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48
Forecasting future volatility from option prices, Working
, 2000
"... Weisbach are gratefully acknowledged. I bear full responsibility for all remaining errors. Forecasting Future Volatility from Option Prices Evidence exists that option prices produce biased forecasts of future volatility across a wide variety of options markets. This paper presents two main results. ..."
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Weisbach are gratefully acknowledged. I bear full responsibility for all remaining errors. Forecasting Future Volatility from Option Prices Evidence exists that option prices produce biased forecasts of future volatility across a wide variety of options markets. This paper presents two main results. First, approximately half of the forecasting bias in the S&P 500 index (SPX) options market is eliminated by constructing measures of realized volatility from five minute observations on SPX futures rather than from daily closing SPX levels. Second, much of the remaining forecasting bias is eliminated by employing an option pricing model that permits a nonzero market price of volatility risk. It is widely believed that option prices provide the best forecasts of the future volatility of the assets which underlie them. One reason for this belief is that option prices have the ability to impound all publicly available information – including all information contained in the history of past prices – about the future volatility of the underlying assets. A second related reason is that option pricing theory maintains that if an option prices fails to embody optimal forecasts of the future volatility of the underlying asset, a profitable trading strategy should be available whose implementation would push the option price to the level that reflects the best possible forecast of future volatility.
Conditional Risk and Performance Evaluation: Volatility Timing, Overconditioning, and New Estimates of Momentum Alphas
, 2009
"... Unconditional alpha estimates are biased when conditional beta covaries with market risk premia (“markettiming”) or volatility (“volatilitytiming”). We demonstrate an additional bias (“overconditioning”) that can occur any time an empiricist uses a risk proxy not in the investor information set — ..."
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Cited by 8 (1 self)
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Unconditional alpha estimates are biased when conditional beta covaries with market risk premia (“markettiming”) or volatility (“volatilitytiming”). We demonstrate an additional bias (“overconditioning”) that can occur any time an empiricist uses a risk proxy not in the investor information set — for example when asset payoffs are nonlinear and the conditional loading is proxied by contemporaneous realized beta. Calibrating to U.S. equity returns, volatilitytiming and overconditioning plausibly impact alphas much more than markettiming, which has been the focus of prior literature. A variety of instrumental variables estimators using realized betas can substantially correct market and volatilitytiming biases, while eliminating overconditioning. Empirically, appropriate instrumentation reduces momentum alphas by 2040 % relative to unconditional, whereas overconditioned alphas overstate performance by up to 2.5 times. Volatilitytiming inflates unconditionally estimated momentum alpha because the formationperiod market return (i) positively predicts holdingperiod beta (Grundy and Martin, 2001) and (ii) negatively predicts holdingperiod market volatility (French, Schwert, and Stambaugh,
Stochastic volatility: origins and overview
 Handbook of Financial Time Series
, 2008
"... Stochastic volatility (SV) models are used heavily within the fields of financial economics and mathematical finance to capture the impact of timevarying volatility on financial markets and decision making. The development of the subject has been highly multidisciplinary, with results drawn from fi ..."
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Stochastic volatility (SV) models are used heavily within the fields of financial economics and mathematical finance to capture the impact of timevarying volatility on financial markets and decision making. The development of the subject has been highly multidisciplinary, with results drawn from financial economics, probability theory and econometrics blending to produce methods that
A Dual Russian Option for Selling Short
 Proc. Kolmogorov Probability Seminar
, 1993
"... We propose a new call option where the option seller pays the minimum price (in inflated dollars) that the asset has ever traded at during the time period (which may be indefinitely long) between the selling time and the delivery time (to be chosen by the seller). This option is the dual of the put ..."
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Cited by 6 (1 self)
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We propose a new call option where the option seller pays the minimum price (in inflated dollars) that the asset has ever traded at during the time period (which may be indefinitely long) between the selling time and the delivery time (to be chosen by the seller). This option is the dual of the put option where the option buyer receives the maximum price (in discounted dollars) that the asset has ever traded at during the time period (which may be indefinitely long) between the buying time and the exercise time (to be chosen by the buyer). Because the settlement payoff is at the minimum (for the call) or the maximum (for the put) there is reduced regret in the sense that it is not necessary for the option holder to worry about missing a good price in the recent past (of course he may regret not holding on longer) since he gets the best price up to the settlement time. We give the exact simple formula for the optimal expected present value (fair price) that can be derived from the opt...
Pricing stock options under stochastic volatility and stochastic interest rates with efficient method . . .
, 1998
"... ..."
Improving portfolio selection using optionimplied volatility and skewness, Working Paper
, 2009
"... Our objective in this paper is to examine whether one can use optionimplied information to improve the selection of portfolios with a large number of stocks, and to document which aspects of optionimplied information are most useful for improving their outofsample performance. Portfolio performa ..."
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Cited by 5 (2 self)
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Our objective in this paper is to examine whether one can use optionimplied information to improve the selection of portfolios with a large number of stocks, and to document which aspects of optionimplied information are most useful for improving their outofsample performance. Portfolio performance is measured in terms of four metrics: volatility, Sharpe ratio, certaintyequivalent return, and turnover. Our empirical evidence shows that, while using optionimplied volatility and correlation does not improve significantly the portfolio volatility, Sharpe ratio, and certaintyequivalent return, exploiting information contained in the volatility risk premium and optionimplied skewness increases substantially both the Sharpe ratio and certaintyequivalent return, although this is accompanied by higher turnover. And, the volatility risk premium and optionimplied skewness help improve not just the performance of meanvariance portfolios, but also the performance of parametric portfolios developed in Brandt, SantaClara, and Valkanov (2009).
Using Implied Volatility to Measure Uncertainty About Interest Rates.” Federal Reserve
 Bank of St. Louis Review, May/June
"... Option prices can be used to infer the level of uncertainty about future asset prices. The first two parts of this article explain such measures (implied volatility) and how they can differ from the market’s true expectation of uncertainty. The third then estimates the implied volatility of threemon ..."
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Cited by 4 (2 self)
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Option prices can be used to infer the level of uncertainty about future asset prices. The first two parts of this article explain such measures (implied volatility) and how they can differ from the market’s true expectation of uncertainty. The third then estimates the implied volatility of threemonth eurodollar interest rates from 1985 to 2001 and evaluates its ability to predict realized volatility. Implied volatility shows that uncertainty about shortterm interest rates has been falling for almost 20 years, as the levels of interest rates and inflation have fallen. And changes in implied volatility are usually coincident with major news about the stock market, the real economy, and monetary policy. Federal Reserve Bank of St. Louis Review, May/June 2005, 87(3), pp. 40725. Economists often use asset prices along with models of their determination to derive financial markets ’ expectations of events. For example, monetary economists use federal funds futures prices to measure expectations of interest rates (Krueger and Kuttner, 1995; Pakko and Wheelock, 1996). Similarly, a large literature on fixed and target zone exchange rates has used forward exchange rates to measure the credibility of exchange rate regimes or to predict their collapse (Svensson,
Are European equity markets efficient? New evidence from fractal analysis
"... Abstract: Fractal analysis is carried out on the stock market indices of six developed European countries. Evidence is found of longrange autocorrelation in the log return series of the Mibtel, the index of the Italian stock market, in contravention of the Random Walk Hypothesis. Longrange autocor ..."
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Abstract: Fractal analysis is carried out on the stock market indices of six developed European countries. Evidence is found of longrange autocorrelation in the log return series of the Mibtel, the index of the Italian stock market, in contravention of the Random Walk Hypothesis. Longrange autocorrelation implies that predictable patterns in the log returns do not dissipate quickly, and may therefore produce potential arbitrage opportunities. No evidence contrary to the Random Walk Hypothesis is found for the other five stock markets.
How to Account for Virtual Arbitrage in the Standard Derivative Pricing
"... In this short note we show how virtual arbitrage opportunities can be modelled and included in the standard derivative pricing without changing the general framework. Whatever people say about the drawbacks of the BlackScholes (BS) approach [1] to derivative pricing, it is a standard method and al ..."
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In this short note we show how virtual arbitrage opportunities can be modelled and included in the standard derivative pricing without changing the general framework. Whatever people say about the drawbacks of the BlackScholes (BS) approach [1] to derivative pricing, it is a standard method and almost any pricing and hedging software in financial institutions is based on it. Practitioners have got used to BSlike partial differential equations, martingales and other related mathematical animals. Both analytical and numerical methods are well developed and it is hardly surprising that practitioners are rather reluctant to "buy" complicated new theories. That is why it is interesting to see how some limitations of BS analysis can be overcome in the same mathematical framework without disturbing the foundations. One way to improve BS is to use a more realistic price process instead of the geometrical random walk. The most popular alternatives are ARCHGARCH models where the volatility of...