• Title/Summary/Keyword: binomial tree

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AN IMPROVED BINOMIAL METHOD FOR PRICING ASIAN OPTIONS

  • Moon, Kyoung-Sook;Kim, Hongjoong
    • Communications of the Korean Mathematical Society
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    • v.28 no.2
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    • pp.397-406
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    • 2013
  • We present an improved binomial method for pricing European- and American-type Asian options based on the arithmetic average of the prices of the underlying asset. At each node of the tree we propose a simple algorithm to choose the representative averages among all the effective averages. Then the backward valuation process and the interpolation are performed to compute the price of the option. The simulation results for European and American Asian options show that the proposed method gives much more accurate price than other recent lattice methods with less computational effort.

Tree-Structure-Aware Genetic Operators in Genetic Programming

  • Seo, Kisung;Pang, Chulhyuk
    • Journal of Electrical Engineering and Technology
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    • v.9 no.2
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    • pp.749-754
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    • 2014
  • In this paper, we suggest tree-structure-aware GP (Genetic Programming) operators that heed tree distributions in structure space and their possible structural difficulties. The main idea of the proposed GP operators is to place the generated offspring of crossover and/or mutation in a specified region of tree structure space insofar as possible by biasing the tree structures of the altered subtrees, taking into account the observation that most solutions are found in that region. To demonstrate the effectiveness of the proposed approach, experiments on the binomial-3 regression, multiplexor and even parity problems are performed. The results show that the results using the proposed tree-structure-aware operators are superior to the results of standard GP for all three test problems in both success rate and number of evaluations.

Genetic Operators Based on Tree Structure in Genetic Programming (유전 프로그래밍을 위한 트리 구조 기반의 진화연산자)

  • Seo, Ki-Sung;Pang, Cheul-Hyuk
    • Journal of Institute of Control, Robotics and Systems
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    • v.14 no.11
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    • pp.1110-1116
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    • 2008
  • In this paper, we suggest GP operators based on tree structure considering tree distributions in structure space and structural difficulties. The main idea of the proposed genetic operators is to place generated offspring into the specific region which nodes and depths are balanced and most of solutions exist. To enable that, the proposed operators are designed to utilize region information where parents belong and node/depth rates of selected subtree. To demonstrate the effectiveness of our proposed approach, experiments of binomial-3 regression, multiplexer and even parity problem are executed. The experiments results show that the proposed operators based on tree structure is superior to the results of standard GP for all three test problems in both success rate and number of evaluations.

Road O&M Cost Prediction Model with the Integration of the Impacts of Climate Change using Binomial Tree Model (기후변화 영향을 고려한 도로시설 유지관리 비용변동성 예측 이항분석모델)

  • Kim, Du Yon;Kim, Byungil
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.35 no.5
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    • pp.1165-1171
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    • 2015
  • Due to the increasing trend of operation and maintenance cost (O&M cost) of infrastructure, the accurate estimation of O&M cost is crucial part to the government. Recent literatures pointed out that gradual climate changes such as average temperature changes, average precipitation changes, and etc. have significant impact on infrastructure O&M cost. This research is intended to develop a long-term O&M cost prediction model of road facilities by considering the impacts of average temperature changes. For this end, the climate change scenarios of Intergovernmental Panel on Climate Change (IPCC)'s $5^{th}$ report are adopted to structure the impact of average temperature changes by using binomial lattice model. The proposed framework is expected to regional government in supporting decisions for road O&M cost.

Option Strategies: An Analysis of Naked Put Writing

  • Lekvin Brent J.;Tiwari Ashish
    • The Korean Journal of Financial Studies
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    • v.3 no.2
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    • pp.329-364
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    • 1996
  • Writing naked put options is a strategy employed either as a speculation to capture premium income, or as a method of placing a limit order to buy the underlying at the strike price in return for premium received. Using a Monte Carlo simulation, twenty thousand equity prices are generated under known volatility and return parameters. A binomial tree is constructed using the same volatility and return parameters. Put options on these 'equities' are valued with the binomial methodology. The performance of various put writing strategies is evaluated on a risk-adjusted basis. Evidence presented suggests that the judicious use of put options may enhance returns during portfolio construction.

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RELIABILITY ESTIMATION FOR A DIGITAL INSTRUMENT AND CONTROL SYSTEM

  • Yaguang, Yang;Russell, Sydnor
    • Nuclear Engineering and Technology
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    • v.44 no.4
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    • pp.405-414
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    • 2012
  • In this paper, we propose a reliability estimation method for DI&C systems. At the system level, a fault tree model is suggested and Boolean algebra is used to obtain the minimal cut sets. At the component level, an exponential distribution is used to model hardware failures, and Bayesian estimation is suggested to estimate the failure rate. Additionally, a binomial distribution is used to model software failures, and a recently developed software reliability estimation method is suggested to estimate the software failure rate. The overall system reliability is then estimated based on minimal cut sets, hardware failure rates and software failure rates.

Analysis of Neighborhood Environmental Factors Affecting Bicycle Accidents and Accidental Severity in Seoul, Korea (서울시 자전거 교통사고와 사고 심각도에 영향을 미치는 근린환경 요인 분석)

  • Hwang, Sun-Geun;Lee, Sugie
    • Journal of Korea Planning Association
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    • v.53 no.7
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    • pp.49-66
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    • 2018
  • The purpose of this study is to analyze neighborhood environmental factors affecting bicycle accidents and accidental severity in Seoul, Korea. The use of bicycles has increased rapidly as daily transportation means in recent years. As a result, bicycle accidents are also steadily increasing. Using Traffic Accident Analysis System (TAAS) data from 2015 to 2017, this study uses negative binomial regression analysis to identify neighborhood environmental factors affecting bicycle accidents and accidential severity. The main results are as follows. First, bicycle accidents are more likely to occur in commercial and mixed land use areas where pedestrians, bicycle and vehicles are moving together. Second, bicycle accidents are positively associated with road structures such as four-way intersection. In contrast, three-way intersection is negatively associated with serious bicycle accidents. The density of speed hump or street tree is negatively associated with bicycle accidents and accidential severity. This finding indicates the effect of speed limit or street trees on bicycle safety. Fourth, bicycle infrastructures are also important factors affecting bicycle accidents and accidential severity. Bicycle-exclusive roads or bicycle-pedestrian mixed roads are positively associated with bicycle accidents and accidential severity. Finally, this study suggests policy implications to improve bicycle safety.

Recirculating Shuffle-Exchange Interconnection ATM Switching Network Based on a Priority Control Algorithm (우선순위 제어기법을 기반으로 한 재순환 Shuffle-Exchage 상호연결 ATM 스위치)

  • Park, Byeong-Su
    • The Transactions of the Korea Information Processing Society
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    • v.7 no.6
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    • pp.1949-1955
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    • 2000
  • This paper proposes a multistage interconnection ATM switching network without internal blocking. The first is recirculating shuffle-exchange network improved on hardware complexity. The next is connected to Rank network with tree structure. In this network, after the packets transferred to the same output ports are given each priority, only a packet with highest priority is sent to the next, an the others are recirculated to the first. Rearrangeability through decomposition and composition algorithm is applied for the transferred packets in hanyan network and all they arrive at a final destinations. To analyze throughput, waiting time and packet loss ratio according tothe size of buffer, the probabilities are modeled by a binomial distribution of packet arrival.

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FPGA-Based Design of Black Scholes Financial Model for High Performance Trading

  • Choo, Chang;Malhotra, Lokesh;Munjal, Abhishek
    • Journal of information and communication convergence engineering
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    • v.11 no.3
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    • pp.190-198
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    • 2013
  • Recently, one of the most vital advancement in the field of finance is high-performance trading using field-programmable gate array (FPGA). The objective of this paper is to design high-performance Black Scholes option trading system on an FPGA. We implemented an efficient Black Scholes Call Option System IP on an FPGA. The IP may perform 180 million transactions per second after initial latency of 208 clock cycles. The implementation requires the 64-bit IEEE double-precision floatingpoint adder, multiplier, exponent, logarithm, division, and square root IPs. Our experimental results show that the design is highly efficient in terms of frequency and resource utilization, with the maximum frequency of 179 MHz on Altera Stratix V.

Option pricing and profitability: A comprehensive examination of machine learning, Black-Scholes, and Monte Carlo method

  • Sojin Kim;Jimin Kim;Jongwoo Song
    • Communications for Statistical Applications and Methods
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    • v.31 no.5
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    • pp.585-599
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    • 2024
  • Options pricing remains a critical aspect of finance, dominated by traditional models such as Black-Scholes and binomial tree. However, as market dynamics become more complex, numerical methods such as Monte Carlo simulation are accommodating uncertainty and offering promising alternatives. In this paper, we examine how effective different options pricing methods, from traditional models to machine learning algorithms, are at predicting KOSPI200 option prices and maximizing investment returns. Using a dataset of 2023, we compare the performance of models over different time frames and highlight the strengths and limitations of each model. In particular, we find that machine learning models are not as good at predicting prices as traditional models but are adept at identifying undervalued options and producing significant returns. Our findings challenge existing assumptions about the relationship between forecast accuracy and investment profitability and highlight the potential of advanced methods in exploring dynamic financial environments.