Implied Volatility Shocks and Revisit of Options Market Misreaction

2006 ◽  
Author(s):  
George J. Jiang ◽  
Yisong S. Tian
Keyword(s):  
Implied Volatility ◽  
Options Market

The implied volatility smirk in the Chinese equity options market

2021 ◽  
pp. 101624
Author(s):  
Tian Yue ◽  
Sebastian Gehricke ◽  
Jin E. Zhang ◽  
Zheyao Pan
Keyword(s):  
Implied Volatility ◽  
Options Market ◽  
Equity Options

scholarly journals Predictability of OTC Option Volatility for Future Stock Volatility

2020 ◽  
Vol 12 (12) ◽  
pp. 5200
Author(s):  
Jungmu Kim ◽  
Yuen Jung Park
Keyword(s):  
Information Content ◽  
Unbiased Estimator ◽  
Implied Volatility ◽  
Realized Volatility ◽  
Stock Index ◽  
Options Market ◽  
Over The Counter ◽  
Index Options ◽  
Stock Volatility ◽  
Future Realized Volatility

This study explores the information content of the implied volatility inferred from stock index options in the over-the-counter (OTC) market, which has rarely been studied in the literature. Using OTC calls, puts, and straddles on the KOSPI 200 index, we find that implied volatility generally outperforms historical volatility in predicting future realized volatility, although it is not an unbiased estimator. The results are more apparent for options with shorter maturity. However, while implied volatility has strong predictability during normal periods, historical volatility is superior to implied volatility during a period of crisis due to the liquidity contraction of the OTC options market. This finding suggests that the OTC options market can play a role in conveying important information to predict future volatility.

Information Content of Adjusted Implied Volatility in the KOSPI 200 Index Options Market

2009 ◽  
Vol 17 (4) ◽  
pp. 75-103
Author(s):  
Byung Jin Kang ◽  
Sohyun Kang ◽  
Sun-Joong Yoon
Keyword(s):  
Implied Volatility ◽  
Risk Premia ◽  
Risk Averse ◽  
Options Market ◽  
Jump Risk ◽  
Index Options ◽  
Volatility Risk ◽  
Model Free ◽  
Volatility Model ◽  
Forecasting Errors

This study examines the forecasting ability of the adjusted implied volatility (AIV), which is suggested by Kang, Kim and Yoon (2009), using the horserace competition with historical volatility, model-free implied volatility, and BS implied volatility in the KOSPI 200 index options market. The adjusted implied volatility is applicable when investors are not risk averse or when underlying returns do not follow a normal distribution. This implies that AIV is consistent with the presence of risk premia for other risk such as volatility risk and jump risk. Using KOSPI 200 index options, it is shown that the AIV outperforms other volatility estimates in terms of the unbiasedness for future realized volatilities as well as the forecasting errors.

scholarly journals Markov Chain Monte Carlo Method for Estimating Implied Volatility in Option Pricing

2018 ◽  
Vol 10 (6) ◽  
pp. 108
Author(s):  
Yao Elikem Ayekple ◽  
Charles Kofi Tetteh ◽  
Prince Kwaku Fefemwole
Keyword(s):  
Option Pricing ◽  
Implied Volatility ◽  
Call Option ◽  
Option Prices ◽  
Options Market ◽  
European Call Option ◽  
Black Scholes Model ◽  
Black Scholes ◽  
Real Market ◽  
Independent Path

Using market covered European call option prices, the Independence Metropolis-Hastings Sampler algorithm for estimating Implied volatility in option pricing was proposed. This algorithm has an acceptance criteria which facilitate accurate approximation of this volatility from an independent path in the Black Scholes Model, from a set of finite data observation from the stock market. Assuming the underlying asset indeed follow the geometric brownian motion, inverted version of the Black Scholes model was used to approximate this Implied Volatility which was not directly seen in the real market: for which the BS model assumes the volatility to be a constant. Moreover, it is demonstrated that, the Implied Volatility from the options market tends to overstate or understate the actual expectation of the market. In addition, a 3-month market Covered European call option data, from 30 different stock companies was acquired from Optionistic.Com, which was used to estimate the Implied volatility. This accurately approximate the actual expectation of the market with low standard errors ranging between 0.0035 to 0.0275.

scholarly journals Does Risk-Neutral Skewness Predict the Cross-Section of Equity Option Portfolio Returns?

2013 ◽  
Vol 48 (4) ◽  
pp. 1145-1171 ◽  
Author(s):  
Turan G. Bali ◽  
Scott Murray
Keyword(s):  
Stock Price ◽  
Stock Options ◽  
Implied Volatility ◽  
Asset Returns ◽  
Options Market ◽  
Short Term ◽  
Portfolio Returns ◽  
Risk Neutral ◽  
Market Factors ◽  
Equity Option

AbstractWe investigate the pricing of risk-neutral skewness in the stock options market by creating skewness assets comprised of two option positions (one long and one short) and a position in the underlying stock. The assets are created such that exposure to changes in the underlying stock price (delta), and exposure to changes in implied volatility (vega) are removed, isolating the effect of skewness. We find a strong negative relation between risk-neutral skewness and the skewness asset returns, consistent with a positive skewness preference. The returns are not explained by well-known market, size, book-to-market, momentum, short-term reversal, volatility, or option market factors.

scholarly journals Volatility Informed Trading in the Options Market: Evidence from India

2015 ◽  
Vol 17 (1) ◽  
pp. 13-22
Author(s):  
Rajesh Pathak
Keyword(s):  
Implied Volatility ◽  
Stock Exchange ◽  
Intrinsic Value ◽  
Emerging Market ◽  
Informed Trading ◽  
Options Market ◽  
Trading Activity ◽  
Market Trends ◽  
Vector Auto Regression ◽  
Auto Regression

The purpose of this paper is to investigate the trading activity in options market based on information about expected future volatility in spot market. We employ Common Implied Volatility as a measure of expected volatility and options volume and changes in Open Interests as measures of options trading activity. We first test for simultaneous information flow in the two markets using multiple regression technique. Next, we test for information based or hedge based use of options using Trivariate Vector-auto Regression framework. We further consider the classes of options moneyness and the market trends in our analysis to examine if the trader’s preference of options changes with change in description of options intrinsic value and market environment. We use daily closing data of S&P CNX Nifty Index options traded on National Stock Exchange, India. We, for the most part, find negative and significant relationship in contemporaneous regression suggesting active trading by arbitrageurs. A feedback relationship is observed in vector auto regression analysis suggesting that options are traded in India for both information based trading and hedging purposes. We also observe the relationship to be varying when market trends and classes of options moneyness are considered. This indicates that traders are not indifferent in their choice of trading venue when market conditions and factors change. The results of this study are helpful for traders in managing the risk and return of their portfolio based on volatility forecast. This study is distinctive as it examines the scarcely researched area of volatility informed trading in an emerging market set up.

scholarly journals Patterns of 50 ETF Options Implied Volatility in China: On Implied Volatility Functions

2021 ◽  
Vol 24 (1) ◽  
pp. 135-145
Author(s):  
Pengshi Li ◽  
Yan Lin ◽  
Yuting Zhong
Keyword(s):  
Implied Volatility ◽  
Stock Exchange ◽  
Empirical Work ◽  
Ordinary Least Squares ◽  
Options Pricing ◽  
Options Market ◽  
European Options ◽  
Volatility Smile ◽  
Volatility Function ◽  
Better Than

The aim of this study is to examine the volatility smile based on the European options on Shanghai stock exchange 50 ETF. The data gives evidence of the existence of a well-known U-shaped implied volatility smile for the SSE 50 ETF options market in China. For those near-month options, the implied volatility smirk is also observed. And the implied volatility remains high for the short maturity and decreases as the maturity increases. The patterns of the implied volatility of SSE 50 ETF options indicate that in-the-money options and out-of-the-money options are more expensive relative to at-the-money options. This makes the use of at-the-money implied volatility for pricing out-of- or in-the-money options questionable. In order to investigate the implied volatility, the regression-based implied volatility functions model is considered employed to study the implied volatility in this study as this method is simple and easy to apply in practice. Several classical implied volatility functions are investigated in this paper to find whether some kind of implied volatility functions could lead to more accurate options pricing values. The potential determinants of implied volatility are the degree of moneyness and days left to expiration. The empirical work has been expressed by means of simple ordinary least squares framework. As the study shows, when valuing options, the results of using volatility functions are mixed. For far-month options, using at-the-money implied volatility performs better than other volatility functions in option valuation. For near-month options, the use of volatility functions can improve the valuation accuracy for deep in-the-money options or deep out-of-the-money options. However, no particular implied volatility function performs very well for options of all moneyness level and time to maturity.

Sign in / Sign up

Export Citation Format

Share Document