Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

The Information Content of Corridor Volatility Measures During Calm and Turmoil Periods

1 Fox School of Business and Management, Temple University, Philadelphia, PA, USA
2 Department of Economics, University of Modena and Reggio Emilia, Italy
3 Department of Economics and CEFIN, University of Modena and Reggio Emilia, Italy

Special Issue: Volatility of Prices of Financial Assets

Measurement of volatility is of paramount importance in finance because of the effects on risk measurement and risk management. Corridor implied volatility measures allow us to disentangle the volatility of positive returns from that of negative returns, providing investors with additional information beyond standard market volatility. The aim of the paper is twofold. First, to propose different types of corridor implied volatility and some combinations of them as risk indicators, in order to provide useful information about investors’ sentiment and future market returns. Second, to investigate their usefulness in prediction of market returns under different market conditions (with a particular focus on the subprime crisis and the European debt crisis). The data set consists of daily index options traded on the Italian market and covers the 2005–2014 period. We find that upside corridor implied volatility measure embeds the highest information content about contemporaneous market returns, claiming the superiority of call options in measuring current sentiment in the market. Moreover, both upside and downside volatilities can be considered as barometers of investors’ fear. The volatility measures proposed have forecasting power on future returns only during high volatility periods and in particular during the European debt crisis. The explanatory power on future market returns improves when two of the proposed volatility measures are combined together in the same model.
  Figure/Table
  Supplementary
  Article Metrics

Keywords model-free implied volatility; corridor implied volatility; market turmoil; financial crises; return predictability

Citation: Elyas Elyasiani, Luca Gambarelli, Silvia Muzzioli. The Information Content of Corridor Volatility Measures During Calm and Turmoil Periods. Quantitative Finance and Economics, 2017, 1(4): 454-473. doi: 10.3934/QFE.2017.4.454

References

  • 1.Ait-Sahalia Y, Lo AW (1998) Nonparametric estimation of state-price densities implicit in financial asset prices. J Financ 53: 499–547.    
  • 2.Andersen TG, Bondarenko O (2007) Construction and Interpretation of Model-Free Implied Volatility. In: Nelken I, Volatility as an Asset Class, Risk Books, 141–181.
  • 3.Black F, Scholes M (1973) Pricing of Options and Corporate Liabilities. J Polit Econ 81: 637–654.    
  • 4.Britten-Jones M, Neuberger A (2000) Option prices, implied price processes, and stochastic volatility. J Financ 55: 839–866.    
  • 5.Carr P, Madan D (1998) Towards a theory of volatility trading. In: Jarrow R, Volatility: New Estimation Techniques for Pricing Derivatives, Vol. Risk Books, 417–427.
  • 6.Carr P, Madan D (2005) A Note on Sufficient Conditions for No Arbitrage. Fin Res Lett 2: 125–130.
  • 7.CBOE, The CBOE Volatility index: VIX. CBOE White Paper, 2009. Available from: https://www.cboe.com/micro/vix/vixwhite.pdf.
  • 8.Cremers M, Weinbaum D (2010) Deviations from Put-Call Parity and Stock Return Predictability. J Finan Quant Anal 45: 335–367.    
  • 9.Feunou B, Jahan-Parvar MJ, Okou C (2017) Downside Variance Risk Premium. J Fin Econom. In press.
  • 10.Feunou B, Jahan-Parvar MJ, Tédongap R (2016) Which parametric model for conditional skewness? Eur J Financ 22: 1237–1271.    
  • 11.Foresi S, Wu L (2005) Crash-O-Phobia: A Domestic Fear or a Worldwide Concern? J Deriva 13: 8–21.
  • 12.Giot P (2005) Relationships Between Implied Volatility Indexes and Stock Index Returns. J Portf Manage 31: 92–100.
  • 13.Jackwerth JC, Rubinstein M (1996) Recovering Probability Distributions from Option Prices. J Financ 51: 1611–1631.    
  • 14.Kozhan R, Neuberger A, Schneider P (2013) The Skew Risk Premium in the Equity Index Market. Rev Financ Stud 26: 2174–2203.    
  • 15.Lin TC, Lu X (2015) Why do options prices predict stock returns? Evidence from analyst tipping. J Bank Fin 52: 17–28.
  • 16.Liu ZF, Faff RW (2017) Hitting SKEW for SIX. Econ Model 64: 449–464.    
  • 17.Muzzioli S (2013a) The forecasting performance of corridor implied volatility in the Italian market. Comp Econ 41: 359–386.
  • 18.Muzzioli S (2013b) The Information Content of Option-Based Forecasts of Volatility: Evidence from the Italian Stock Market. Quart J Financ 3.
  • 19.Muzzioli S (2015) The optimal corridor for implied volatility: from calm to turmoil periods. J Econ Bus 81: 77–94.
  • 20.Rubbaniy G, Asmerom R, Rizvi SKA, et al. (2014) Do fear indices help predict stock returns? Quant Financ 14: 831–847.    
  • 21.Rubinstein M (1985) Nonparametric Tests of Alternative Option Pricing Models Using All Reported Trades and Quotes on the 30 Most Active CBOE Option Classes from August 23, 1976 through August 31, 1978. J Financ 40: 455–480.    
  • 22.Rubinstein M (1994) Implied Binomial Trees. J Financ 49: 771–818.    
  • 23.Whaley RE (2000) The Investor Fear Gauge. J Portf Manage 26: 12–17.

 

Reader Comments

your name: *   your email: *  

Copyright Info: 2017, Silvia Muzzioli, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0))

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved