closing and the opening price within a time period (i.e. open close P P Return ln ln = The volatility of price movements for each period is calculated using all four price observations following the method of Garman and Klass (1980) who show that there is a significant efficiency gain for 4 Other interest rate futures contracts traded on the SFE that were initially considered for this paper include the 90 day bank bill futures and the 3 year Commonwealth bond futures. The former was excluded from the analysis as there are ....

Garman, M. and M. Klass, 1980, On the estimation of security price volatilities from historical data, Journal of Business 53, 67-78.

....financial data either through interpolation of the low frequency data or aggregation of the high frequency data in a fashion that would retain some of the high frequency information. As an example, one can consider using intra month high and low prices to calculate monthly volatility (similar to Garman and Klass, 1980). The potential benefits from using high frequency financial data in a crash forecasting model are indicated in Figure 3. Notice that in the weeks just prior to the July 2 crash of the baht, volatility in the baht FX market was extraordinarily high. This volatility contained useful information, ....

Garman, M., and M. Klass (1980), "On the Estimation of Security Price Volatilities from Historical Data," Journal of Business, 53, 67-78.

....during the same time period. 13 Three minute time intervals are generated from the time and sales records of the S P 500, MMI, and NYSE futures contracts for each day of the seven months chosen. Each time interval employs its open, high, low, and close price for each contract to generate Garman Klass (1980) volatility measures. This measure is seventimes more efficient than using the typical close to close between intervals. 14 The Garman Klass volatility measure is defined by: Var(GK) 1 2 [ln(High) ln(Low) 2 [2 ln(2) 1] ln(Open) ln(Close) 2 (1) Table 1 provides summary ....

Garman, M., and Klass, M., "On the Estimation of Security Price Volatilities from Historical Data." Journal of Business, Vol. 53 No. 1 (January 1980), pp. 67-78.

....noisy indicator of the trajectory of volatility over the sampling interval (Andersen and Bollerslev, 1998) Financial economists have long known that the daily range of the price series contains extra information about the course of volatility over the day. Within a constant volatility framework, Garman and Klass (1980) and Parkinson (1980) show that use of the range can improve volatility estimates by as much as a factor of eight over the standard estimate. Beckers (1983) and Hsieh (1991) present related results and strong empirical documentation on the efficiency improvement. This paper adopts the stochastic ....

Garman, Mark B., and Michael J. Klass, "On the Estimation of Security Price Volatilities from Historical Data," Journal of Business 53 (1980), 67--78.

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Garman, M. and M. Klass (1980), "On the estimation of security price volatilities from historical data", Journal of Business, 53, 67-78.

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Garman, M. B. and Klass, M. J. 1980, On the Estimation of Security Price Volatilities from Historical Data, Journal of Business 53: 67-78.

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