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RISS 인기검색어
- 검색키워드
- 저자 : Ian Sutherland (검색결과 6 건)
- 무료
- 기관 내 무료
- 유료
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Economic Impact and Revenue Analysis of the Olympic and Winter Olympic Games
Ian Sutherland,Gunhee Lee,Soeon Park 한국관광연구학회 2015 관광연구저널 Vol.29 No.4
Our research sheds light on two aspects of the Olympics. First, our research looks at the direct and indirect economic effects of the upcoming Pyeongchang Winter Olympic Games on the South Korean economy using input-output analysis through inter-industry relation tables. The input-output table used in our study is a reconstruction of a larger table using only industries that are impacted most by the hosting of the Olympic Games. As a result, we found a 2 million dollar increase in production and 0.56 million dollars in supply shortage resulting from hosting the Pyeongchang Olympic Games. Particularly, it is shown that the Olympic industries will grow the Electronics & Electrical Equipment and Real Estate & Business Services industries more than others based on their expected production inducement and supply shortages. The second half of our research uses regression and exploratory analysis of past Olympic and Winter Olympic direct revenue sources to determine characteristics that result in the successful or unsuccessful hosting of the Olympics. When adjusted for event size, two themes appear in classifying the successfulness of hosting the Olympics. The first is a resulting relative increase in licensing and sponsorship revenues by large countries. The second theme is relatively higher revenues from ticketing and broadcasting in countries where the first language is English. Combining these factors, a very rough estimation of $2.6 billion of direct revenues is forecast for the 2018 Winter Olympics in Pyeongchang, South Korea
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Higher Moments in Postmodern Portfolio Asset Allocation
Gunhee Lee,Ian Sutherland,Woohyung Lee 한국시뮬레이션학회 2017 한국시뮬레이션학회 학술대회집 Vol.2017 No.-
While modern portfolio theory (MPT) uses standard deviation as the measure of risk;PostModern Portfolio Theory (PMPT) develops the idea of risk further to only include that of downside-risk. Intuitively this makes sense;because investors are more worried about negative returns;and therefore;the deviation in negative returns is more important to investors. Since returns have been shown historically to not follow the normal distribution;with fatter tails and higher downside risk;the extension of the meanvariance model to incorporate mixed higher moments (i.e. coskewness and cokurtosis) in the allocation of assets has allowed investors to investigate downside risk of assets;particularly for assets that have a larger departure from normality. To evaluate negative risk;mixed higher moments (i.e. coskewness and cokurtosis) are used to optimize asset allocation. The optimization of asset allocation using higher moments is a complex problem which can be solved fairly easily through optimization software or algorithms. We use Quadratic Programming (QP) through R Optimization Infrastructure (ROI) to solve for the quadratic optimization of incorporating four moments into a asset allocation for a portfolio. Adding to the evidence of other studies;our results show that the optimization using higher moments results in drastically different weights for assets;particularly in a manner that minimizes risk. We compare the results between several optimization methods using lower and higher moments.
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Higher Moments in Postmodern Portfolio Asset Allocation
Gunhee Lee(이군희),Ian Sutherland(이안 서더렌드),Woohyung Lee(이우형) 대한산업공학회 2017 대한산업공학회 춘계학술대회논문집 Vol.2017 No.4
While modern portfolio theory (MPT) uses standard deviation as the measure of risk, Post-Modern Portfolio Theory (PMPT) develops the idea of risk further to only include that of downside-risk. Intuitively this makes sense, because investors are more worried about negative returns, and therefore, the deviation in negative returns is more important to investors. Since returns have been shown historically to not follow the normal distribution, with fatter tails and higher downside risk, the extension of the meanvariance model to incorporate mixed higher moments (i.e. coskewness and cokurtosis) in the allocation of assets has allowed investors to investigate downside risk of assets, particularly for assets that have a larger departure from normality. To evaluate negative risk, mixed higher moments (i.e. coskewness and cokurtosis) are used to optimize asset allocation. The optimization of asset allocation using higher moments is a complex problem which can be solved fairly easily through optimization software or algorithms. We use Quadratic Programming (QP) through R Optimization Infrastructure (ROI) to solve for the quadratic optimization of incorporating four moments into a asset allocation for a portfolio. Adding to the evidence of other studies, our results show that the optimization using higher moments results in drastically different weights for assets, particularly in a manner that minimizes risk. We compare the results between several optimization methods using lower and higher moments.
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Higher Moments in Postmodern Portfolio Asset Allocation
Gunhee Lee(이군희),Ian Sutherland(이안 서더렌드),Woohyung Lee(이우형) 한국경영과학회 2017 한국경영과학회 학술대회논문집 Vol.2017 No.4
While modern portfolio theory (MPT) uses standard deviation as the measure of risk, Post-Modern Portfolio Theory (PMPT) develops the idea of risk further to only include that of downside-risk. Intuitively this makes sense, because investors are more worried about negative returns, and therefore, the deviation in negative returns is more important to investors. Since returns have been shown historically to not follow the normal distribution, with fatter tails and higher downside risk, the extension of the meanvariance model to incorporate mixed higher moments (i.e. coskewness and cokurtosis) in the allocation of assets has allowed investors to investigate downside risk of assets, particularly for assets that have a larger departure from normality. To evaluate negative risk, mixed higher moments (i.e. coskewness and cokurtosis) are used to optimize asset allocation. The optimization of asset allocation using higher moments is a complex problem which can be solved fairly easily through optimization software or algorithms. We use Quadratic Programming (QP) through R Optimization Infrastructure (ROI) to solve for the quadratic optimization of incorporating four moments into a asset allocation for a portfolio. Adding to the evidence of other studies, our results show that the optimization using higher moments results in drastically different weights for assets, particularly in a manner that minimizes risk. We compare the results between several optimization methods using lower and higher moments.
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