Estimating Variance From High, Low and Closing Prices

Rogers, L. C. G.; Satchell, Stephen

doi:10.1214/aoap/1177005835

Cited by 463 publications

(333 citation statements)

References 4 publications

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“…"Stochastic volatility" models (Barndorff-Nielsen et al [26], Chib et al [75], Ghysels et al [145], Harvey and Shephard [163], Jacquier et al [174], Shephard [280], Taylor [288]), "implied volatility" models (Day and Lewis [88], Latane and Rendleman [195], Schmalensee and Trippi [272]), "historical volatility" models (Beckers [30], Garman and Klass [140], Kunitomo [190], Parkinson [263], Rogers and Satchell [269]) and "realized volatility" models are examples from the financial econometric literature of estimating volatility of asset returns.…”

Section: Other Methods Of Volatility Modelingmentioning

confidence: 99%

Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review

Degiannakis

Xekalaki

2004

Quality Technology & Quantitative Management

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Autoregressive Conditional Heteroscedasticity (ARCH) models have successfully been employed in order to predict asset return volatility. Predicting volatility is of great importance in pricing financial derivatives, selecting portfolios, measuring and managing investment risk more accurately. In this paper, a number of univariate and multivariate ARCH models, their estimating methods and the characteristics of financial time series, which are captured by volatility models, are presented. The number of possible conditional volatility formulations is vast. Therefore, a systematic presentation of the models that have been considered in the ARCH literature can be useful in guiding one's choice of a model for exploiting future volatility, with applications in financial markets.

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Section: Other Methods Of Volatility Modelingmentioning

confidence: 99%

Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review

Degiannakis

Xekalaki

2004

Quality Technology & Quantitative Management

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“…Rogers and Satchell [32] derive an extreme value estimator for the unconditional variance of an asset price which has the attractive property that it remains unbiased for any value of the drift. Suppose O t , H t , L t and C t are the opening, high, low and closing prices of an asset on day t. Define:…”

Section: Rogers and Satchell (1991) Range Based Volatility Estimatormentioning

confidence: 99%

“…Hence the unbiased extreme value estimator of variance as proposed by Rogers and Satchell [32] is given by:…”

Section: Rogers and Satchell (1991) Range Based Volatility Estimatormentioning

confidence: 99%

On Volatility Transmission from Crude Oil to Agricultural Commodities

Kumar¹

2017

TEL

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The paper examines volatility transmission from crude oil market to agricultural commodities like wheat, corn, cotton and soybeans. We find that the volatility transmission from crude oil to agricultural commodities exhibits sudden changes over a study period. We also examine whether the sudden changes in volatility influence the observed sudden changes in volatility transmission from crude oil to agricultural commodities. Our results indicate the observed sudden change in volatility transmission mechanism is not influenced by sudden changes in volatility series.

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“…The BS model adopts the assumption that the volatility is constant (Black and Scholes, 1973;Merton, 1973). In order to obtain it, the historical volatility of the underlying was proposed as proxy and several methods to calculate it has been developed (Rogers and Satchell, 1991;Rogers et al, 1994;Parkinson, 1980). The option prices using the historical volatility mismatch with the real market option prices since the assumptions of the BS model do not satisfy the real market properties.…”

Section: Introductionmentioning

confidence: 99%

Implied Volatility Function Approximation with Korean ELWs (Equity-Linked Warrants) via Gaussian Processes

Han

2014

Management Science and Financial Engineering

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A lot of researches have been conducted to estimate the volatility smile effect shown in the option market. This paper proposes a method to approximate an implied volatility function, given noisy real market option data. To construct an implied volatility function, we use Gaussian Processes (GPs). Their output values are implied volatilities while moneyness values (the ratios of strike price to underlying asset price) and time to maturities are as their input values. To show the performances of our proposed method, we conduct experimental simulations with Korean Equity-Linked Warrant (ELW) market data as well as toy data.

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Estimating Variance From High, Low and Closing Prices

Cited by 463 publications

References 4 publications

Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review

Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review

On Volatility Transmission from Crude Oil to Agricultural Commodities

Implied Volatility Function Approximation with Korean ELWs (Equity-Linked Warrants) via Gaussian Processes

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