Option Trading Strategy Research Articles

Volatility models for cryptocurrencies and applications in the options market

We investigate the effectiveness of various volatility models using the Bitcoin (BTC) and Ethereum (ETH) price series. Amongst the single-variate models, the GARCH model performs well both in sample and out of sample. Moreover, we do not observe any significant asymmetric volatility reponse to past returns in the GJR-GARCH model. Although the multi-variate VARMA-DCC-AGARCH model outperforms in sample, it performs worse out of sample than the single-variate GARCH model. Furthermore, the GARCH volatility forecast outperforms the option implied volatility in forecasting future realized volatility. We formulate an option trading strategy by exploiting the volatility spread between the GARCH volatility forecast and the option implied volatility. We show that a simple volatility-spread trading strategy with delta-hedging can yield robust profits for both BTC and ETH options. We interpret the strategy profitability as evidence for the pricing inefficiencies in the cryptocurrency options market during our sample period.

Implied volatility forecast and option trading strategy

We examine the implied volatility derived from an improved Artificial Bee Colony with Back Propagation (BP) neural network model that is Artificial Bee Colony-Back Propagation (ABC-BP) neural network model. We find that the improved model can better predict the implied volatility than basic BP neural network model and Monte Carlo simulation. Nevertheless, the option price derived from the Monte Carlo simulation is more efficient when we apply the simulation to the option straddle trading strategy. Additionally, in a robustness test we find that our proposed neural network model performs better than the traditional GARCH model in building up option trading strategies.

Options trading strategy based on ARIMA forecasting

Purpose The purpose of this paper is to illustrate a profitable and original index options trading strategy. Design/methodology/approach The methodology is based on auto regressive integrated moving average (ARIMA) forecasting of the S&P 500 index and the strategy is tested on a large database of S&P 500 Composite index options and benchmarked to the generalized auto regressive conditional heteroscedastic (GARCH) model. The forecasts validate a set of criteria as follows: the first criterion checks if the forecasted index is greater or lower than the option strike price and the second criterion if the option premium is underpriced or overpriced. A buy or sell and hold strategy is finally implemented. Findings The paper demonstrates the valuable contribution of this option trading strategy when trading call and put index options. It especially demonstrates that the ARIMA forecasting method is a valid method for forecasting the S&P 500 Composite index and is superior to the GARCH model in the context of an application to index options trading. Originality/value The strategy was applied in the aftermath of the 2008 credit crisis over 60 months when the volatility index (VIX) was experiencing a downtrend. The strategy was successful with puts and calls traded on the USA market. The strategy may have a different outcome in a different economic and regional context.

An Optimal Model for Water Resources Risk Hedging Based on Water Option Trading

The uncertainty of forecasted runoffs brings risks of water shortages to water users in the intake area of long-distance water transfer projects, and the uncertainty of spot market prices may cause them to buy water at high prices. In order to hedge these risks, this paper proposes a risk hedging model for decision-making in water option trading from the viewpoint of water users. With the objective of maximizing the expected revenue of water users, the proposed model was solved by an analytical method and an optimal water option strategy was obtained for the users. The proposed model is applied to an intake area of an inter-basin water transfer project in China. The results show that the proposed water option trading model can provide water users with an optimal option strategy. The optimal options trading strategy can effectively reduce the risk caused by the uncertainties of forecasted runoffs and water prices. We also explored the influence of the uncertainty degree of the forecasted runoffs and water price on the option trading strategy. The results show that the expected revenue of water users increases as the variances of the errors of forecasted runoffs and water prices increase.

A Novel Approach of Option Portfolio Construction Using the Kelly Criterion

Money management is one of the most important issues in financial trading. Many skills of money managements are based on the Kelly criterion, which is a theoretical optimization of bidding an optimal fraction for position sizing. However, there is still a large gap between the theory and the real trading for money management. In this paper, we design an option trading strategy via Kelly criterion. While the price movements of options are highly volatile, various options’ portfolio can be formed by long or short at different strike prices to pre-lock the losses and profits; then we have a fixed profit and loss distribution via holding an option portfolio. Consequently, the Kelly criterion can be applied to the options’ trading for calculating the optimal bidding fraction. We propose a method for option trading, in finding the profitable option portfolio by bidding optimal fraction. Compared with prior works, our proposed model is a novel approach for options’ trading with the money management of position sizing. Experiments are conducted to show the feasibility and profitability of our method in practical scenarios. Future works are provided in the final section.

Can Big Data Help Predict Financial Market Dynamics?: Evidence from the Korean Stock Market

This study quantifies the dynamic interrelationship between the KOSPI index return and search query data derived from the Naver DataLab. The empirical estimation using a bivariate GARCH model reveals that negative contemporaneous correlations between the stock return and the search frequency prevail during the sample period. Meanwhile, the search frequency has a negative association with the one-week-ahead stock return but not vice versa. In addition to identifying dynamic correlations, the paper also aims to serve as a test bed in which the existence of profitable trading strategies based on big data is explored. Specifically, the strategy interpreting the heightened investor attention as a negative signal for future returns appears to have been superior to the benchmark strategy in terms of the expected utility over wealth. This paper also demonstrates that the big data-based option trading strategy might be able to beat the market under certain conditions. These results highlight the possibility of big data as a potential source-which has been left largely untapped-for establishing profitable trading strategies as well as developing insights on stock market dynamics.

Can Big Data Help Predict Financial Market Dynamics?: Evidence from the Korean Stock Market

(ProQuest: ... denotes formulae omitted.)I. INTRODUCTIONThis paper estimates dynamic relationships between the Korean stock market index and the related online search queries within a multivariate GARCH framework. In addition, the paper also attempts to investigate whether the information from search query data can be served as a potential source for designing profitable trading strategies in the Korean stock market.The emergence of internet and social networking services combined with the extensive dissemination of smart phones have revolutionized the way we communicate and exchange information. Consequently, big data continuously flowing from increasing online activities by users have become a buzz word for recent years because of its potentials for various uses including marketing, political predictions, disease epidemics, social dynamics, etc.1Economists are one of the late professions who delves into the investigation of possible use of big data mainly for forecasting market dynamics and related issues.2 The seminal paper by Choi and Varian (2012) shows the use of Google search query data as a key predictor for various economic activities including auto sales, travels, etc.3 It stimulates subsequent studies in the field of economics, which mostly concentrates on exploiting big data to increase the prediction power of forecasting models for economic variables of own interests.The idea that the search query might contain information about subsequent actions by users is based on the premise that economic agents living in a contemporary society largely rely on the prior information-search process before making important economic decisions such as the purchase of durable goods and financial investments.However, the motivaton of information demand does not always run in this direction; the heightened information-gathering activity itself can be the manifestation of a simple endogenous response to major events in markets in quest of more information, which might yield possible effects on market developments in next rounds. These intricacies in the causal relationship between the information demand and the market outcomes make it difficult to assess correctly the real importance of big data. In spite of this complicacy, the predictive power of information generated from online big data for market activity is supported by numerous studies ranging from stock markets to housing markets.4The empirical analysis on the Korean stock market in this study reveals that the search frequency related to the Korean stock market has negative contemporaneous correlations with the KOSPI return for the majority of time with the occasional tightening of its magnitude. Furthermore, a negative association between the search query and the one-week-ahead stock return is observed, while the stock return has no statitically significant impact on the level of the future search query.Based on these observations, we experiment with a hypothetical trading strategy that interprets the increased level of online search activity as a negative signal for future stock returns so as to examine whether profitable trading schemes can be constructed out of big data. The result from this simple exercise demonstrates that the big data-based strategy outperforms the benchmark strategy in terms of the expected utility over wealth. The other experiment also shows that the big data-based option trading strategy can beat the market for certain KOSPI200 option contracts.As a result, this study is the first attempt to analyse the relationship between the KOSPI return and the degree of investor attention5 using the Korean stock market data and a Korean-based internet platform. We conjecture that the contribution is not limited to quantifying the dynamic relationships between stock retums and investor attention and estimating time-varying volatilities; it also explores the potential of big data as one of the practical tools for financial investment strategies, which is rarely pursued in the related literature. …

Accounting quality, information risk and the term structure of implied volatility around earnings announcements

We examine the association between accounting quality, which is used as a proxy for firm information risk, and the behavior of the term structure of implied option volatility around earnings announcements. By employing a large sample of US firms having options traded on their equity during 1996–2010, we find that lower (higher) accounting quality is significantly associated with stronger (weaker) changes in the steepness of the term structure of implied volatility curve around quarterly earnings announcements. This finding (which is robust to controls for business-stemming uncertainty regarding future firm performance) is consistent with a stronger differential of short vs. long-term uncertainty for higher information risk firms, indicating greater uncertainty on the future economic performance of poorer vs. stronger accounting quality firms. We also establish the trading implications of these findings by demonstrating a (profitable in-sample) self-financed option trading strategy that is based on the quality of the accounting information released on earnings announcement days.

Attention on Options

This paper investigates the impact of retail investor attention, measured by Google search frequency, on option trading and option pricing as well as stock return volatility. We document a significant positive relation between Google search volume and future option trading activity of retail investors. Moreover, retail investors tend to take more bullish option positions following increased attention. Although option implied volatilities initially rise, the long run impact of Google search volume on option prices is negative, albeit small in magnitude. In contrast, we find a significantly positive and persistent relation between Google search volume and future realized stock return volatility. An option trading strategy of buying high attention delta-hedged straddles and selling low attention delta-hedged straddles generates an annual return of 6.44%, which is uncorrelated with standard risk factors. Our findings suggest that retail investor attention not only affects trading activity but also is a source of noise trader risk.

러프집합을 활용한 KOSPI200 옵션시장의 변동성 회귀 전략

본 논문에서는 옵션시장에서의 변동성 회귀특성과 러프집합 알고리즘을 이용한 옵션전략을 개발하는 것을 제안한다. 이제까지 주식, 선물 시장에서는 다양한 연구가 선행되어 왔지만, 옵션시장에 대한 연구는 활발하지 않았다. 특히 고빈도 자료를 이용한 옵션 트레이딩 전략은 미미한 수준이다. 본 연구의 목적은 두가지로 구성된다. 첫째는 내재변동성 고평가, 저평가 상태를 측정하여 괴리가 발생했을 때 이익을 향유하는 변동성 회귀 모델을 구축하는 것이다. 둘째는 옵션트레이딩전략에 러프집합 알고리즘을 사용하여 부정확한 진입신호를 필터링하여 더 안정적인 수익을 추구하는 것이다. 이 논문의 요점은 옵션시장이 기초자산, 변동성, 이자율과 같은 다양한 요소에 영향을 받기 때문에, 변동성을 제외한 요인 (기초자산의 방향성)을 선물로 헤지하면서, 변동성괴리에 따른 이익만을 향유하는 것이다. This study proposes a novel option strategy by using characteristic of volatility reversion and rough set algorithm in options market. Until now, various research has been conducted on stock and future markets, but minimal research has been done in options market. Particularly, research on the option trading strategy using high frequency data is limited. This study consists of two purposes. The first is to enjoy a profit using volatility reversion model when volatility gap is occurred. The second is to pursue a more stable profit by filtering inaccurate entry point through rough set algorithm. Since options market is affected by various elements like underlying assets, volatility and interest rate, the point of this study is to hedge elements except volatility and enjoy the profit following the volatility gap.

Using a Differential Evolutionary Algorithm to Test the Efficient Market Hypothesis

The proposition that a relatively new technology such as a differential evolutionary algorithm (DEA) can violate the weak form of the efficient market hypothesis is tested using daily data from the Australian share market from 2000 until 2008. An option trading strategy based on forecasts from a DEA is shown to perform better than a buy and hold strategy over parts of the sample space and, on average, over all of it. Speculators may make supernormal profits using new methodologies however such profits are unlikely to be sustained.

Option Trading Strategy in Nifty in NSE, India

Options are most often regarded as insurance against the price movements in the underlying stock or an index. It is a right to exercise the holders desire to buy (CALL) or sell (PUT). These options are priced based on the Holy Grail kind of formulae called ‘Black-Scholes’ model. This logic behind the formulae is based on current underlying asset price, intrinsic value, time for expiry, volatility, interest rates and dividend, if any, paid. As the expiry date nears, the option premium price tends to go down. However such fall happens only at the fag end of the option period. In between periods, one can watch an interesting fact about the option premium price. It tends to move along with the value of the underlying asset. But the spread between the value of underlying assets and the option premium is wide enough to make huge profit. Investors can benefit from this spread on the direction of the underlying. In this paper, we are going to discuss various strategies that can be used to gain this spread.

Effective options trading strategies based on volatility forecasting recruiting investor sentiment

This study investigates an algorithm for an effective option trading strategy based on superior volatility forecasts using actual option price data for the Taiwan stock market. The forecast evaluation supports the significant incremental explanatory power of investor sentiment in the fitting and forecasting of future volatility in relation to its adversarial multiple-factor model, especially the market turnover and volatility index which are referred to as the investors’ mood gauge and proxy for overreaction. After taking into consideration the margin-based transaction cost, the simulated trading indicates that a long or short straddle 15days before the options’ final settlement day based on the 60-day in-sample-period volatility forecasting recruiting market turnover achieves the best average monthly return of 15.84%. This study bridges the gap between option trading, market volatility, and the signal of the investors’ overreaction through the simulation of the option trading strategy. The trading algorithm based on the volatility forecasting recruiting investor sentiment could be further applied in electronic trading and other artificial intelligence decision support systems.

Option straddle trading: Financial performance and economic significance of direct profit forecast and conventional strategies

The present study focuses on the trading of at-the-money straddles using options on foreign currency futures, namely British Pound, Canadian Dollar, and Japanese Yen. The financial performance and economic significance of a direct profit forecast trading strategy are examined. This strategy uses a linear projection to directly forecast the profit (net of transaction costs) of engaging in a straddle. The straddle is purchased when the forecast is positive and sold when negative. This differs from the conventional option trading strategy of basing trading decisions on a two-step procedure of first generating a volatility forecast and then inputting the volatility forecast into an appropriate option pricing model to price the straddle. The direct profit forecast trading strategy removes volatility forecasting and option pricing models from the straddle trading decision process altogether. This method has only one source of model risk, compared to the conventional two step method which has two sources of model risk. It is possible that the direct forecast trading strategy with only one source of model risk may outperform the conventional method of trading straddles. The experimental investigation confirms this notion and the out-of-sample results indicate that, for each of the currencies analysed, the direct forecasting strategy is more profitable than the conventional two-step method. Furthermore, the results are robust with respect to different transaction cost assumptions. Finally, tests of economic significance indicate consistent market timing value for the direct forecast method.

Endogenous instability in financial markets

In a simple counter‐example it is shown how financial markets can be destabilized through the trading of derivative instruments. In particular, an assessment is made of the impact on the stock market of a widely used equity option trading strategy, and it is shown that instability may arise as a consequence of option trading. Furthermore, it is suggested that potential instability is endogenous because it arises from the use of available financial instruments in a manner which is not unusual or in other ways pathological.