Browsing by Author "Chiu, Chun-Yuan"

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Loading Prediction and Barring Controls for Machine Type Communication
(2013-01-01)
Chou, Chie Ming
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Huang, Ching Yao
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Chiu, Chun-Yuan
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
Machine Type Communication (MTC) is a very distinct technology that enables the "Internet of things". Overload that caused by a large number of MTC device is one of the primary issues for supporting MTC. Extended Access Barring (EAB) is a barring mechanism for long-term evolution (LTE) network to selectively control access attempts in order to prevent overload. In the paper, a method to predict access loading and adapt a barring ratio for EAB is designed. The simulation results show the proposal could relieve overload significantly without aggressively barring.
Pricing Asian option by the FFT with higher-order error convergence rate under Levy processes
(2015-02-01)
Chiu, Chun-Yuan
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Dai, Tian-Shyr
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Lyuu, Yuh-Dauh
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
Pricing Asian options is a long-standing hard problem; there is no analytical formula for the probability density of its payoff even when the process of the underlying asset follows the simple lognormal diffusion process. It is known that the density function of a discretely-sampled Asian option\'s payoff can be efficiently approximated by the Fast Fourier Transform (FFT). As a result, we can accurately price the option under more general Levy processes. This paper shows that the pricing error of this approach, called the FFT approach, can be decomposed into the truncation error, the integration error, and the interpolation error. We prove that previous algorithms that follow the FFT approach converge quadratically. To improve the error convergence rate, our proposed algorithms reduce the integration error by the higher-order Newton-Cotes formulas and new integration rules derived from the Lagrange interpolating polynomial. The interpolation error is reduced by the higher-order Newton divided-difference interpolation formula. Consequently, our algorithms can be sped up by the FFT to achieve the same time complexity as previous algorithms, but with a faster error convergence rate. Numerical results are given to verify the efficiency and the fast convergence of our algorithms. (c) 2014 Elsevier Inc. All rights reserved.
Pricing barrier stock options with discrete dividends by approximating analytical formulae
(2014)
Dai, Tian-Shyr
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Chiu, Chun-Yuan
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資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Deriving accurate analytical formulas for pricing stock options with discrete dividend payouts is a hard problem even for the simplest vanilla options. This is because the falls in the stock price process due to discrete dividend payouts will significantly increase the mathematical difficulty in pricing the option. On the other hand, much literature uses other dividend settings to simplify the difficulty, but these settings may produce inconsistent pricing results. This paper derives accurate approximating formulae for pricing a popular path-dependent option, the barrier stock option, with discrete dividend payouts. The fall in stock price due to dividend payout at an exdividend date t is approximated by an accumulated price decrement due to a continuous dividend yield up to time t. Thus, the stock price process prior to time t and after time t can be separately modelled by two different lognormal-diffusive stock processes which help us to easily derive analytical pricing formulae. Numerical experiments suggest that our formulae provide more accurate and coherent pricing results than other approximation formulae. Our formulae are also robust under extreme cases, like the high volatility (of the stock price) case. Besides, our formulae also extend the applicability of the first-passage model (a type of structural credit risk model) to measure how the firm\'s payout influences its financial status and the credit qualities of other outstanding debts.