Author: 李原豪Publisher:
ISBN:
Category :
Languages : en
Pages : 76
Book Description
Implied Volatility Spread and Future Returns
Author: 李原豪Call-Put Implied Volatility Spreads and Option Returns
Author: James DoranPublisher:
ISBN:
Category :
Languages : en
Pages : 49
Book Description
Prior literature shows that the implied volatility spread between call and put options is a bullish signal for future returns on the underlying stocks. A common interpretation is that a high call-put implied volatility spread indicates favorable private information revealed by informed option investors. However, this paper finds that a high call-put implied volatility spread is a strong bearish signal for future returns on out-of-the-money call options. Using unique data on daily option volumes, we reconcile the two pieces of seemingly contradicting evidence by showing that demand for options by sophisticated, firm investors drives the positive relationship between volatility spreads and future stock returns, while demand for options by less sophisticated, customer investors drives the negative relationship between volatility spreads and future call option returns. Taken together, our evidence suggests that call-put implied volatility spreads contain information about firm fundamentals as well as option mispricing.
Implied Volatility Spreads and Future Options Returns Around Information Events and Conditions
Author: Chuang-Chang ChangPublisher:
ISBN:
Category :
Languages : en
Pages : 45
Book Description
While numerous prior studies report that call-put implied volatility spreads positively predict future stock returns, recent literature shows that the predictive relation is negative for future call option returns. We investigate whether and, if so, how the predictive relation for options returns is influenced by various information events and conditions. In addition to confirming an opposite predictive relation for both call and put returns, we show that the predictive relation is stronger during periods of earnings announcement and/or high sentiment. In addition, we find that investors learn from informed trading and revise their predictability bias by examining the impacts of information asymmetry, stock liquidity, and options liquidity on the predictive relationships.
Volatility Spreads and Expected Stock Returns
Author: Turan G. BaliPublisher:
ISBN:
Category :
Languages : en
Pages : 33
Book Description
We examine the relation between expected future volatility (options' implied volatility) and the cross-section of expected returns. A trading strategy buying stocks in the highest implied volatility quintile and shorting stocks in the lowest implied volatility quintile generates insignificant returns. A similar strategy using one-month lagged realized volatility generates significantly negative returns. To investigate the differences and interactions between alternative measures of total risk, we estimate three principal components based on realized volatility, call implied and put implied volatility. Long-short trading strategies generate significant returns only for the second and the third principal components. We find that the second principal component is related to the realized-implied volatility spread which can be viewed as a proxy for volatility risk. We find that the third principal component is related to the call-put implied volatility spread that reflects future price increase of the underlying stock.
A Deeper Look at the Implied Volatility Spread as a Predictor of Stock Returns
Author: Maxim SokolovPublisher:
ISBN:
Category : Options (Finance)
Languages : en
Pages : 99
Book Description
"I develop a new explanation of the implied volatility spread anomaly of Bali and Hovakimian (2009) and Cremers and Weinbaum (2010). The stock price observed in the stock market and the option implied stock price inferred from the option market are two noisy sources of information about the stock value. If these sources contain enough nonredundant information, the estimate of the stock value is between these prices, and the prices are expected to revert toward this estimate. This simple model is able to explain the reversals of the option implied prices toward the stock prices. Overall, the model of noisy prices is better aligned with the empirical patterns associated with the implied volatility spread phenomenon than other existing explanations of the phenomenon. I also document that if we invest in the implied volatility spread strategy at the end of each month, the next day excess return is 71 bps, which is almost twice as high as the average daily excess return of the implied volatility spread strategy. I show that this abnormal return from the end-of-month signal does not seem to be driven by seasonal trading patterns of institutional investors. If we take into account transaction costs, active trading on the implied volatility spread is too costly even for the marginal investor. This result is consistent with the model of noisy prices. However, the implied volatility spread can be used as a signal for the optimization of other trading strategies. If the implied volatility spread is used as a screening signal for a small stocks strategy, it modestly improves the performance of the baseline strategy"--Page vii.
Vertical Option Spreads, + Website
Author: Charles Conrick, IVPublisher: John Wiley & Sons
ISBN: 1118537009
Category : Business & Economics
Languages : en
Pages : 256
Book Description
Make trades on vertical options spreads with the precision of a laser beam Vertical Options Spreads is a combination of a bona-fide academic research-based study and a complete method to trade credit and debit spreads, along with other complex option combination trades such as iron condors and butterflies. Here, the author has accumulated five years of daily data on the ETF, SPY and provided historical evidence of actual win rates at specific multiples of entry points, both in time and price level. For example, traders will be able to use the weekly options, pick a level of risk and return desired, learn how to place the trade, and then discover the actual percent return that the trade would have yielded. This must-have resource includes the basics of option trading and contains references to many excellent works by other authors that explore more about the intricacies of option mechanics and trading. It is far more than an analysis of one specific asset, SPY, featuring a study of probability theory and how it has applied to trading over the past five years, including the highly volatile 2007 to 2009 time frame and the more "normal" 2010 to 2012 time period. The book offer a thorough understanding of how price movement, actual volatility, and implied volatility all provide a complex but workable web in which the informed trader can generate excellent returns. However, the trader must have the discipline to act within the confines of probability and the "law" of large numbers refusing to place trades based on gut feelings or hunches. Offers high-probability based trading that uses the new weekly options Contains handy interactive worksheets that allow traders to select their own risk/return with precision Includes a website with daily and weekly information on the estimate of the actual standard deviation points on the price spectrum Vertical Options Spreads offers traders a research-based guide for trading Standard & Poors 500 ETF, SPY using historic and estimated probabilities and returns that will give them an edge in the marketplace.
Implied Volatility Spreads and Expected Market Returns
Author: Yigit AtilganPublisher:
ISBN:
Category :
Languages : en
Pages : 57
Book Description
This paper investigates the intertemporal relation between volatility spreads and expected returns on the aggregate stock market. We provide evidence for a significantly negative link between volatility spreads and expected returns at the daily and weekly frequencies. We argue that this link is driven by the information flow from option markets to stock markets. The documented relation is significantly stronger for the periods during which (i) S&P 500 constituent firms announce their earnings; (ii) cash flow and discount rate news are large in magnitude; and (iii) consumer sentiment index takes extreme values. The intertemporal relation remains strongly negative after controlling for conditional volatility, variance risk premium and macroeconomic variables. Moreover, a trading strategy based on the intertemporal relation with volatility spreads has higher portfolio returns compared to a passive strategy of investing in the S&P 500 index, after transaction costs are taken into account.
Relation Between Intraday Implied Volatility Spreads and Index Returns
Author: 姜奕帆Option-Implied Volatility Measures and Stock Return Predictability
Author: Fu, XiPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Using firm-level option and stock data, we examine the predictive ability of option-implied volatility measures proposed by previous studies and recommend the best measure using up-to-date data. Portfolio level analysis implies significant non-zero risk-adjusted returns on arbitrage portfolios formed on the call-put implied volatility spread, implied volatility skew, and realized-implied volatility spread. Firm-level cross-sectional regressions show that, the implied volatility skew has the most significant predictive power over various investment horizons. The predictive power persists before and after the 2008 Global Financial Crisis.
Empirical Asset Pricing
Author: Turan G. BaliPublisher: John Wiley & Sons
ISBN: 1118589475
Category : Business & Economics
Languages : en
Pages : 512
Book Description
“Bali, Engle, and Murray have produced a highly accessible introduction to the techniques and evidence of modern empirical asset pricing. This book should be read and absorbed by every serious student of the field, academic and professional.” Eugene Fama, Robert R. McCormick Distinguished Service Professor of Finance, University of Chicago and 2013 Nobel Laureate in Economic Sciences “The empirical analysis of the cross-section of stock returns is a monumental achievement of half a century of finance research. Both the established facts and the methods used to discover them have subtle complexities that can mislead casual observers and novice researchers. Bali, Engle, and Murray’s clear and careful guide to these issues provides a firm foundation for future discoveries.” John Campbell, Morton L. and Carole S. Olshan Professor of Economics, Harvard University “Bali, Engle, and Murray provide clear and accessible descriptions of many of the most important empirical techniques and results in asset pricing.” Kenneth R. French, Roth Family Distinguished Professor of Finance, Tuck School of Business, Dartmouth College “This exciting new book presents a thorough review of what we know about the cross-section of stock returns. Given its comprehensive nature, systematic approach, and easy-to-understand language, the book is a valuable resource for any introductory PhD class in empirical asset pricing.” Lubos Pastor, Charles P. McQuaid Professor of Finance, University of Chicago Empirical Asset Pricing: The Cross Section of Stock Returns is a comprehensive overview of the most important findings of empirical asset pricing research. The book begins with thorough expositions of the most prevalent econometric techniques with in-depth discussions of the implementation and interpretation of results illustrated through detailed examples. The second half of the book applies these techniques to demonstrate the most salient patterns observed in stock returns. The phenomena documented form the basis for a range of investment strategies as well as the foundations of contemporary empirical asset pricing research. Empirical Asset Pricing: The Cross Section of Stock Returns also includes: Discussions on the driving forces behind the patterns observed in the stock market An extensive set of results that serve as a reference for practitioners and academics alike Numerous references to both contemporary and foundational research articles Empirical Asset Pricing: The Cross Section of Stock Returns is an ideal textbook for graduate-level courses in asset pricing and portfolio management. The book is also an indispensable reference for researchers and practitioners in finance and economics. Turan G. Bali, PhD, is the Robert Parker Chair Professor of Finance in the McDonough School of Business at Georgetown University. The recipient of the 2014 Jack Treynor prize, he is the coauthor of Mathematical Methods for Finance: Tools for Asset and Risk Management, also published by Wiley. Robert F. Engle, PhD, is the Michael Armellino Professor of Finance in the Stern School of Business at New York University. He is the 2003 Nobel Laureate in Economic Sciences, Director of the New York University Stern Volatility Institute, and co-founding President of the Society for Financial Econometrics. Scott Murray, PhD, is an Assistant Professor in the Department of Finance in the J. Mack Robinson College of Business at Georgia State University. He is the recipient of the 2014 Jack Treynor prize.