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Project description
Portfolio optimization is a cornerstone of modern finance theory, as it is very attractive in the field of decision making under uncertainty.
Financial crises, economic imbalances, algorithmic trading and highly volatile movements of asset prices in the recent times have raised high
alarms on the management of financial risks. Inclusion of risk measures towards balancing optimal portfolios has become very crucial and
equally critical. Varied mathematical models have emerged leading towards practical risk-based asset allocation strategies.
Formally, financial portfolio optimization adheres to a formal approach in making investment decisions:
• For selection of investment portfolios containing the financial instruments
• To mitigate financial risks and ensure better preparedness for uncertainties
• To establish mathematical and computational methods on realistic constraints
• To provide stability across inter and intraday market fluctuations.
The major objective of this project is to study the most important portfolio optimization models used to mitigate financial risks.
Descriptions:
In section 1, give a brief Introduction of the project
In section 2, describe the different kinds of financial risks that are faced by investors and financial institutions.
In section 3 present the major risk measures used in portfolio optimization, (variance, mean-absolute deviation, Value at Risk, Conditional Value
at Risk, etc.) as well as the associated mathematical formulations of the optimization models.
In section 4, present the results of a practical implementation of the assigned model for the Greek (American) stock exchange. More specifically,
you wish to allocate an initial budget in the stocks of the FTSE/Large Cap index (25 stocks of the S&P500, explain how you select them, or 17
industry portfolios from Kenneth French web page (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html )) to construct an
optimal portfolio. Use monthly returns for the period between January 2000 and November 2015. Conduct both static tests (efficient frontiers) as
well as dynamic tests (backtesting experiments over the last 24 months). Describe the related Figures.
In section 5 give a brief conclusion.
References..
Deadline : Thusday, 12 January.
GROUPS
GROUPS
NAMES
MODEL
DATA
GROUP 1
Παπαχαραλάμπους
Παρασκευή
Σπρίνη Έλενα Μαρία
Στυλιανοπούλου
Κωνσταντίνα
CVAR vs MAD
S&P 500
GROUP 2
Τζιώτη Μαρία Αναστασία
GROUP 3
Νικολέτα Δήμητρα Χαίρα
Σάκκης Παναγιώτης
Ευάγγελος Τσάμης
CVAR
S&P 500
MAD
S&P 500
GROUP 4
Άντζα Γκρούμιτς
PUT CALL
S&P 500
GROUP 5
Γεώργιος Δόσης
NAMES
MODEL
DATA
NAMES
MODEL
DATA
Βασίλειος Σκορδάς
Νικολόπουλος
Ιωάννης-Ραφαήλ
CVAR vs MAD
LARGE CAP 25
GROUP 9
Νικόλαος Σαρδέλης
Γιάγκος Σαπουνάκης
GROUP 6
Κωνσταντίνος Αντωνίου
CVAR
LARGE CAP 25
GROUP 10
Γεωργια Αργυρακη
Χρυση Κλαδου
GROUP 7
Δράμης Παναγιώτης
MAD
LARGE CAP 25
GROUP 11
Τζέμης Αλέξιος
GROUP 8
Χριστίνα
Κουτροπούλου
PUT CALL
LARGE CAP 25
GROUP 12
Άννα Μαρία Κυριτσάκη
Αποστόλης Κυριακόπουλος
CVAR vs MAD
CVAR
MAD
TRACKING
17 INDUSTRY
PORTFOLIOS
17 INDUSTRY
PORTFOLIOS
17 INDUSTRY
PORTFOLIOS
17 INDUSTRY
PORTFOLIOS
GROUP 13
Panagiotis Mourikis
Alexandros
Ntelifilippidis
Konstantinos Isaias
PUT CALL vs
TRACKING
17 INDUSTRY
PORTFOLIOS