Extracting Model-Free Volatility from Option Prices: An Examination of the Vix Index

2006 ◽  
Author(s):  
George J. Jiang ◽  
Yisong S. Tian
Keyword(s):  
Option Prices ◽  
Model Free

scholarly journals Model-Free Stochastic Collocation for an Arbitrage-Free Implied Volatility, Part II

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 30
Author(s):  
Fabien Le Floc’h ◽  
Cornelis Oosterlee
Keyword(s):  
Implied Volatility ◽  
Initial Guess ◽  
Stochastic Collocation ◽  
Option Prices ◽  
Spline Collocation ◽  
Model Free ◽  
Free Interpolation ◽  
Collocation Technique ◽  
Interpolation Technique ◽  
The Stability

This paper explores the stochastic collocation technique, applied on a monotonic spline, as an arbitrage-free and model-free interpolation of implied volatilities. We explore various spline formulations, including B-spline representations. We explain how to calibrate the different representations against market option prices, detail how to smooth out the market quotes, and choose a proper initial guess. The technique is then applied to concrete market options and the stability of the different approaches is analyzed. Finally, we consider a challenging example where convex spline interpolations lead to oscillations in the implied volatility and compare the spline collocation results with those obtained through arbitrage-free interpolation technique of Andreasen and Huge.

An Empirical Research on the Informational Efficiency of Model Free Implied Volatility

2008 ◽  
Vol 16 (2) ◽  
pp. 67-94
Author(s):  
Byung Kun Rhee ◽  
Sang Won Hwang
Keyword(s):  
Implied Volatility ◽  
Realized Volatility ◽  
Informational Efficiency ◽  
Option Prices ◽  
Index Options ◽  
Model Free ◽  
Black Scholes ◽  
Option Pricing Model ◽  
Korean Market ◽  
Better Than

Black-Scholes Imolied volatility (8SIV) has a few drawbacks. One is that the model Is not much successful in fitting the option prices. and It Is n야 guaranteed the model is correct one. Second. the usual tradition in using the BSIV is that only at-the-money Options are used. It is well-known that IV's of In-the-money or Qut-of-the-money ootions are much different from those estimated from near-the-money options. In this regard, a new model is confronted with Korean market data. Brittenxmes and Neuberger (2000) derive a formula for volatility which is a function of option prices‘ Since the formula is derived without using any option pricing model. volatility estimated from the formula is called model-tree implied volatillty (MFIV). MFIV overcomes the two drawbacks of BSIV. Jiang and Tian (2005) show that. with the S&P index Options (SPX), MFIV is suoerlor to historical volatility (HV) or BSIV in forecasting the future volatllity. In KOSPI 200 index options, when the forecasting performances are compared, MFIV is better than any other estimated volatilities. The hypothesis that MFIV contains all informations for realized volatility and the other volatilities are redundant is oot rejected in any cases.

FORWARD AND FUTURE IMPLIED VOLATILITY

2011 ◽  
Vol 14 (03) ◽  
pp. 407-432 ◽  
Author(s):  
PAUL GLASSERMAN ◽  
QI WU
Keyword(s):  
Implied Volatility ◽  
Transition Density ◽  
Option Prices ◽  
Piecewise Constant ◽  
Model Based ◽  
Model Free ◽  
Out Of Sample ◽  
Sabr Model ◽  
Using Data ◽  
Better Than

We address the problem of defining and calculating forward volatility implied by option prices when the underlying asset is driven by a stochastic volatility process. We examine alternative notions of forward implied volatility and the information required to extract these measures from the prices of European options at fixed maturities. We then specialize to the SABR model and show how the asymptotic expansion of the bivariate transition density in Wu (forthcoming) allows calibration of the SABR model with piecewise constant parameters and calculation of forward volatility. We then investigate empirically whether current option prices at multiple maturities contain useful information in predicting future option prices and future implied volatility. We undertake this investigation using data on options on the euro-dollar, sterling-dollar, and dollar-yen exchange rates. We find that prices across maturities do indeed have predictive value. Moreover, we find that model-based forward volatility extracts this predicative information better than a standard "model-free" measure of forward volatility and better than spot implied volatility. The enhancement to out-of-sample forecasting accuracy gained from model-based forward volatility is greatest at longer forecasting horizons.

Model-Free Option Prices

2014 ◽  
Author(s):  
Kuo-Ping Chang
Keyword(s):  
Option Prices ◽  
Model Free

scholarly journals Option prices and model-free measurement of implied herd behavior in stock markets

2015 ◽  
Vol 02 (02) ◽  
pp. 1550012 ◽  
Author(s):  
Daniël Linders ◽  
Jan Dhaene ◽  
Wim Schoutens
Keyword(s):  
Stock Prices ◽  
Stock Markets ◽  
Random Variables ◽  
Herd Behavior ◽  
Option Prices ◽  
Market Sentiment ◽  
Model Free ◽  
Risk Neutral ◽  
Market Perception ◽  
Future Date

In this paper, we introduce two classes of indices which can be used to measure the market perception concerning the degree of dependency that exists between a set of random variables, representing different stock prices at a fixed future date. The construction of these measures is based on the theory of comonotonicity. Both types of herd behavior indices are model-free and risk-neutral, derived from available option data. Depending on its particular definition, each index represents a particular aspect of the market sentiment concerning future co-movement of the underlying stock prices.

scholarly journals Option Prices and Model-Free Measurement of Implied Herd Behavior in Stock Markets

2014 ◽  
Author(s):  
Daniil Linders ◽  
Jan Dhaene ◽  
Wim Schoutens
Keyword(s):  
Stock Markets ◽  
Herd Behavior ◽  
Option Prices ◽  
Model Free

scholarly journals Option Prices and Model-Free Measurement of Implied Herd Behavior in Stock Markets

2014 ◽  
Author(s):  
Daniil Linders ◽  
Jan Dhaene ◽  
Wim Schoutens
Keyword(s):  
Stock Markets ◽  
Herd Behavior ◽  
Option Prices ◽  
Model Free

A Theory of Dissimilarity Between Stochastic Discount Factors

2020 ◽  
Author(s):  
Gurdip Bakshi ◽  
Xiaohui Gao ◽  
George Panayotov
Keyword(s):  
Time Series ◽  
International Finance ◽  
Cross Section ◽  
Structural Models ◽  
Option Prices ◽  
Bond Prices ◽  
Currency Option ◽  
Discount Factors ◽  
Model Free ◽  
Stochastic Discount Factors

This paper proposes a measure of dissimilarity between stochastic discount factors (SDFs) in different economies. The SDFs are made comparable using the respective bond prices as the numeraire. The measure is dimensionless, synthesizes features of the risk-neutral moments of excess currency returns, and can be extracted from currency option prices. Linking theory to data, we provide evidence gathered from (i) the cross section of 45 currency option prices, (ii) the time series of currency returns, (iii) estimated SDFs using model-free restrictions, and (iv) structural models in international finance. This paper was accepted by David Simchi-Levi, finance.

Extracting Model-Free Volatility from Option Prices

2007 ◽  
Vol 14 (3) ◽  
pp. 35-60 ◽  
Author(s):  
George J. Jiang ◽  
Yisong S. Tian
Keyword(s):  
Option Prices ◽  
Model Free

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

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