Choosing the right options trading strategy: Risk-return trade-off and performance in different market conditions
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Received February 9, 2022;Accepted March 16, 2022;Published April 14, 2022
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Author(s)Link to ORCID Index: https://orcid.org/0000-0003-2253-6108Link to ORCID Index: https://orcid.org/0000-0002-1346-7641Link to ORCID Index: https://orcid.org/0000-0002-5798-6363Link to ORCID Index: https://orcid.org/0000-0001-9024-0435Link to ORCID Index: https://orcid.org/0000-0001-6759-7612
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DOIhttp://dx.doi.org/10.21511/imfi.19(2).2022.04
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Article InfoVolume 19 2022, Issue #2, pp. 37-50
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Cited by4 articlesJournal title: IIMBG Journal of Sustainable Business and InnovationArticle title: Non-monotonicity of risk-expected return trade-off in pricing of equity securities: the case of premium board of Nigerian Stock ExchangeDOI: 10.1108/IJSBI-02-2023-0003Volume: 2 / Issue: 1 / First page: 43 / Year: 2024Contributors: Peter Ngozi AmahJournal title: Business: Theory and PracticeArticle title: PRIORITIES OF IMPACT INVESTING IN ENVIRONMENTAL PROTECTION PROJECTS: THE CASE OF THE FUTURE POST-WAR RECONSTRUCTION OF UKRAINEDOI: 10.3846/btp.2023.18020Volume: 24 / Issue: 2 / First page: 459 / Year: 2023Contributors: Oleksandra Rieznyk, Alla Treus, Serhiy KozmenkoJournal title: Cogent Economics & FinanceArticle title: Are Options Trading Strategies Really Effective for Hedging in the Indian Derivatives Market?DOI: 10.1080/23322039.2022.2111783Volume: 10 / Issue: 1 / First page: / Year: 2022Contributors: Shivaprasad S P, Geetha E, Raghavendra Acharya, Vidya Bai G, Rajeev MathaJournal title: Journal of the Turkish-German Gynecological AssociationArticle title: Assessment of the ovarian reserve in patients with beta-thalassemia major: a prospective longitudinal studyDOI: 10.4274/jtgga.galenos.2023.2022-12-2Volume: 24 / Issue: 3 / First page: 159 / Year: 2023Contributors: Aykut Özcan, Varol Gülseren, Esin Özcan, Emrah Toz, Volkan Turan
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The investment decisions are subjected to risk and return of the financial asset. Options strategies help employ a suitable strategy to balance the risk-return trade-off. The study analyzes the risk-return trade-off of the long straddle, long strangle, long call butterfly (LCB), short straddle, short strangle, and short call butterfly (SCB) strategies. Moreover, it measures the impact of strategy risk and options premiums on strategy return using panel data analysis. Additionally, the study evaluates the performance of options strategies using the excess returns to risk approach under neutral and volatile market conditions. This paper considered companies of top-six sector indices of the National Stock Exchange from 2009 to 2020 and collected data of 18,720 option contracts and 3,744 observations for each strategy (22,464 observations). The study revealed that risks of long straddle and long strangle strategies have a positive impact, and options premiums negatively influence their payoff. ATM call premiums positively affect LCB payoff, while OTM and ITM call premiums positively influence SCB payoff. However, the risks of butterfly strategy did not influence its payoff. The risk of short straddle and short strangle strategies negatively influenced the payoff and were considered riskier strategies. Moreover, short straddle and short strangle strategies enhanced excess returns under both market conditions. The results would help the investors in choosing the appropriate strategy by analyzing the impact of risk on the payoff and the ability to enhance excess returns to the risk of various options strategies to incorporate in their investment.
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JEL Classification (Paper profile tab)C33, G11, G32
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References59
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Tables9
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Figures0
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- Table 1. Descriptive statistics of option strategies payoffs
- Table 2. Augmented Dickey-Fuller test results
- Table 3. Panel regression analysis for LCB and SCB strategies
- Table 4. Panel regression analysis for long straddle and short straddle strategies
- Table 5. Panel regression analysis for long strangle and short strangle strategies
- Table 6. Excess returns to standard deviation in neutral market condition
- Table 7. Excess returns to standard deviation in volatile market condition
- Table 8. Excess returns to beta in neutral market condition
- Table 9. Excess returns to beta in volatile market condition
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- Aguilera, S. C., & López-Pascual, J. (2013). Analysing hedge fund strategies through the use of an option based approach. Spanish Journal of Finance and Accounting – Revista Española de Financiación Y Contabilidad, 42(158), 167-186.
- Ahn, H., Muni, A., & Swindle, G. (1999). Optimal hedging strategies for misspecified asset price models. Applied Mathematical Finance, 6(3), 197-208.
- Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., & de Gracia, F. P. (2020). Oil and asset classes implied volatilities: Investment strategies and hedging effectiveness. Energy Economics, 91, 104762.
- Bajo, E., Barbi, M., & Romagnoli, S. (2015). A generalized approach to optimal hedging with option contracts. The European Journal of Finance, 21(9), 714-733.
- Bangur, P. (2020). Strangle to resuscitate: evidence from India. The Journal of Investment Strategies, 9(2), 1-14.
- Bartonova, M. (2012). Hedging of Sales by Zero-cost Collar and its Financial Impact. Journal of Competitiveness, 4(2), 111-127.
- Basson, L., van den Berg, L., van Vuuren, G., & McMillan, D. (rev.ed). (2018). Performance of two zero-cost derivative strategies under different market conditions. Cogent Economics & Finance, 6(1), 1492893.
- Bauer, R., Cosemans, M., & Eichholtz, P. (2009). Option trading and individual investor performance. Journal of Banking & Finance, 33(4), 731-746.
- Bhat, A. (2021). The Profitability of Volatility Trading on Exchange-traded Dollar-rupee Options: Evidence of a Volatility Risk Premium? Global Business Review.
- Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637-654.
- Breusch, T. S., & Pagan, A. R. (1980). The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics. The Review of Economic Studies, 47(1), 239-253.
- Broadie, M., Johannes, M. S., & Chernov, M. (2007). Understanding Index Option Returns (AFA 2008 New Orleans Meetings Paper).
- Chang, C.-C., Hsieh, P.-F., & Wang, Y.-H. (2010). Information content of options trading volume for future volatility: Evidence from the Taiwan options market. Journal of Banking & Finance, 34(1), 174-183.
- Chaput, J. S., & Ederington, L. H. (2003). Option Spread and Combination Trading. The Journal of Derivatives, 10(4), 70-88.
- Chen, A.-S., & Leung, M. T. (2003). Option straddle trading: Financial performance and economic significance of direct profit forecast and conventional strategies. Applied Economics Letters, 10(8), 493-498.
- Chong, J. (2004). Options trading profits from correlation forecasts. Applied Financial Economics, 14(15), 1075-1085.
- Coval, J. D., & Shumway, T. (2001). Expected Option Returns. The Journal of Finance, 56(3), 983-1009.
- Diaz, M., & Kwon, R. H. (2017). Optimization of covered call strategies. Optimization Letters, 11(7), 1303-1317.
- Dickey, D. A., & Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series With a Unit Root. Journal of the American Statistical Association, 74(366), 427-431.
- Dickey, D. A., & Fuller, W. A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica, 49(4), 1057-1072.
- Dixit, A., Vipul, & Singh, S. (2019). Options pricing and short-selling in the underlying: Evidence from India. Journal of Futures Markets, 39(10), 1250-1268.
- Elices, A., & Giménez, E. (2013). Applying hedging strategies to estimate model risk and provision calculation. Quantitative Finance, 13(7), 1015-1028.
- Fernandes, G., Gomes, L., Vasconcelos, G., & Brandão, L. (2016). Mitigating wind exposure with zero-cost collar insurance. Renewable Energy, 99, 336-346.
- Fontanills, G. (2005). The options course: high profit & low stress trading methods. Wiley.
- Fullwood, J., James, J., & Marsh, I. W. (2021). Volatility and the cross-section of returns on FX options. Journal of Financial Economics, 141(3), 1262-1284.
- Gao, C., Xing, Y., & Zhang, X. (2018). Anticipating Uncertainty: Straddles around Earnings Announcements. Journal of Financial and Quantitative Analysis, 53(6), 2587-2617.
- Goltz, F., & Lai, W. N. (2009). Empirical Properties of Straddle Returns. The Journal of Derivatives, 17(1), 38-48.
- Gordiaková, Z., & Lalić, M. (2014). Long Strangle Strategy Using Barrier Options and its Application in Hedging Against a Price Increase. Procedia Economics and Finance, 15, 1438-1446.
- Guo, B., Shi, Y., & Xu, Y. (2020). Volatility information difference between CDS, options, and the cross section of options returns. Quantitative Finance, 20(12), 2025-2036.
- Guo, D. (2000). Dynamic volatility trading strategies in the currency option market. Review of Derivatives Research, 4, 133-154.
- Harvey, C. R., & Whaley, R. E. (1992). Market volatility prediction and the efficiency of the S & P 100 index option market. Journal of Financial Economics, 31(1), 43-73.
- Hoffmann, A. O. I., & Fischer, E. T. S. (2012). Behavioral Aspects of Covered Call Writing: An Empirical Investigation. Journal of Behavioral Finance, 13(1), 66-79.
- Hong, H., Sung, H.-C., & Yang, J. (2018). On profitability of volatility trading on S&P 500 equity index options: The role of trading frictions. International Review of Economics & Finance, 55, 295-307.
- Israelov, R., & Klein, M. (2016). Risk and Return of Equity Index Collar Strategies. The Journal of Alternative Investments, 19(1), 41-54.
- Kavussanos, M. G., & Visvikis, I. D. (2008). Hedging effectiveness of the Athens stock index futures contracts. The European Journal of Finance, 14(3), 243-270.
- Kedžo, M. G., & Šego, B. (2021). The relative efficiency of option hedging strategies using the third-order stochastic dominance. Computational Management Science, 18, 477-504.
- Kownatzki, C., Putnam, B., & Yu, A. (2021). Case study of event risk management with options strangles and straddles. Review of Financial Economics.
- Kumar Meher, B., Thonse Hawaldar, I., Spulbar, C., & Birau, R. (2021). Forecasting stock market prices using mixed ARIMA model: a case study of Indian pharmaceutical companies. Investment Management and Financial Innovations, 18(1), 42-54.
- Larikka, M., & Kanniainen, J. (2012). Calibration strategies of stochastic volatility models for option pricing. Applied Financial Economics, 22(23), 1979-1992.
- Leggio, K. B., & Lien, D. (2002). Covered Call Investing in a Loss Aversion Framework. Journal of Psychology and Financial Markets, 3(3), 182-191.
- Madan, D. B., & Sharaiha, Y. M. (2015). Option overlay strategies. Quantitative Finance, 15(7), 1175-1190.
- Maris, K., Nikolopoulos, K., Giannelos, K., & Assimakopoulos, V. (2007). Options trading driven by volatility directional accuracy. Applied Economics, 39(2), 253-260.
- Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91.
- Mercurio, F., & Vorst, T. C. F. (1996). Option pricing with hedging at fixed trading dates. Applied Mathematical Finance, 3(2), 135-158.
- Mugwagwa, T., Ramiah, V., Naughton, T., & Moosa, I. (2012). The efficiency of the buy-write strategy: Evidence from Australia. Journal of International Financial Markets, Institutions and Money, 22(2), 305-328.
- Niblock, S. J., & Sinnewe, E. (2018). Are covered calls the right option for Australian investors? Studies in Economics and Finance, 35(2), 222-243.
- NSE India. (2013). NSE – National Stock Exchange of India Ltd.
- Pesaran, M. H. (2004). General Diagnostic Tests for Cross Section Dependence in Panels. SSRN Electronic Journal.
- Qiu, S. (2020). American Strangle Options. Applied Mathematical Finance, 27(3), 228-263.
- Rusnáková, M., Šoltés, V., & Szabo, Z. K. (2015). Short Combo Strategy Using Barrier Options and its Application in Hedging. Procedia Economics and Finance, 32, 166-179.
- Samuel, Y. M. Z.-T. (2018). Option implied beta and option return. Applied Economics, 50(2), 128-142.
- Scholes, M. S. (1996). Global financial markets, derivative securities, and systemic risks. Journal of Risk and Uncertainty, 12(2-3), 271-286.
- Sharpe, W. F. (1963). A Simplified Model for Portfolio Analysis. Management Science, 9(2), 277-293.
- Sheu, H.-J., & Wei, Y.-C. (2011). Effective options trading strategies based on volatility forecasting recruiting investor sentiment. Expert Systems with Applications, 38(1), 585-596.
- Timková, M., & Šoltés, M. (2019). Managing the equity risk using Short Put Ladder strategy by barrier options. Investment Management and Financial Innovations, 16(4), 133-145.
- Treynor, J. L. (1961). Market Value, Time, and Risk. SSRN Electronic Journal.
- Treynor, J. L. (1962). Toward a Theory of Market Value of Risky Assets. SSRN Electronic Journal.
- Wang, C. S. H., Chen, Y.-C., & Lo, H.-Y. (2021). A fresh look at the risk-return trade-off. Pacific-Basin Finance Journal, 68, 101546.
- Whaley, R. E. (2002). Return and Risk of CBOE Buy Write Monthly Index. The Journal of Derivatives, 10(2), 35-42.
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Conceptualization
Shivaprasad S. P., Geetha E., Raghavendra, Kishore L., Rajeev Matha
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Data curation
Shivaprasad S. P., Kishore L., Rajeev Matha
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Formal Analysis
Shivaprasad S. P., Geetha E., Raghavendra, Kishore L., Rajeev Matha
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Investigation
Shivaprasad S. P., Geetha E., Raghavendra, Kishore L., Rajeev Matha
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Methodology
Shivaprasad S. P., Raghavendra, Kishore L., Rajeev Matha
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Resources
Shivaprasad S. P., Geetha E., Rajeev Matha
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Software
Shivaprasad S. P., Geetha E.
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Validation
Shivaprasad S. P., Geetha E., Raghavendra, Kishore L.
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Writing – original draft
Shivaprasad S. P., Geetha E., Raghavendra, Kishore L., Rajeev Matha
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Writing – review & editing
Shivaprasad S. P., Geetha E., Raghavendra, Kishore L., Rajeev Matha
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Funding acquisition
Geetha E., Kishore L.
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Project administration
Geetha E., Raghavendra
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Supervision
Raghavendra, Kishore L.
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Conceptualization
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Economic freedom and democracy: determinant factors in increasing macroeconomic stability
Yuri Yevdokimov , Leonid Melnyk , Oleksii Lyulyov , Olga Panchenko , Victoria Kubatko doi: http://dx.doi.org/10.21511/ppm.16(2).2018.26Problems and Perspectives in Management Volume 16, 2018 Issue #2 pp. 279-290 Views: 1683 Downloads: 289 TO CITE АНОТАЦІЯThe main goal of the article is to analyze the role and influence of economic freedom on macroeconomic stability. For this purpose, the authors used the integrated index of economic freedom, calculated by the Heritage Foundation and Democracy Index. It is noted that this index indicator was calculated by the experts from the World Bank using the index of voice and accountability. In the paper, the authors used the multinational panel dataset for 11 countries of the EU for the purpose of checking the correlation between economic freedom, democracy and macroeconomic stability. It should be highlighted that the abovementioned 11 countries are related by the fluctuation of economic growth during the transformation process (1996–2016) from communist party to the democracy and political pluralism. In addition, the authors proposed to add the indicators of political stability and trade openness, which allowed to take into account implementation of flexible macroeconomic instruments, including monetary policy, which towards increasing the economic growth, employment and financial development of the countries. The findings are directed received using the regression equation with fixed and random effects showed the high level of correspondence of the model used with the original observations. Despite the chosen approach to estimate the macroeconomic stability, the findings showed that there is a positive and statistically significant impact of economic freedom and democracy on macroeconomic stability.
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Natural disasters, information/communication technologies, foreign direct investment and economic growth in developed countries
Nadia Benali , Rochdi Feki doi: http://dx.doi.org/10.21511/ee.09(2).2018.06Environmental Economics Volume 9, 2018 Issue #2 pp. 80-87 Views: 1576 Downloads: 165 TO CITE АНОТАЦІЯThis paper investigates the causal relationship between natural disasters (DMS), information and communication technologies (ICT), foreign direct investment (FDI) and economic growth (GDP per capita) for 10 developed countries over the period 1990 to 2016. Panel DOLS and FMOLS results show that there is a positive relationship running from ICT to natural disasters and to foreign direct investment. In addition, ICT have a positive effect on GDP per capita. VECM Granger causality analysis results reveal a unidirectional causality in the short and long term from ICT to natural disaster and to FDI at the 5% and 10% levels. Therefore, one may note that there is a unidirectional relationship running from natural disaster to GDP and a bidirectional relationship between FDI and GDP.
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The relationship between the Nasdaq Composite Index and energy futures markets
Investment Management and Financial Innovations Volume 15, 2018 Issue #4 pp. 1-16 Views: 1218 Downloads: 148 TO CITE АНОТАЦІЯThis paper sheds light on the relationship between the Nasdaq Composite Index and a newly proposed Energy Futures Conditions Index (EFCI). While various financial conditions indices provide information about the financial stability of a country, the existence of an energy condition index, using futures markets, is scarce. Using weekly data over the period 1992–2017, this paper introduces an energy futures index using principal component analysis and test its predictability over the Nasdaq Composite Index. The EFCI captures 95% of the variability inherent in crude oil, heating oil and natural gas futures’ total reportable positions. Stability in forecast errors over different lags suggests a one week lag is sufficient to forecast weekly Nasdaq Composite Index. 95% prediction levels support that the estimated model captures actual equity market index values, except for the 2000 technology bubble. Distributions of level data were non-normal, not serially correlated and homoscedastic under the whole sample period, with diagnostics on pre and post technology bubble crisis showing mixed results. While differencing ensured homoscedastic errors in the forecasting model, Granger causality supported non-causality from both energy futures and equity markets, suggesting no evidence of cross market information flows.