Standard deviations implied in option prices as predictors of future stock price variability

Beckers, Stan

doi:10.1016/0378-4266(81)90032-7

Cited by 331 publications

(186 citation statements)

References 12 publications

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“…Early empirical studies by Latané and Rendleman (1976), Chiras and Manaster (1978) and Beckers (1981) have indicated that implied volatility, when compared with historical standard deviations, can be regarded as a good predictor of future volatility. Implied volatility is often referred to as the market's volatility forecast and is said to be forward looking as opposed to historical based methods which are by definition backward looking.…”

Section: Introductionmentioning

confidence: 99%

Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements

Koopman

Jungbacker

Hol

2005

Journal of Empirical Finance

465

205

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe increasing availability of financial market data at intraday frequencies has not only led to the development of improved volatility measurements but has also inspired research into their potential value as an information source for volatility forecasting. In this paper we explore the forecasting value of historical volatility (extracted from daily return series), of implied volatility (extracted from option pricing data) and of realised volatility (computed as the sum of squared high frequency returns within a day). First we consider unobserved components and long memory models for realised volatility which is regarded as an accurate estimator of volatility. The predictive abilities of realised volatility models are compared with those of stochastic volatility models and generalised autoregressive conditional heteroskedasticity models for daily return series. These historical volatility models are extended to include realised and implied volatility measures as explanatory variables for volatility. The main focus is on forecasting the daily variability of the Standard & Poor's 100 stock index series for which trading data (tick by tick) of almost seven years is analysed. The forecast assessment is based on the hypothesis of whether a forecast model is outperformed by alternative models. In particular, we will use superior predictive ability tests to investigate the relative forecast performances of some models. Since volatilities are not observed, realised volatility is taken as a proxy for actual volatility and is used for computing the forecast error. A stationary bootstrap procedure is required for computing the test statistic and its p-value. The empirical results show convincingly that realised volatility models produce far more accurate volatility forecasts compared to models based on daily returns. Long memory models seem to provide the most accurate forecasts.JEL classification: C22, C53, G15.

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Section: Introductionmentioning

confidence: 99%

Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements

Koopman

Jungbacker

Hol

2005

Journal of Empirical Finance

465

205

View full text Add to dashboard Cite

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“…In order to solve this problem, Lemgruber (1995) recommends 2 alternatives: (i) to calculate a weighted-average implied volatility; and (ii) to use the implied volatility associated with the at-the-money (ATM) option. As suggested by Beckers (1981), ATM options are better than any other approaches based on weightedaverages. Additionally, these options are usually the most traded instruments, best reproducing market 3 Christensen and Prabhala (1998), Jorion (1995) and Navatte and Villa (2000).…”

Section: Data Descriptionmentioning

confidence: 97%

Investigating unusual changes in real-dollar exchange rate

Gomes¹,

Takami²,

Brandi³

2008

Rev. Bras. Econ.

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A previsibilidade das distribuições de preços de ativos financeiros consiste em tema relevante tanto na literatura de risco como de apreçamento de ativos. Interesse especial reside na investigação acerca das probabilidades dos eventos de cauda, cujas realizações apresentam impacto de maior severidade comparativamente ao previsto pela distribuição Normal. Este trabalho tem como objetivo verificar se as volatilidades implícitas de opções de dólar detêm conteúdo informacional e poder preditivo em relação a retornos de grande magnitude. Nossos resultados sugerem que essas expectativas de mercado, de fato, carregam informação relevante sobre retornos futuros, além de conter poder preditivo significativo. Price distributions forecast has become a relevant subject for risk and pricing literature. Special concern resides on tail probabilities, which usually present more severe observations than Normal distributions would predict. This work aims to verify if the volatility implied in real-dollar options contains useful information about unexpected large-magnitude returns. Implied volatility is

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“…The empirical results are at best mixed. Earlier research by Latane and Rendleman (1976), Schmalensee and Trippi (1978), Chiras and Manaster (1978), Beckers (1981) indicate that IV is a better predictor of actual volatility than volatility based on historical data. Lamourex and Lastrapes (1993) conduct a joint test of the Hull-White (1987) option pricing model and market efficiency, and they find that although IV helps predict volatility, available information in historical data can be used to improve the market's forecasts as measured by IV.…”

Section: Introductionmentioning

confidence: 93%

Modeling Volatility in Foreign Currency Option Pricing

Hoque¹,

Chan²,

Manzur

2009

MFJ

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This paper presents a general optimization framework to forecast put and call option prices by exploiting the volatility of the options prices. The approach is flexible in that different objective functions for predicting the underlying volatility can be modified and adapted in the proposed framework. The framework is implemented empirically for four major currencies, including Euro. The forecast performance of this framework is compared with those of the Multiplicative Error Model (MEM) of implied volatility and the GARCH(1,1). The results indicate that the proposed framework is capable of producing reasonable accurate forecasts for put and call prices.(JEL: G12, G13)

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Standard deviations implied in option prices as predictors of future stock price variability

Cited by 331 publications

References 12 publications

Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements

Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements

Investigating unusual changes in real-dollar exchange rate

Modeling Volatility in Foreign Currency Option Pricing

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