Option pricing with the control variate technique beyond Monte Carlo simulation |
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| Affiliation: | 1. Department of Economics, University of Texas at San Antonio, Taiwan, R.O.C;2. Department of Banking and Finance, National Chi Nan University, Taiwan, R.O.C;1. Department of Industrial and System Engineering, Hosei University, Japan;2. School of Business, Aoyama Gakuin University, Japan;1. Department of Economics and Finance, College of Economics and Political Science, Sultan Qaboos University, Muscat, Oman;2. Institute of Business Research, University of Economics Ho Chi Minh City, Vietnam;3. Faculty of Business Administration, Bilkent University, Ankara 06800, Turkey;4. Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon;5. Institute of Business Research and CFVG, University of Economics Ho Chi Minh City, Vietnam;6. PNU Business School, Pusan National University, Busan, Republic of Korea;1. Department of Information and Financial Management and Institute of Finance, National Chiao-Tung University, Taiwan;2. Department of Finance, National Central University, Taiwan;3. Risk and Insurance Research Center, College of Commerce, National Chengchi University, Taiwan |
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| Abstract: | Although mostly used alongside Monte Carlo simulation, the control-variate (CV) technique can be applied to other numerical algorithms in option pricing. This paper studies the conditions under which a numerical method (simulation-based or not) can benefit from the CV technique and what approximators can serve as CVs. We demonstrate the ideas with Carr and Madan’s Fourier transform-based algorithm, convolution-based pricing algorithms, and classic binomial trees. Numerical results are provided to show that the CV-enhanced versions are more efficient than the original algorithms. |
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| Keywords: | Numerical algorithm Monte Carlo simulation Control variate Binomial tree Convolution |
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