Implied volatility forecast and option trading strategy

Liu, Dehong; Liang, Yucong; Zhang, Lili; Lung, Peter P.; Ullah, Rizwan

doi:10.1016/j.iref.2020.10.023

Cited by 13 publications

(5 citation statements)

References 19 publications

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“…Although their use is hardly new in finance, with one of the earliest studies employing neural networks to capture the rate of bank failures in the early 1990s (Tam & Kiang, 1992), in the last 5 years, the use of deep learning and related methods have become more popular and mainstream—with their applications ranging from assessing time series predictability of returns (Leippold et al, 2022) to reviewing market efficiency (Brogaard & Zareei, 2022; Dong et al, 2022) to capturing stock market sentiments based on photographs (Obaid & Pukthuanthong, 2022) and choice of words in investor conference calls (Garcia et al, 2023). In terms of their applications to modeling volatility smile and surface, some of the more relevant papers include Zeng and Klabjan (2019), Cao et al (2020), and Liu et al (2021). The study of Medvedev and Wang (2022), however, comes closest to ours in terms of the use of different implementations in a trading setting.…”

Section: Literature Review and Research Questionsmentioning

confidence: 99%

Harvesting the volatility smile in a large emerging market: A Dynamic Nelson–Siegel approach

Kumar

Agarwalla

Varma

et al. 2023

Journal of Futures Markets

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While there is a large literature on modeling volatility smile in options markets, most such studies are eventually focused on the forecasting performance of the model parameters and not on the applicability of the models in a trading environment. Drawing on the analogy of volatility smile like a term structure in the context of interest rates in fixed‐income markets, we evaluate the performance of the Dynamic Nelson–Siegel (DNS) approach to modeling the dynamics of volatility smile in a trading environment against competing alternatives. Using model‐based mispricing as our sorting criterion, and deploying a trading strategy of going long the options in the upper deciles and going short the options in the lower deciles, we show that dynamic models consistently outperform their static counterparts, with the worst dynamic model outperforming the best static model in terms of the percentage of mean returns from the trading portfolios and the Sharpe ratio. Specifically, we find that the DNS model consistently outperforms all other competing specifications on most of our selected criteria.

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Section: Literature Review and Research Questionsmentioning

confidence: 99%

Harvesting the volatility smile in a large emerging market: A Dynamic Nelson–Siegel approach

Kumar

Agarwalla

Varma

et al. 2023

Journal of Futures Markets

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“…They employ Markov-Switching GARCH models and find out significant changes in the volatility in the natural gas market between two sub-periods of the price sets before 2010 and after 2010. Neural Network Model is used in forecasting implied volatility by Liu et al (2021). Tissaoui et al (2021) investigate the impact of volatility on the illiquidity of the Saudi stock market through an ARDL approach.…”

Section: Literature Reviewmentioning

confidence: 99%

How Volatility and Herding of the Stock Markets in the Oceania Region Influence Investors and Policymakers: A Sector-Wise Exploration in Pre and Post-COVID Period

Roy¹,

Arefin²,

Mallik³

2022

IJEF

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The paper probes the sector-wise presence of volatility persistence, herding behavior and corresponding implications on investors and policymakers in the Oceania region both in Pre-COVID & Post-COVID era. The inspection is based on seven identical sectors from both Australia and New Zealand using GARCH (Generalized autoregressive conditional heteroscedasticity) methods for volatility analysis and CSAD (Cross-Sectional Absolute Deviation) method for herding behavior. This paper finds the existence of herding behavior only in the consumer discretionary sector for both countries which delineates efficient market conditions for other sectors. The market is highly favorable for the investors in Food & Beverages, IT, and Healthcare sectors in both countries due to the potential growth opportunity while Real Estate and Financial sectors should be meticulously assessed in line with the alteration of macroeconomic forces. Fiscal and monetary measures along with the influx of labor forces and technological breakthroughs should be the key concentrations for the policymakers of both countries.

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“…Especially when the option sell-side operates Naked Option, the maximum profit is only the premium, but the maximum loss is unlimited. When the market fluctuates sharply, it is easy to be called by the exchange for margin or forced liquidation (Cox et al, 1979;Liu et al, 2021). Therefore, fund management and risk control are particularly important for option sell-side.…”

Section: Background and Motivationmentioning

confidence: 99%

A Quantitative Model for Option Sell-Side Trading with Stop-Loss Mechanism by Using Random Forest

Chao¹,

Wang²,

Wu³

2021

Preprint

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Due to the characteristics of high leverage and low margin, option is very suitable for quantitative trading by applying portfolio management to control the profit and risk. The money management is an important issue to build a portfolio especially for option sell-side trader, since the profit is only the premium, while the loss is unlimited. In this research, we propose a model for option sell-side strategy to estimate the win-rate of option by the premium, time to maturity, and volatility based on statistical approach and random forest algorithm. The prediction of the model is visualized through heatmap which can reveal the profitable trading range intuitively, we use the precision score to evaluate the performance in these two models and proof the effectiveness and robustness of predictive model proposed by random forest algorithm. In the future, we plan to apply other machine learning algorithm to propose the predictive model for spread trading.

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Implied volatility forecast and option trading strategy

Cited by 13 publications

References 19 publications

Harvesting the volatility smile in a large emerging market: A Dynamic Nelson–Siegel approach

Harvesting the volatility smile in a large emerging market: A Dynamic Nelson–Siegel approach

How Volatility and Herding of the Stock Markets in the Oceania Region Influence Investors and Policymakers: A Sector-Wise Exploration in Pre and Post-COVID Period

A Quantitative Model for Option Sell-Side Trading with Stop-Loss Mechanism by Using Random Forest

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