The Performance of Equity Index Option Strategy Returns During the Financial Crisis

2015 ◽  
Author(s):  
Dominik Schulte
Keyword(s):  
Financial Crisis ◽  
Equity Index ◽  
Index Option

scholarly journals ON THE RELATIONSHIP OF IMPLIED, REALIZED AND HISTORICAL VOLATILITY: EVIDENCE FROM NSE EQUITY INDEX OPTIONS

2014 ◽  
Vol 15 (5) ◽  
pp. 915-934 ◽  
Author(s):  
Puja Padhi ◽  
Imlak Shaikh
Keyword(s):  
Instrumental Variable ◽  
Implied Volatility ◽  
Return Volatility ◽  
Volatility Forecasting ◽  
Variable Technique ◽  
Index Options ◽  
Equity Index ◽  
Relationship Of ◽  
Index Option ◽  
The Relationship

This study examines the information content of implied volatility, using the options of the underlying S&P CNX Nifty index. In this study, implied, historical and realized volatilities are calculated using non-overlapping monthly at-the-money samples. The study covers the period from introduction of options on the derivative segment of NSE, June 2001 to May 2011. The results reveal that call and put implied volatility of S&P CNX Nifty index option does contain information about future realized return volatility. This study accounts for the problem of error-in-variable and controls for it by using the instrumental variable technique. In the 2SLS estimation, the Hausman H-statistic shows that call implied volatility is measured with error. Hence, 2SLS coefficients are more consistent than the OLS estimates. Results of this study might prove to be helpful to the volatility traders in volatility forecasting and option pricing.

Implied Volatility of the KOSPI 200 Index Option Market

2015 ◽  
Vol 23 (4) ◽  
pp. 517-541
Author(s):  
Dam Cho
Keyword(s):  
Financial Crisis ◽  
Trading Volume ◽  
Implied Volatility ◽  
Spot Price ◽  
Strike Price ◽  
Market Pricing ◽  
Put Options ◽  
The Difference ◽  
Index Option ◽  
Option Market

This paper analyzes implied volatilities (IVs), which are computed from trading records of the KOSPI 200 index option market from January 2005 to December 2014, to examine major characteristics of the market pricing behavior. The data includes only daily closing prices of option transactions for which the daily trading volume is larger than 300 contracts. The IV is computed using the Black-Scholes option pricing model. The empirical findings are as follows; Firstly, daily averages of IVs have shown very similar behavior to historical volatilities computed from 60-day returns of the KOSPI 200 index. The correlation coefficient of IV of the ATM call options to historical volatility is 0.8679 and that of the ATM put options is 0.8479. Secondly, when moneyness, which is measured by the ratio of the strike price to the spot price, is very large or very small, IVs of call and put options decrease days to maturity gets longer. This is partial evidence of the jump risk inherent in the stochastic process of the spot price. Thirdly, the moneyness pattern showed heavily skewed shapes of volatility smiles, which was more apparent during the global financial crises period from 2007 to 2009. Behavioral reasons can explain the volatility smiles. When the moneyness is very small, the deep OTM puts are priced relatively higher due to investors’ crash phobia and the deep ITM calls are valued higher due to investors’ overconfidence and confirmation biases. When the moneyness is very large, the deep OTM calls are priced higher due to investors’ hike expectation and the deep ITM puts are valued higher due to overconfidence and confirmation biases. Fourthly, for almost all moneyness classes and for all sub-periods, the IVs of puts are larger than the IVs of calls. Also, the differences of IVs of deep OTM put ranges minus IVs of deep OTM calls, which is known to be a measure of crash phobia or hike expectation, shows consistent positive values for all sub-periods. The difference in the financial crisis period is much bigger than in other periods. This suggests that option traders had a stronger crash phobia in the financial crisis.

scholarly journals The Fine Structure of Equity-Index Option Dynamics

2013 ◽  
Author(s):  
Torben G. Andersen ◽  
Oleg Bondarenko ◽  
Viktor Todorov ◽  
George Tauchen
Keyword(s):  
Fine Structure ◽  
Equity Index ◽  
Index Option

scholarly journals The fine structure of equity-index option dynamics

2015 ◽  
Vol 187 (2) ◽  
pp. 532-546 ◽  
Author(s):  
Torben G. Andersen ◽  
Oleg Bondarenko ◽  
Viktor Todorov ◽  
George Tauchen
Keyword(s):  
Fine Structure ◽  
Equity Index ◽  
Index Option

Cumulative Prospect Theory, Option Returns, and the Variance Premium

2018 ◽  
Vol 32 (9) ◽  
pp. 3667-3723 ◽  
Author(s):  
Lieven Baele ◽  
Joost Driessen ◽  
Sebastian Ebert ◽  
Juan M Londono ◽  
Oliver G Spalt
Keyword(s):  
Prospect Theory ◽  
Cumulative Prospect Theory ◽  
Probability Weighting ◽  
Asset Pricing Model ◽  
Equilibrium Asset Pricing ◽  
Option Returns ◽  
Dynamic Setting ◽  
Editorial Decision ◽  
Equity Index ◽  
Index Option

Abstract We develop a tractable equilibrium asset pricing model with cumulative prospect theory (CPT) preferences. Using GMM on a sample of U.S. equity index option returns, we show that by introducing a single common probability weighting parameter for both tails of the return distribution, the CPT model can simultaneously generate the otherwise puzzlingly low returns on both out-of-the-money put and out-of-the-money call options as well as the high observed variance premium. In a dynamic setting, probability weighting and time-varying equity return volatility combine to match the observed time-series pattern of the variance premium. Received May 30, 2017; editorial decision August 10, 2018 by Editor Andrew Karolyi.

Leverage Effect, Volatility Feedback, and Self-Exciting Market Disruptions

2017 ◽  
Vol 52 (5) ◽  
pp. 2119-2156 ◽  
Author(s):  
Peter Carr ◽  
Liuren Wu
Keyword(s):  
Option Pricing ◽  
Cash Flows ◽  
Stock Option ◽  
Financial Leverage ◽  
Relative Contribution ◽  
Risk Increase ◽  
Volatility Feedback ◽  
The Cost ◽  
Equity Index ◽  
Index Option

Equity index volatility variation and its interaction with the index return can come from three distinct channels. First, index volatility increases with the market’s aggregate financial leverage. Second, positive shocks to systematic risk increase the cost of capital and reduce the valuation of future cash flows, generating a negative correlation between the index return and its volatility, regardless of financial leverage. Finally, large negative market disruptions show self-exciting behaviors. This article proposes a model that incorporates all three channels and examines their relative contribution to index option pricing and stock option pricing for different types of companies.

Pricing Efficiency of Equity Index Option Contracts: Evidence from National Stock Exchange of India

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Uma Shankar ◽  
Kapil Gupta
Keyword(s):  
Stock Exchange ◽  
Index Options ◽  
Mean Squared Errors ◽  
U Statistics ◽  
Black Scholes Model ◽  
Pricing Efficiency ◽  
Black Scholes ◽  
Risk Free Rate ◽  
Equity Index ◽  
Index Option

The present study examines the pricing efficiency of equity index options traded at National Stock Exchange of India by comparing the premium of options contracts traded on NIFTY, BANK NIFTY, CNXIT, NIFTY JUNIOR, and CNX100 indices with their respective theoretical price estimated by using Black-Scholes Model. 91-day T-Bill rate is used as risk-free rate and standard deviation computed on daily returns of the underlying index is used as volatility to estimate the theoretical prices. Pricing efficiency has been tested both for daily closing prices per se as well as for the In-the-Money, At-the-Money, and Out-of-the-Money options contracts. Mean Absolute Errors (MAE), Mean Squared Errors (MSE), Root Mean Squared Errors (RMSE), and Theils U statistics suggest that the premium of equity index options contracts do not follow Black-Scholes Model.

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