BUSN1-UC MISC Risk Management - New York University: DMA Durham AM HW Write-Up _ Decision Models & Analytics
BUSN1-UC MISC Risk Management - New York University: DMA Durham AM HW Write-Up _ Decision Models & Analytics BUSN1-UC MISC Risk Management - New York University: DMA Durham AM HW Write-Up _ Decision Models & Analytics DMA Durham AM HW Write-Up 1. Use the information in the file to create a history o f 24 monthly returns for the 15 companies. Compute the historical average return of each stock. In particular, what was the historical return of ALCOA from 12/29/89 to 1/31/90? What was the historical return of Boeing from 5/31/90 to 6/29/90? Explain how you account for dividends and stock splits in computing monthly returns. (Note: This has already been done for you in . If you are interested, take a look and see how “if” statements were used to deal with the stock splits.) 2. Develop 24 future scenario returns for each of the 15 stocks using equation (i). What is the explanation underlying it? In particular, what is the return of ALCOA if scenario 1 occurs? What is the return of Reynolds Metals if scenario 3 occurs? 3. Compute and graph the mean-standard deviation efficient frontier, with no shorting allowed. Compute at least six points on the efficient frontier (including the minimum standard deviation and maximum expected return points). Create a table of results showing the following for each of your points on the efficient frontier: (1) the optimal portfolio weights, (2) mean portfolio return, and (3) standard deviation. (Briefly explain the equations and optimization model used in the spreadsheet.)
Escuela, estudio y materia
- Institución
- BUSN1-UC MISC Risk Management - New York Universit
- Grado
- BUSN1-UC MISC Risk Management - New York Universit
Información del documento
- Subido en
- 9 de abril de 2023
- Número de páginas
- 2
- Escrito en
- 2022/2023
- Tipo
- Examen
- Contiene
- Preguntas y respuestas
Temas
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busn1 uc misc risk management new york university dma durham am hw write up decision models amp analytics