Assessment of option price volatility


Financial derivatives are becoming increasingly popular on a daily basis. As markets become more unpredictable, companies and individual investors are increasingly using these tools to manage risk, leverage, and increase investment returns. The most important aspect of any contract is the contract price, as the financial result of the contract depends on the price. Also for an options. In each case, the option price depends on many factors that are difficult to define and predict in advance. The price sensitivity of the option allows you to determine where and on what the option price depends. Knowing this, the investor can manage the risk of the options. The purpose of the article is to assess the sensitivity of different options to market factors based on scientific literature and real market data. The study uses the Black-Scholes option pricing model, calculating and analyzing the value of Greek letters for the determination and valuation of transaction price sensitivity. The study showed that the most sensitive to changes in the underlying asset price, volatility and risk-free interest rate is the price of the currency option, and the price of the gold option is most sensitive over time (although in theory, gold retains its value in the long run). Knowing which components a particular option is sensitive to and capable of predicting changes in those components, you can predict changes in the option price and avoid additional risk.

Article in Lithuanian.

Pasirinkimo sandorių kainos jautrumo vertinimas


Dėl finansų inžinerijos atsiradusios išvestinės finansinės priemonės kasdien vis labiau populiarėja. Rinkoms tampant labiau nenuspėjamoms, įmonės ir individualūs investuotojai vis dažniau naudoja šias priemones rizikai ir įsipareigojimams valdyti, bei investicijų grąžai didinti. Sudarant bet kurį sandorį, svarbiausias aspektas – sandorio kaina, nes nuo jos priklauso finansinis sandorio rezultatas. Ne išimtis ir pasirinkimo sandoriai. Kiekvienu atveju pasirinkimo sandorio kaina priklauso nuo daugelio veiksnių, kuriuos sunku apibrėžti ir numatyti iš anksto. Pasirinkimo sandorio kainos jautrumo vertinimas leidžia nustatyti, nuo ko ir kaip priklauso pasirinkimo sandorio kaina. Tai žinodamas investuotojas gali valdyti pasirinkimo sandorių riziką. Straipsnio tikslas – remiantis mokslinės literatūros šaltiniais ir realiais rinkos duomenimis, įvertinti skirtingų pasirinkimo sandorių kainos jautrumą rinkos veiksniams. Tyrimui atlikti taikomas Black-Scholes pasirinkimo sandorių kainodaros modelis, sandorių kainos jautrumui nustatyti ir vertinti apskaičiuojamos ir analizuojamos graikiškųjų raidžių reikšmės. Atlikus tyrimą paaiškėjo, kad bazinio turto kainos pokyčiams, kintamumo ir nerizikingos palūkanų normos pokyčiams jautriausia yra valiutos pasirinkimo sandorio kaina, o laikui jautriausia yra aukso pasirinkimo sandorio kaina (nors teoriškai auksas ilgu laikotarpiu puikiai išsaugo savo vertę). Žinant, kurioms komponentėms tam tikras pasirinkimo sandoris yra jautrus ir sugebant prognozuoti tų komponenčių pokyčius, galima numatyti pasirinkimo sandorio kainos pokyčius ir išvengti papildomos rizikos.

Reikšminiai žodžiai: pasirinkimo sandoris, kainos jautrumas, graikiškosios raidės, Black-Scholes modelis, išvestinė finansinė priemonė.

Keyword : option contract, price sensitivity, Greek letters, Black-Scholes model, derivative

How to Cite
Sodaunykaitė, V., & Martinkutė-Kaulienė, R. (2020). Assessment of option price volatility. Mokslas – Lietuvos Ateitis / Science – Future of Lithuania, 12.
Published in Issue
Mar 31, 2020
Abstract Views
PDF Downloads
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


AAPL. (2018). Yahoo finance.

Aleknevičienė, V. (2005). Finansai ir kreditas. Enciklopedija.

Anton, S. G. (2016). Risk management with financial derivatives: Empirical evidence from Romanian Non-financial firms. Annals of Faculty of Economics, University of Oradea, 1(2), 336–342.

Bellalah, M. (2009). Derivatives, risk management and value. World Scientific Publishing.

Cassidy, D. T., Hamp, M. J., & Ouyed, R. (2013). Log Student’s t-distribution-based option sensitivities: Greeks for the Gosset formulae. Quantitive Finance, 8(13), 1289–1302.

Chen, H. Y., & Lee, C. F. (2008). Handbook of quantitative finance and risk management. Rutgers University, and Weikang Shih, Rutgers University.

Chisholm, A. M. (2010). Derivatives demystified: A step-by-step guide to forwards, futures, swaps and options (2nd ed.). John Willey & Sons.

Chui, M. (2016). Derivatives markets, products and participants: An overview. IFC Bulletin No 35.

Cocoa. (2018). Trading economics.

Coelho, F. R., & Reddy, Y. V. (2017). Applicability of BlackScholes and Black’s option pricing models in Indian derivatives market. IUP Journal of Financial Risk Management, 14(2), 61–69.

Dejanovski, A. (2014). The role and importance of the options as a unstandardized financial derivatives. TEM Journal, 3(1), 81–87.

Donohoe, M. P. (2015). Financial derivatives in corporate tax avoidance: A conceptual perspective. The Journal of the American Taxation Association, 37(1), 37–68.

Edeki, O. S., Ugbebor, O., & Owoloko, A. (2015). Analytical solutions of the Black–Scholes pricing model for European option valuation via a projected differential transformation method. Entropy, 2(17), 7510–7521.

EUR/USD Historical Data. (2018). Investing.

Gold. (2018). Trading economics.

Goodman, T., Neamtiu, M., & Zhang, F. (2018). Fundamental analysis and option returns. Journal of Accounting, Auditing & Finance, 33(1), 72–97.

Gregoriou, A. (2010). Modeling the non-linear behaviour of option price deviations from the Black Scholes model. Journal of Economic Studies, 1(37), 25–35.

Helfenstein, S. (2017). Back – to – Basics: A short primer on option pricing models and the DLOM. A Professional Development Journal for the Consulting Disciplines, 6.

Hull, J. (2015). Options, futures and other derivatives. Pearson.

Jarrow, R. (2014). Financial derivatives pricing. Selected works of Robert Jarrow.

Jiang, I. M., Lo, C., Karathanasopoulos, A., & Skindilas, K. (2017). A risk control tool for foreign financial activities – A new derivatives pricing model. Journal of Asset Management, 18(4), 269–294.

Juneja, S. (2013). Understanding the Greeks and their uses to measure risk. International Journal of Research in Commerce, IT and Management, 3(10), 2231–2256.

Juozapavičienė, A. (2013). Išvestiniai instrumentai tarptautinėse finansų rinkose (2-asis leid.). Technologija.

Kancerevyčius, G. (2009). Finansai ir investicijos. Smaltijos leidykla.

Kim, Y., Bae, H. O., & Koo, H. K. (2014). Option pricing and Greeks via a moving least square meshfree method. Quantitive Finance, 14(10), 1753–1764.

Kornel, T. (2014). The effect of derivative financial instruments on bank risks, relevance and faithful representation: Evidence from banks in hungary. Annals of the University of Oradea, Economic Science Series, 23(1), 698–706.

Lee, C. F., Chen, Y., & Lee, J. (2016). Alternative methods to derive option pricing models: Review and comparison. Quantitive Finance, 8(47), 417–451.

Lee, M. K., Yang, S. J., & Kim, J. H. (2017). Pricing vulnerable options with constant elasticity of variance versus stochastic elasticity of variance. Economic Computation and Economic Cybernetics Studies and Research, 51(1), 233–247.

Lesmana, D. C., & Wang, S. (2017). A numerical scheme for pricing american options with transaction costs under a jump diffusion process. Journal of Industrial and Management Optimization, 4(13), 1793–1813.

Martinkutė-Kaulienė, R. (2013). Sensitivity of option contacts. Verslas: Teorija ir Praktika, 14(2), 157–165.

Murauskaitė, L. (2011). Egzotinių opcionų vertinimo specifika (Magistro baigiamasis darbas).

Nandy, S., & Chattopadhyay, A. (2014). Impact of introducing different financial derivative instruments in India on its stock market volatility. Paradigm, 18(2), 135–153.

Nwozo, C. R., & Fadugba, S. E. (2014). On the accuracy of binomial model for the valuation of standard options with dividend yield in the context of Black-Scholes model. International Journal of Applied Mathematics, 44(1), 33–44.

Park, D., & Kim, J. (2015). Financial derivatives usage and monetary policy transmission: Evidence from Korean Firm-level data. Global Economic Review, 44(1), 101–115.

Paunovic, J. (2014). Options, Greeks, and risk management. Singidunum Journal of Applied Sciences, 11(1), 74–83.

Rajanikanth, C., & Reddy, E. L. (2015). Analysis of price using Black Scholes and Greek letters in derivative European option market. International Journal of Research in Management, Science & Technology, 3(1), 34–37.

Rutkauskas, A. V., & Stankevičius, P. (2006). Investicinių sprendimų valdymas. VPU leidykla.

Sahoo, A. (2016). Pattern of corporate hedging through financial derivatives in non-financial companies of India. Journal of Commerce & Management Thought, 7(3), 444–477.

Sinclair, E. (2010). Option trading: Pricing and volatility strategies and techniques. John Wiley & Sons.

Sinha, P., & Johar, A. (2010). Hedging greeks for a portfolio of options using linear and quadratic programming. The Journal of Prediction Markets, 4(1), 17–26.

Song, L., & Wang, W. (2013). Solution of the fractional BlackScholes option pricing model by finite difference method. Abstract and Applied Analysis, 2013, ID 194286, 10. Hindawi.

Sūdžius, V. (2011). Finansinių priemonių ir paslaugų rinkodara: principai ir praktika. Technika.

Svishchuk, A. V. (2013). Modeling and pricing of swaps for financial and energy markets with stochastic volatilities. World Scientific.

Valakevičius, E. (2008). Investavimas finansų rinkose. Technologija.

Walling, J., & Moore, C. (2010). Does Black-Scholes overvalue early stage company allocations. Business Valuation, 16(1), 16–31.

Xu, S., & Yang, Y. (2013). Fractional Black-Scholes model and technical analysis of stock price. Journal of Applied Mathematics, 2013, ID 631795.