Analogy Making and the Puzzles of Index Option Returns and Implied Volatility Skew

2014 ◽  
Author(s):  
Hammad Siddiqi
Keyword(s):  
Implied Volatility ◽  
Option Returns ◽  
Index Option

The Puzzle of Index Option Returns

CFA Digest ◽  
2014 ◽  
Vol 44 (6) ◽  
Author(s):  
Brindha Gunasingham
Keyword(s):  
Option Returns ◽  
Index Option

scholarly journals Volatility Forecasts for the RTS Stock Index: Option-Implied Volatility Versus Alternative Methods

2019 ◽  
Author(s):  
Guglielmo Maria Caporale ◽  
Daria Teterkina
Keyword(s):  
Implied Volatility ◽  
Stock Index ◽  
Alternative Methods ◽  
Index Option

scholarly journals Index option returns and systemic equity risk

2018 ◽  
Vol 4 (4) ◽  
pp. 273-298
Author(s):  
Weiping Li ◽  
Tim Krehbiel
Keyword(s):  
Option Returns ◽  
Equity Risk ◽  
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.

scholarly journals Optimal Option Portfolio Strategies: Deepening the Puzzle of Index Option Mispricing

2017 ◽  
Vol 52 (1) ◽  
pp. 277-303 ◽  
Author(s):  
José Afonso Faias ◽  
Pedro Santa-Clara
Keyword(s):  
Asset Allocation ◽  
Risk Premia ◽  
Volatility Risk ◽  
Short Sample ◽  
Option Returns ◽  
Portfolio Strategies ◽  
Portfolio Strategy ◽  
Out Of Sample ◽  
Index Option ◽  
Mean Variance

Traditional methods of asset allocation (such as mean–variance optimization) are not adequate for option portfolios because the distribution of returns is non-normal and the short sample of option returns available makes it difficult to estimate their distribution. We propose a method to optimize a portfolio of European options, held to maturity, with a myopic objective function that overcomes these limitations. In an out-of-sample exercise incorporating realistic transaction costs, the portfolio strategy delivers a Sharpe ratio of 0.82 with positive skewness. This performance is mostly obtained by exploiting mispricing between options and not by loading on jump or volatility risk premia.

Do the Option Prices Forecast Spot Price? Evidence from the KOSPI 200 Index Option Market

2011 ◽  
Vol 19 (3) ◽  
pp. 251-280
Author(s):  
Byungwook Choi
Keyword(s):  
Implied Volatility ◽  
Parametric Method ◽  
Trading Strategy ◽  
Stock Index ◽  
Rate Of Return ◽  
Spot Price ◽  
Option Prices ◽  
Daily Rate ◽  
Risk Neutral ◽  
Index Option

This study investigates a forecasting power of volatility curvatures and risk neutral densities implicit in KOSPI 200 option prices by analyzing minute by minute historical index option intraday trading data from January of 2007 to January of 2011. We begin by estimating implied volatility functions and risk neutral price densities based on non-parametric method every minute and by calculating volatility curvature and skewness premium. We then compare the daily rate of return of the signal following trading strategy that we buy (sell) a stock index when the volatility curvature or skewness premium increases (decreases) with that of an intraday buy-and-hold strategy that we buy a stock index on 9:05AM and sell it on 2:50PM. We found that the rate of return of the signal following trading strategy was significantly higher than that of the intraday buy-and-hold strategy, which implies that the option prices have a strong forecasting power on the direction of stock market. Another finding is that the information contents of option prices disappear after three or four minutes.

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.

The Determinants of the KOSPI200 Index Option Returns

2016 ◽  
Vol 24 (1) ◽  
pp. 65-96
Author(s):  
Byung Jin Kang
Keyword(s):  
Cross Section ◽  
Global Financial Crisis ◽  
Factor Model ◽  
Related Factor ◽  
Related Factors ◽  
Cross Sectional ◽  
Option Returns ◽  
The Cross ◽  
The Global Financial Crisis ◽  
Index Option

This study examines the effects of crisis-related factors on the returns of KOSPI200 index options using a factor model, which was introduced by Constantinides, Jackwerth and Savov (2013). Three factors incorporating price jumps, changes in volatility, and volatility jumps are considered as the crisis-related factors. With the data for the period from 2004 to 2015, we find followings : First, most of the crisis-related factor premia are statistically significant, and their signs are consistent with those expected. Second, these crisis-related factors contribute to improve the understanding of the cross-sectional variation in KOSPI200 index option returns. Third, the crisis-related factor premia became much more significant after the global financial crisis in 2008. Finally, our empirical findings are robust to whether the long options and the in-the-money options are included in the sample or not, and to whether the factor premia are constrained to equal the corresponding premia estimated from the cross-section of equities.

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