DOCSLIB.ORG

The Price of Interest Rate Variance Risk and Optimal Investments in Interest Rate Derivatives

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Price of Interest Rate Variance Risk and Optimal Investments in Interest Rate Derivatives The price of interest rate variance risk and optimal investments in interest rate derivatives Anders B. Trolle Copenhagen Business School Abstract Recent research on unspanned stochastic variance raises the possibility that interest rate derivatives constitute an important component of optimal fixed income portfo- lios. In this paper, I estimate a flexible dynamic term structure model that allows for unspanned stochastic variance on an extensive data set of swaps and swaptions. I find that variance risk is predominantly unspanned by bonds, and that the price of risk on the unspanned variance factor is significantly larger in absolute value than the prices of risk on the term structure factors. Consequently, Sharpe ratios on variance sensitive derivatives are about three times larger than Sharpe ratios on bonds or short-term bond futures. These findings are corroborated by an analysis of the Treasury futures market, where the variance risk premium is estimated with a model independent approach. I then solve the dynamic portfolio choice problem for a long-term fixed income investor with and without access to interest rate derivatives and find substantial utility gains from participating in the derivatives market. JEL Classification: G11 Keywords: Portfolio choice, derivatives, stochastic variance, swaps, Treasury futures This version: December 2008 ——————————— I have benefitted from discussions with Giovanni Barone-Adesi, Michael Brennan, Wolfgang B¨uhler, Pierre Collin-Dufresne, David Lando, Francis Longstaff, Claus Munk, Carsten Sørensen and seminar participants at Copenhagen Business School. I am particularly grateful to Eduardo Schwartz for extensive comments. Some of the results in this paper were previously circulated under the title “Dynamic interest rate derivative strategies in the presence of unspanned stochastic volatility”. I thank the Danish Social Science Research Council for financial support. Address: Department of Finance, Copenhagen Business School, Solbjerg Plads 3, DK-2000 Frederiksberg, Denmark. Phone: +45 3815 3058. Email: abt.fi@cbs.dk. 1 Introduction The market for interest rate derivatives has grown rapidly over the last decade. For instance, the notional amount of outstanding over-the-counter interest rate options has increased 691 percent from USD 7.6 trillion in June 1998 to USD 62.1 trillion in June 2008, see BIS (2008). In addition, many standard fixed income securities such as mortgage-backed securities and agency securities imbed interest rate options. While there is an enormous amount of literature on the pricing, hedging, and risk management of interest rate derivatives, few papers view interest rate derivatives from a portfolio perspective, despite the fact that this issue is obviously important for many fixed income investors. This paper attempts to fill this gap in the literature. One reason that the portfolio choice literature has ignored interest rate derivatives is that standard term structure models assume that the fixed income market is complete in the sense that variance risk is completely spanned by bonds. In these models, interest rate derivatives are redundant securities that can be perfectly replicated by trading in the underlying bonds. However, this assumption has been challenged in recent years with a number of papers showing that a component of variance risk is not spanned by bonds and, therefore, that interest rate derivatives are not redundant securities. Unspanned stochastic variance was first discussed by Collin-Dufresne and Goldstein (2002) and further evidence in support for it has been provided by Heidari and Wu (2003), Andersen and Benzoni (2005), Li and Zhao (2006, 2008), Trolle and Schwartz (2008a), and Collin-Dufresne, Goldstein, and Jones (2008), among others. Unspanned stochastic variance raises the possibility that interest rate derivatives constitute an important component of optimal fixed income portfolios. First, to the extent that the unspanned component of variance risk is priced, derivatives improve investment opportunities and second, to the extent that that investment opportunities depend on variance, derivatives improve the ability to hedge against adverse changes in the investment opportunity set. The first goal of the paper is to analyze the extent to which interest rate variance risk – in particular, the unspanned component of variance risk – is priced. I use two approaches; first, I estimate the variance risk premium in the Treasury futures market without using a particular pricing model, and, second, I estimate the variance risk premium in the interest rate swap market using a dynamic term structure model. The reason for using different data in the two approaches is the following: the model independent analysis is predicated upon the existence of a liquid market for options across a wide range of strikes. While such a market has long existed in the case of options on Treasury futures, it has only recently emerged in the case of 1 options on interest rate swaps, i.e. swaptions, where only ATM options were actively quoted until four or five years ago. On the other hand, estimating a dynamic term structure model simultaneously on Treasury futures and their associated options is rather involved, whereas it is relatively straightforward to estimate such a model simultaneously on swaps and their associated swaptions. The two approaches complement each other. The model independent analysis provides robust estimates of the variance risk premium with which the model dependent estimates may be compared, and the model independent analysis also gives insights into the dynamics of the variance risk premium which forms the basis for parameterizing the market price of variance risk in the dynamic term structure model. To estimate the interest rate variance risk premium without a particular pricing model, I rely on synthetic variance swaps which pay the difference between the realized variance of a Treasury futures contract over the life of the swap and a fixed variance swap rate, which can be inferred from a cross-section of options on the Treasury futures contract. The average payoff or return on a sequence of such variance swaps provides a model independent estimate of the Treasury futures variance risk premium. I use daily data on 5, 10, and 30 year Treasury futures and their associated options from January 3, 1995 until March 5, 2008. I find that 1) the Treasury futures variance risk premium is significantly negative, 2) short- ing variance swaps generate Sharpe ratios that are about two to three times larger than the Sharpe ratios of the underlying Treasury futures, 3) the variance risk premium is not a com- pensation for exposure to bond or equity market risks, suggesting that there is an unspanned variance factor with a significant risk premium, and 4) the variance risk premium varies over time and becomes more negative when variance increases, particularly when the premium is measured in dollar terms. To estimate the interest rate variance risk premium within a dynamic term structure model, I develop a model that shares many features with that in Trolle and Schwartz (2008a) but has a more parsimonious structure. It has N term structure factors and one additional unspanned variance factor. Innovations to variance and the term structure may be correlated so that variance can contain both a spanned and an unspanned component. Inspired by the model independent analysis, I parameterize the variance risk premium such that it is linear in variance itself. I estimate the model using daily data on LIBOR and swap rates and short-term ATM swaptions on 2, 5, 10, and 30 year swaps from January 23, 1997 to April 30, 2008. For the last four years of the sample period, I also use unique data on swaption “smiles”, i.e. swaptions 2 with a wide range of strikes, obtained from the largest broker in the interest rate derivatives market. This “smile” information is important for estimating the extent to which variance is unspanned, and, to my knowledge, it is the first time that this data has been used in the empirical term structure literature. I consider model specifications with one, two, and three term structure factors and estimate with quasi-maximum likelihood in conjunction with the extended Kalman filter. In a methodological contribution, I derive a fast and accurate Fourier-based swaption pricing formula that enables me to estimate the model on a large set of swaptions. I find that 1) innovations to variance are only weakly related to innovations to the term structure, i.e. variance risk is predominantly unspanned, 2) the estimated market price of risk on the unspanned variance factor is strongly negative – much more negative than the prices of risk on the term structure factors – and statistically significant, and 3) the model-implied Sharpe ratio on a derivative exposed solely to variance is about three times larger in absolute value than the model-implied Sharpe ratios on bonds or short-term bond futures, consistent with the findings in the model-independent analysis. These findings hold true regardless of the number of term structure factors. The second goal of the paper is to analyze the benefits of including interest rate derivatives in fixed income portfolios. I assume that investment opportunities evolve according to the term structure model estimated in this paper. I then derive the optimal portfolio strategy for a long-term fixed income investor with CRRA utility over terminal wealth, who either does or does not participate in the interest rate derivatives market, and I compute the utility
Load more

Recommended publications
  • Arithmetic Variance Swaps
    Arithmetic variance swaps Article (Accepted Version) Leontsinis, Stamatis and Alexander, Carol (2017) Arithmetic variance swaps. Quantitative Finance, 17 (4). pp. 551-569. ISSN 1469-7688 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/id/eprint/62303/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher’s version. Please see the URL above for details on accessing the published version. Copyright and reuse: Sussex Research Online is a digital repository of the research output of the University. Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available. Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. http://sro.sussex.ac.uk Arithmetic Variance Swaps Stamatis Leontsinisa and Carol Alexanderb a RwC Asset Management, London b School of Business, Management and Economics, University of Sussex To appear in Quantitative Finance, 2016 (in press) Abstract Biases in standard variance swap rates can induce substantial deviations below market rates.
    [Show full text]
  • Further Developments in Volatility Derivatives Modeling
    Introduction Prior work Parameter estimation Model vs market prices Conclusion Further Developments in Volatility Derivatives Modeling Jim Gatheral Global Derivatives Trading & Risk Management, Paris May 21, 2008 Introduction Prior work Parameter estimation Model vs market prices Conclusion Disclaimer The opinions expressed in this presentation are those of the author alone, and do not necessarily reflect the views of of Merrill Lynch, its subsidiaries or affiliates. Introduction Prior work Parameter estimation Model vs market prices Conclusion Motivation and context We would like to have a model that prices consistently 1 options on SPX 2 options on VIX 3 options on realized variance We believe there may be such a model because we can identify relationships between options on SPX, VIX and variance. For example: 1 Puts on SPX and calls on VIX both protect against market dislocations. 2 Bruno Dupire constructs an upper bound on the price of options on variance from the prices of index options. 3 The underlying of VIX options is the square-root of a forward-starting variance swap. The aim is not necessarily to find new relationships; the aim is to devise a tool for efficient determination of relative value. Introduction Prior work Parameter estimation Model vs market prices Conclusion Outline 1 Review of prior work Double Lognormal vs Double Heston 2 Parameter estimation Time series analysis of variance curves Time series analysis of SABR fits to SPX options 3 Model vs market prices Numerical techniques Pricing of options on VIX and SPX Options on realized variance 4 Conclusion Is the model right? Introduction Prior work Parameter estimation Model vs market prices Conclusion Review of prior work We found that double-mean reverting lognormal dynamics for SPX instantaneous variance gave reasonable fits to both SPX and VIX option volatility smiles.
    [Show full text]
  • Credit Derivatives Handbook
    08 February 2007 Fixed Income Research http://www.credit-suisse.com/researchandanalytics Credit Derivatives Handbook Credit Strategy Contributors Ira Jersey +1 212 325 4674 [email protected] Alex Makedon +1 212 538 8340 [email protected] David Lee +1 212 325 6693 [email protected] This is the second edition of our Credit Derivatives Handbook. With the continuous growth of the derivatives market and new participants entering daily, the Handbook has become one of our most requested publications. Our goal is to make this publication as useful and as user friendly as possible, with information to analyze instruments and unique situations arising from market action. Since we first published the Handbook, new innovations have been developed in the credit derivatives market that have gone hand in hand with its exponential growth. New information included in this edition includes CDS Orphaning, Cash Settlement of Single-Name CDS, Variance Swaps, and more. We have broken the information into several convenient sections entitled "Credit Default Swap Products and Evaluation”, “Credit Default Swaptions and Instruments with Optionality”, “Capital Structure Arbitrage”, and “Structure Products: Baskets and Index Tranches.” We hope this publication is useful for those with various levels of experience ranging from novices to long-time practitioners, and we welcome feedback on any topics of interest. FOR IMPORTANT DISCLOSURE INFORMATION relating to analyst certification, the Firm’s rating system, and potential conflicts
    [Show full text]
  • Volatility As Investment - Crash Protection with Calendar Spreads of Variance Swaps
    Journal of Applied Operational Research (2014) 6(4), 243–254 © Tadbir Operational Research Group Ltd. All rights reserved. www.tadbir.ca ISSN 1735-8523 (Print), ISSN 1927-0089 (Online) Volatility as investment - crash protection with calendar spreads of variance swaps Uwe Wystup 1,* and Qixiang Zhou 2 1 MathFinance AG, Frankfurt, Germany 2 Frankfurt School of Finance & Management, Germany Abstract. Nowadays, volatility is not only a risk measure but can be also considered an individual asset class. Variance swaps, one of the main investment vehicles, can obtain pure exposure on realized volatility. In normal market phases, implied volatility is often higher than the realized volatility will turn out to be. We present a volatility investment strategy that can benefit from both negative risk premium and correlation of variance swaps to the underlying stock index. The empirical evidence demonstrates a significant diversification effect during the financial crisis by adding this strategy to an existing portfolio consisting of 70% stocks and 30% bonds. The back-testing analysis includes the last ten years of history of the S&P500 and the EUROSTOXX50. Keywords: volatility; investment strategy; stock index; crash protection; variance swap * Received July 2014. Accepted November 2014 Introduction Volatility is an important variable of the financial market. The accurate measurement of volatility is important not only for investment but also as an integral part of risk management. Generally, there are two methods used to assess volatility: The first one entails the application of time series methods in order to make statistical conclusions based on historical data. Examples of this method are ARCH/GARCH models.
    [Show full text]
  • The Term Structure of Variance Swap Rates and Optimal Variance Swap Investments
    JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS Vol. 45, No. 5, Oct. 2010, pp. 1279–1310 COPYRIGHT 2010, MICHAEL G. FOSTER SCHOOL OF BUSINESS, UNIVERSITY OF WASHINGTON, SEATTLE, WA 98195 doi:10.1017/S0022109010000463 The Term Structure of Variance Swap Rates and Optimal Variance Swap Investments Daniel Egloff, Markus Leippold, and Liuren Wu∗ Abstract This paper performs specification analysis on the term structure of variance swap rates on the S&P 500 index and studies the optimal investment decision on the variance swaps and the stock index. The analysis identifies 2 stochastic variance risk factors, which govern the short and long end of the variance swap term structure variation, respectively. The highly negative estimate for the market price of variance risk makes it optimal for an investor to take short positions in a short-term variance swap contract, long positions in a long-term variance swap contract, and short positions in the stock index. I. Introduction The financial market is becoming increasingly aware of the fact that the re- turn variance on stock indexes is stochastic (Engle (2004)) and the variance risk is heavily priced (Carr and Wu (2009)). Associated with this recognition is the development of a large number of variance-related derivative products. The most actively traded is the variance swap contract. The contract has 0 value at inception. At maturity, the long side of the variance swap contract receives the difference be- tween a standard measure of the realized variance and a fixed rate, called the vari- ance swap rate, determined at the inception of the contract. Traditional derivative contracts such as calls, puts, and straddles also have variance risk exposure, ∗Egloff, [email protected], QuantCatalyst, Hardturmstrasse 101, 8005 Zurich, Switzerland; Leippold, [email protected], University of Zurich, Swiss Banking Institute, Plattenstrasse 14, 8032 Zurich, Switzerland; and Wu, [email protected], Baruch College, Zicklin School of Business, One Bernard Baruch Way, Box B10-225, New York, NY 10010.
    [Show full text]
  • Variance Swap Payoffs, Risk Premia and Extreme Market Conditions
    Variance swap payoffs, risk premia and extreme market conditions Jeroen V.K. Rombouts1, Lars Stentoft2 and Francesco Violante3 June 5, 2017 Abstract This paper estimates the Variance Risk Premium (VRP) directly from synthetic variance swap payoffs. Since variance swap payoffs are highly volatile, we extract the VRP by using signal extraction techniques based on a state-space representation of our model in combination with a simple economic constraint. Our approach, only requiring option implied volatilities and daily returns for the underlying, provides measurement error free estimates of the part of the VRP related to normal market conditions, and allows constructing variables indicating agents' expectations under extreme market conditions. The latter variables and the VRP generate different re- turn predictability on the major US indices. A factor model is proposed to extract a market VRP which turns out to be priced when considering Fama and French port- folios. Keywords: Variance risk premium; Variance swaps; Return predictability; Factor Model, Kalman filter, CAPM. JEL Classification: C12, C22, G12, G13 1ESSEC Business School, Av. B. Hirsch, Cergy Pontoise, France 95021. Phone: +33 1 3443 3049 - E-mail: [email protected] 2Department of Economics and Department of Statistical and Actuarial Sciences, University of Western Ontario, Social Science Centre, London, ON, Canada N6A 5C2, Phone: +1 519 661 2111 ext. 85311 - E-mail: [email protected] 3MEMOTEF - Sapienza Universit´adi Roma, Via del Castro Laurenziano 9, Roma, Italy 00161, and
    [Show full text]
  • Local Volatility, Stochastic Volatility and Jump-Diffusion Models
    IEOR E4707: Financial Engineering: Continuous-Time Models Fall 2013 ⃝c 2013 by Martin Haugh Local Volatility, Stochastic Volatility and Jump-Diffusion Models These notes provide a brief introduction to local and stochastic volatility models as well as jump-diffusion models. These models extend the geometric Brownian motion model and are often used in practice to price exotic derivative securities. It is worth emphasizing that the prices of exotics and other non-liquid securities are generally not available in the market-place and so models are needed in order to both price them and calculate their Greeks. This is in contrast to vanilla options where prices are available and easily seen in the market. For these more liquid options, we only need a model, i.e. Black-Scholes, and the volatility surface to calculate the Greeks and perform other risk-management tasks. In addition to describing some of these models, we will also provide an introduction to a commonly used fourier transform method for pricing vanilla options when analytic solutions are not available. This transform method is quite general and can also be used in any model where the characteristic function of the log-stock price is available. Transform methods now play a key role in the numerical pricing of derivative securities. 1 Local Volatility Models The GBM model for stock prices states that dSt = µSt dt + σSt dWt where µ and σ are constants. Moreover, when pricing derivative securities with the cash account as numeraire, we know that µ = r − q where r is the risk-free interest rate and q is the dividend yield.
    [Show full text]
  • Stochastic Volatility
    Chapter 8 Stochastic Volatility Stochastic volatility refers to the modeling of volatility using time-dependent stochastic processes, in contrast to the constant volatility assumption made in the standard Black-Scholes model. In this setting, we consider the pricing of realized variance swaps and options using moment matching approxima- tions. We also cover the pricing of vanilla options by PDE arguments in the Heston model, and by perturbation analysis approximations in more general stochastic volatility models. 8.1 Stochastic Volatility Models . 305 8.2 Realized Variance Swaps . 309 8.3 Realized Variance Options . 314 8.4 European Options - PDE Method . 323 8.5 Perturbation Analysis . 329 Exercises . 334 8.1 Stochastic Volatility Models Time-dependent stochastic volatility The next Figure 8.1 refers to the EURO/SGD exchange rate, and shows some spikes that cannot be generated by Gaussian returns with constant variance. 305 " This version: July 4, 2021 https://personal.ntu.edu.sg/nprivault/indext.html N. Privault Fig. 8.1: Euro / SGD exchange rate. This type data shows that, in addition to jump models that are commonly used to take into account the slow decrease of probability tails observed in market data, other tools should be implemented in order to model a possibly random and time-varying volatility. We consider an asset price driven by the stochastic differential equation √ dSt = rStdt + St vtdBt (8.1) under the risk-neutral probability measure P∗, with solution T √ 1 T ST = St exp (T − t)r + vsdBs − vsds (8.2) wt 2 wt where (vt)t∈R+ is a (possibly random) squared volatility (or variance) process (1) adapted to the filtration F generated by (Bt)t∈R .
    [Show full text]
  • NBER WORKING PAPER SERIES SIMPLE VARIANCE SWAPS Ian Martin Working Paper
    NBER WORKING PAPER SERIES SIMPLE VARIANCE SWAPS Ian Martin Working Paper 16884 http://www.nber.org/papers/w16884 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 March 2011 I am very grateful to Peter Carr, Darrell Duffie, Stefan Hunt, and Myron Scholes for their comments. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer- reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications. © 2011 by Ian Martin. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. Simple Variance Swaps Ian Martin NBER Working Paper No. 16884 March 2011 JEL No. G01,G12,G13 ABSTRACT The large asset price jumps that took place during 2008 and 2009 disrupted volatility derivatives markets and caused the single-name variance swap market to dry up completely. This paper defines and analyzes a simple variance swap, a relative of the variance swap that in several respects has more desirable properties. First, simple variance swaps are robust: they can be easily priced and hedged even if prices can jump. Second, simple variance swaps supply a more accurate measure of market-implied variance than do variance swaps or the VIX index. Third, simple variance swaps provide a better way to measure and to trade correlation. The paper also explains how to interpret VIX in the presence of jumps.
    [Show full text]
  • Interest Rate Variance Swaps and the Pricing of Fixed Income Volatility B Y ANTONIO MELE a ND Y OSHIKI O BAYA SHI
    QUANT PERSPECTIVES Interest Rate Variance Swaps and the Pricing of Fixed Income Volatility B Y ANTONIO MELE A ND Y OSHIKI O BAYA SHI ne of the pillars supporting the recent movement toward standardized measurement and trading of interest rate volatility (see http://www.garp.org/ and explicitly account for the distinct characteristics of each risk-news-and-resources/2013/december/a-push- Oto-standardize-interest-rate-volatility-trading.aspx) - - atility pricing. The meaning of this mouthful is best understood plied volatility of forward swap rates. This article provides an overview of how volatility pricing and indexing methodologies - - - Government Bonds of spanning variance swap payoffs with those of options on the same underlying. challenging, mainly because of the high dimensionality of The price of volatility derived in this framework carries a - clean and intuitive interpretation as the fair market value of ward volatility of a three-month future on the 10-year Treasury note, and that available for trading are American-style options naturally lends itself as the basis of a benchmark index for desired volatility in a strictly model-free fashion? income market and serves as the underlying for standardized The practical answer depends on the magnitude of two is- futures and options contracts for volatility trading. A model-free options-based volatility pricing methodology American option prices. A second relates to the mismatch in maturity between the options and the underlying futures — i.e., prices and exchange rates) was branded and popularized as the one-month options are used to span risks generated by three- - - - Fortunately, in practice, situations arise in which the model- ogy has been carried over to other markets, such as those for dependent components may be presumed small enough, such gold, oil and single stocks.
    [Show full text]
  • Variance Swaps
    Alexander Gairat In collaboration with IVolatility.com Variance swaps Introduction The goal of this paper is to make a reader more familiar with pricing and hedging variance swaps and to propose some practical recommendations for quoting variance swaps (see section Conclusions). We give basic ideas of variance swap pricing and hedging (for detailed discussion see [1]) and apply this analyze to real market data. In the last section we discuss the connection with volatility swaps. A variance swap is a forward contract on annualized variance, the square of the realized volatility. n−1 2 2 1 S ti+1 VR =σR = 252 Log n − 2 S ti i=1 i i @ D yy „ j j zz k k @ D {{ Its payoff at expiration is equal to 2 2 PayOff =σR − KR Where S ti - the closing level of stock on ti valuation date. n - number of business days from the Trade Date up to and including the Maturity Date. @ D When entering the swap the strike KR is typically set at a level so that the counterparties do not have to exchange cash flows (‘fair strike’). Variance swap and option delta-hedging. Advantage of trading variance swap rather than buying options is that it is pure play on realized volatility no path dependency is involved. Let us recall how path dependency appears in trading P&L of a delta-hedged option position. If we used implied volatility σi on day i for hedging option then P&L at maturity is sum of daily variance spread weighted by dollar gamma. n−1 1 2 2 2 Final P & L = ri −σi ∆t Γi Si 2 i=1 = Si+1−Si is stock return on day ri S i i ‚ H L 2 2 Γi Si =Γ i, Si Si - dollar gamma on day i.
    [Show full text]
  • Trading Volatility: What Does This Mean?
    Volatility Trading Workshop Marco Avellaneda New York University RiskMathics Trading and Risk Management Mexico, June 2016/ Rio de Janeiro, November 2016 Trading Volatility: what does this mean? Trading volatility means using equities and options to generate strategies which make or lose money when the market becomes more volatile or less volatile. One can think of market volatility as being the actual (realized) volatility of equities or, alternatively, the volatility implied by option prices (implied volatility) In current markets one can trade -- realized volatility -- implied volatility -- implied versus realized -- implied versus implied -- realized versus realized -- correlation between two or more assets -- implied correlation -- dispersion The Equity Volatility World Major Market Index S&P 500 Futures ETFS VIX options VIX futures SPX options Sector & Leveraged ETF VIX ETF Equity & Equities Options VIX ETFs Index ETF options 2. Focus on VIX futures and ETNs What is VIX? • VIX is an index which tracks the implied volatilities of first- and second- month expiration of SPX options. • Old VIX (since 1992): a weighted average of ATM implied volatilities • New VIX (since 2000): based on Variance Swap formula, representing a basket traded options. Hence New VIX is an ``investable index’’. • VIX Futures started trading in 2004 • VIX Options started trading in 2006 • VIX ETNs started trading in 2009 VIX is one of the most important indices in US equity trading, representing the ``fear gauge’’ of the Market. Statistics of VIX Levels Long
    [Show full text]