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Decomposition of ETFs: Building a synthetic portfolio of ETFs major positions

Gadlijauskas, Donatas LU and Sarul, Evelina LU (2022) NEKN02 20221
Department of Economics
Abstract
This paper investigates the performance of benchmark indices and according ETFs against the synthetic portfolios that were built using the five major holdings of the selected benchmark index and its ETF. Not only do we test the synthetic portfolios, but from them, we make optimal (re-balanced) portfolios using mean-variance optimization (with short-selling constraints). We test and examine the returns and characteristics of constructed portfolios against the benchmark indices and their ETFs to come up with the best investment strategy. By comparing the historical returns, Sharpe ratios, and VaR we analyze the performance of each financial instrument to determine its pros and cons for the three different investor types: risk-averse,... (More)
This paper investigates the performance of benchmark indices and according ETFs against the synthetic portfolios that were built using the five major holdings of the selected benchmark index and its ETF. Not only do we test the synthetic portfolios, but from them, we make optimal (re-balanced) portfolios using mean-variance optimization (with short-selling constraints). We test and examine the returns and characteristics of constructed portfolios against the benchmark indices and their ETFs to come up with the best investment strategy. By comparing the historical returns, Sharpe ratios, and VaR we analyze the performance of each financial instrument to determine its pros and cons for the three different investor types: risk-averse, risk-neutral, and risk-loving. Moreover, we construct GARCH models for the analyzed portfolios to estimate their volatility and compare them against each other since the analyzed time period contains regular and high volatility returns.

We conclude that by constructing synthetic portfolios a retail investor is able to beat the returns of the benchmark index and its passive ETF while having slightly worse risk-to-return metrics. If an individual chooses to compare synthetic portfolio to an actively managed benchmark ETF, the results are quite similar and so the choice of investment strategy is not as straightforward. For re-balanced (optimal) portfolios, stock allocation is extreme and is only recommended for risk-loving investors. For the GARCH models, we captured that a synthetic portfolio usually has a better performance in terms of lower volatility over time. (Less)
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author
Gadlijauskas, Donatas LU and Sarul, Evelina LU
supervisor
organization
course
NEKN02 20221
year
type
H1 - Master's Degree (One Year)
subject
keywords
ETF, Portfolio optimization, Sharpe ratio, VaR, GARCH
language
English
id
9084027
date added to LUP
2022-10-10 09:34:06
date last changed
2022-10-10 09:34:06
@misc{9084027,
  abstract     = {{This paper investigates the performance of benchmark indices and according ETFs against the synthetic portfolios that were built using the five major holdings of the selected benchmark index and its ETF. Not only do we test the synthetic portfolios, but from them, we make optimal (re-balanced) portfolios using mean-variance optimization (with short-selling constraints). We test and examine the returns and characteristics of constructed portfolios against the benchmark indices and their ETFs to come up with the best investment strategy. By comparing the historical returns, Sharpe ratios, and VaR we analyze the performance of each financial instrument to determine its pros and cons for the three different investor types: risk-averse, risk-neutral, and risk-loving. Moreover, we construct GARCH models for the analyzed portfolios to estimate their volatility and compare them against each other since the analyzed time period contains regular and high volatility returns. 

We conclude that by constructing synthetic portfolios a retail investor is able to beat the returns of the benchmark index and its passive ETF while having slightly worse risk-to-return metrics. If an individual chooses to compare synthetic portfolio to an actively managed benchmark ETF, the results are quite similar and so the choice of investment strategy is not as straightforward. For re-balanced (optimal) portfolios, stock allocation is extreme and is only recommended for risk-loving investors. For the GARCH models, we captured that a synthetic portfolio usually has a better performance in terms of lower volatility over time.}},
  author       = {{Gadlijauskas, Donatas and Sarul, Evelina}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Decomposition of ETFs: Building a synthetic portfolio of ETFs major positions}},
  year         = {{2022}},
}