A portfolio insurance strategy for volatility index (VIX) futures

The shape of the Chicago Board Options Exchange Volatility Index (VIX) term structure conveys information about the price of variance risk rather than expected changes in the VIX, a rejection of the expectations hypothesis. The second principal component, SLOPE, summarizes nearly all this information, predicting the excess returns of synthetic Standard & Poor’s (S&P) 500 variance swaps, VIX futures, and S&P 500 straddles for all maturities and to the exclusion of the rest of the term structure. SLOPE’s predictability is incremental to other proxies for the conditional variance risk premia, economically significant, and inconsistent with standard asset pricing models.

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Neural networks and arbitrage in the VIX

Digital Finance ◽

10.1007/s42521-020-00026-y ◽

2020 ◽

Vol 2 (1-2) ◽

pp. 97-115

Author(s):

Joerg Osterrieder ◽

Daniel Kucharczyk ◽

Silas Rudolf ◽

Daniel Wittwer

Keyword(s):

Implied Volatility ◽

Options Markets ◽

Market Participants ◽

Market Inefficiencies ◽

Volatility Index ◽

Arbitrage Opportunities ◽

Novel Approach ◽

Vix Futures ◽

Intraday Data ◽

Potential Strategy

Abstract The Chicago Board Options Exchange Volatility Index (VIX) is considered by many market participants as a common measure of market risk and investors’ sentiment, representing the market’s expectation of the 30-day-ahead looking implied volatility obtained from real-time prices of options on the S&P 500 index. While smaller deviations between implied and realized volatility are a well-known stylized fact of financial markets, large, time-varying differences are also frequently observed throughout the day. Furthermore, substantial deviations between the VIX and its futures might lead to arbitrage opportunities on the VIX market. Arbitrage is hard to exploit as the potential strategy to exploit it requires buying several hundred, mostly illiquid, out-of-the-money (put and call) options on the S&P 500 index. This paper discusses a novel approach to predicting the VIX on an intraday scale by using just a subset of the most liquid options. To the best of the authors’ knowledge, this the first paper, that describes a new methodology on how to predict the VIX (to potentially exploit arbitrage opportunities using VIX futures) using most recently developed machine learning models to intraday data of S&P 500 options and the VIX. The presented results are supposed to shed more light on the underlying dynamics in the options markets, help other investors to better understand the market and support regulators to investigate market inefficiencies.

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Joint pricing of VIX and SPX options with stochastic volatility and jump models

The Journal of Risk Finance ◽

10.1108/jrf-06-2014-0090 ◽

2015 ◽

Vol 16 (1) ◽

pp. 27-48 ◽

Cited By ~ 16

Author(s):

Thomas Kokholm ◽

Martin Stisen

Keyword(s):

Stochastic Volatility ◽

Stochastic Volatility Model ◽

Heston Model ◽

Option Markets ◽

Content Type ◽

Volatility Index ◽

Vix Options ◽

Vix Futures ◽

Volatility Model ◽

Joint Pricing

Purpose – This paper studies the performance of commonly employed stochastic volatility and jump models in the consistent pricing of The CBOE Volatility Index (VIX) and The S&P 500 Index (SPX) options. With the existence of active markets for volatility derivatives and options on the underlying instrument, the need for models that are able to price these markets consistently has increased. Although pricing formulas for VIX and vanilla options are now available for commonly used models exhibiting stochastic volatility and/or jumps, it remains to be shown whether these are able to price both markets consistently. This paper fills this vacuum. Design/methodology/approach – In particular, the Heston model, the Heston model with jumps in returns and the Heston model with simultaneous jumps in returns and variance (SVJJ) are jointly calibrated to market quotes on SPX and VIX options together with VIX futures. Findings – The full flexibility of having jumps in both returns and volatility added to a stochastic volatility model is essential. Moreover, we find that the SVJJ model with the Feller condition imposed and calibrated jointly to SPX and VIX options fits both markets poorly. Relaxing the Feller condition in the calibration improves the performance considerably. Still, the fit is not satisfactory, and we conclude that one needs more flexibility in the model to jointly fit both option markets. Originality/value – Compared to existing literature, we derive numerically simpler VIX option and futures pricing formulas in the case of the SVJ model. Moreover, the paper is the first to study the pricing performance of three widely used models to SPX options and VIX derivatives.

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Enhancing Portfolio Performance and VIX Futures Trading Timing with Markov-Switching GARCH Models

Mathematics ◽

10.3390/math9020185 ◽

2021 ◽

Vol 9 (2) ◽

pp. 185

Author(s):

Oscar V. De la Torre-Torres ◽

Francisco Venegas-Martínez ◽

Mᵃ Isabel Martínez-Torre-Enciso

Keyword(s):

Markov Switching ◽

Stock Index ◽

Garch Models ◽

Portfolio Performance ◽

Futures Trading ◽

Volatility Index ◽

Investment Level ◽

Vix Futures ◽

High Volatility ◽

The Impact

In the present paper, we test the use of Markov-Switching (MS) models with time-fixed or Generalized Autoregressive Conditional Heteroskedasticity (GARCH) variances. This, to enhance the performance of a U.S. dollar-based portfolio that invest in the S&P 500 (SP500) stock index, the 3-month U.S. Treasury-bill (T-BILL) or the 1-month volatility index (VIX) futures. For the investment algorithm, we propose the use of two and three-regime, Gaussian and t-Student, MS and MS-GARCH models. This is done to forecast the probability of high volatility episodes in the SP500 and to determine the investment level in each asset. To test the algorithm, we simulated 8 portfolios that invested in these three assets, in a weekly basis from 23 December 2005 to 14 August 2020. Our results suggest that the use of MS and MS-GARCH models and VIX futures leads the simulated portfolio to outperform a buy and hold strategy in the SP500. Also, we found that this result holds only in high and extreme volatility periods. As a recommendation for practitioners, we found that our investment algorithm must be used only by institutional investors, given the impact of stock trading fees.

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Behind the Volatility Index Levels: The Paradox of 2016

International Research in Economics and Finance ◽

10.20849/iref.v1i1.270 ◽

2017 ◽

Vol 1 (1) ◽

pp. 44

Author(s):

G. D. Hancock

Keyword(s):

Strong Evidence ◽

Abnormal Returns ◽

Investor Sentiment ◽

Market Volatility ◽

Volatility Index ◽

Daily Data ◽

Vix Futures ◽

Negative News ◽

2016 Election ◽

Time Periods

The low 2016 volatility index levels present a paradox in light of previous research suggesting periods of uncertainty and negative news events should reflect higher VIX levels. This study uses daily data for the VIX, VIX futures and the VVIX, to examine the information content of variations in the natural logarithmic changes in the index levels relative to 12 other parallel time periods encompassing 2004-2016. Straight-forward variation and predictive tests are constructed to determine signs of unusual market volatility behavior. The results reveal strong evidence of unusual volatility behavior during the 2016 election period, pocked by frequent periods of abnormal returns. The 2016 VIX levels alone are shown to be insufficient to draw conclusions regarding investor sentiment.

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Volatility as an Asset Class: Holding VIX in a Portfolio

The Journal of Alternative Investments ◽

10.3905/jai.2018.21.2.052 ◽

2018 ◽

Cited By ~ 2

Keyword(s):

Abnormal Returns ◽

Volatility Index ◽

Asymmetric Response ◽

Vix Futures ◽

Asset Class ◽

Portfolio Managers ◽

Price Movements ◽

Sharpe Ratios

Portfolio managers have long sought the ability to increase diversification and hedge market downturns without sacrificing upside returns. Using volatility as a diversifying asset is an attractive proposition because of volatility’s asymmetric response to underlying price movements. Theoretically, being able to hold the CBOE Volatility Index in a portfolio should provide substantial benefits to a portfolio. The authors find, however, that currently available VIX-related products are costly in their implementation and yield negative abnormal returns. Even so, if investors insist on investing in volatility assets, using VIX futures offers the best Sharpe ratios.

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