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Archive for the ‘Realized volatility’ Category

More Than You Ever Wanted To Know About Volatility Swaps

23 Jan

Article by: Kresimir Demeterfi, Emanuel Derman, Michael Kamal, Joseph Zou
Published by: Goldman, Sachs & Co.
Date: 1999

“Volatility swaps are forward contracts on future realized stock volatility. Variance swaps are similar contracts on variance, the square of future volatility. Both of these instruments provide an easy way for investors to gain exposure to the future level of volatility.

“Unlike a stock option, whose volatility exposure is contaminated by its stock-price dependence, these swaps provide pure exposure to volatility alone. You can use these instruments to speculate on future volatility levels, to trade the spread between realized and implied volatility, or to hedge the volatility exposure of other positions or businesses.

“In this report we explain the properties and the theory of both variance and volatility swaps, first from an intuitive point of view and then more rigorously. The theory of variance swaps is more straightforward. We show how a variance swap can be theoretically replicated by a hedged portfolio of standard options with suitably chosen strikes, as long as stock prices evolve without jumps. The fair value of the variance swap is the cost of the replicating portfolio. We derive analytic formulas for theoretical fair value in the presence of realistic volatility skews. These formulas can be used to estimate swap values quickly as the skew changes.

“We then examine the modifications to these theoretical results when reality intrudes, for example when some necessary strikes are unavailable, or when stock prices undergo jumps. Finally, we briefly return to volatility swaps, and show that they can be replicated by dynamically trading the more straightforward variance swap. As a result, the value of the volatility swap depends on the volatility of volatility itself.”

Full article (PDF): Link

 
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Posted in Realized volatility, Trading ideas

 

Volatility Dynamics for the S&P500: Evidence from Realized Volatility, Daily Returns, and Option Prices

22 Jan

Article by: Peter Christoffersen, Kris Jacobs, Karim Mimouni
Published by: Department of Econometrics and Business Statistics, Monash University
Date: 16 Sep 2009

“Most recent empirical option valuation studies build on the affine square root (SQR) stochastic volatility model. The SQR model is a convenient choice, because it yields closed-form solutions for option prices. We investigate alternatives to the SQR model, by comparing its empirical performance with that of five different but equally parsimonious stochastic volatility models. We provide empirical evidence from three different sources: realized volatilities, S&P500 returns, and an extensive panel of option data. The three sources of data all point to the same conclusion: the best volatility specification is one with linear rather than square root diffusion for variance. This model captures the stylized facts in realized volatilities, it performs well in fitting various samples of index returns, and it has the lowest option implied volatility mean squared error in and out of sample.”

Full article (PDF): Link

 
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Pricing of Variance and Volatility Swaps in a stochastic volatility and jump framework

22 Jan

Article by: Emil S. F. Stamp, Thomas F. Thorsen
Published by: Department of Business Studies, Aarhus University
Date: Aug 2011

“Volatility has always been considered a key measure within the field of finance. Financial markets have changed significantly over the last century and the recent financial crises have reshaped the markets in such a way that the role of volatility has become even more pronounced than it was before. The concept finds its use within important areas such as risk management, valuation and asset pricing in general, trading and many more.

“Increased market complexity have historically spurred the demand for more exotic derivatives for directional trading and hedging. In the 1990s a new asset class arose which provided the investor with the opportunity to take a direct position, not in the underlying itself, but in its volatility. With this new derivative class, volatility is no longer viewed as side product inherent in other derivatives, but as an independent asset class of its own. Variance and volatility swaps were the first and most fundamental products to be introduced in this asset class and ever since their introduction, the market for them has exploded. The products are in nature forward contracts which at maturity exchange the difference between a fixed strike and realized variance/volatility, scaled by a predetermined notional. Both are traded OTC which makes it difficult to assess the true market size but recent estimates indicate daily trading volumes of more than $35 million notional. Both market and academic interest for these products has increased in line with demand and much research has recently been devoted to develop efficient pricing methods.”

Full article (PDF): Link

 

Probabilistic Forecasts of Volatility and its Risk Premia

07 Jan

Article by: Worapree Maneesoonthorn, Gael M. Martin, Catherine S. Forbes and Simone Grose
Published by: Department of Econometrics and Business Statistics, Monash University
Date: 21 Feb 2012

“The object of this paper is to produce distributional forecasts of asset price volatility and its associated risk premia using a non-linear state space approach. Option and spot market information on the latent variance process is captured by using dual ‘model-free’’ variance measures to define a bivariate observation equation in the state space model. The premium for variance diffusive risk is defined as linear in the latent variance (in the usual fashion) whilst the premium for variance jump risk is specified as a conditionally deterministic dynamic process, driven by a function of past measurements. The inferential approach adopted is Bayesian, implemented via a Markov chain Monte Carlo algorithm that caters for the multiple sources of non-linearity in the model and for the bivariate measure. The method is applied to spot and option price data on the S&P500 index from 1999 to 2008, with conclusions drawn about investors required compensation for variance risk during the recent financial turmoil. The accuracy of the probabilistic forecasts of the observable variance measures is demonstrated, and compared with that of forecasts yielded by alternative methods. To illustrate the benefits of the approach, it is used to produce forecasts of prices of derivatives on volatility itself. In addition, the posterior distribution is augmented by information on daily returns to produce value at risk predictions. Linking the variance risk premia to the risk aversion parameter in a representative agent model, probabilistic forecasts of (approximate) relative risk aversion are also produced.”

Full article (PDF): Link

 
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Posted in Realized volatility

 
 
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