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SUMMARY:Bias reduction in covariance matrices and its effects on high-dime
nsional portfolios.
DTSTART;VALUE=DATE-TIME:20210702T150300Z
DTEND;VALUE=DATE-TIME:20210702T150400Z
DTSTAMP;VALUE=DATE-TIME:20240806T140650Z
UID:indico-contribution-207@indico.fis.agh.edu.pl
DESCRIPTION:Speakers: Benito Rodriguez Camejo (CIMAT Monterrey)\nThe curre
nt world keeps evolving\, and in our era the challenge of "too much data"
keeps popping up\, in particular in the world of portfolio optimization\,
therein lies the opportunity of providing more accurate results through th
e use of the relatively new tools developed in the random matrix theory li
terature to reduce the bias in the sample covariance matrix of some financ
ial data (real or simulated). In this poster we will present a couple of t
hese tools and a brief summary of the results that can be achieved with th
em.\n\nIn this work we will apply the clipping\, Tracy-Widom\, linear shri
nkage and non linear shrinkage techniques as our bias reduction mechanisms
\, and then to compare their efficacy we will compare them by using the re
sulting estimators of the underlying covariance matrix to optimize our fin
ancial portfolios.\n\nThe financial portfolio model we use is the classic
Markowitz model with fixed returns of one for all our assets and we use tw
o variants\, the first one with the only restriction that we must assign a
ll of our capital into our selected assets\, and the second one with the s
ame restriction plus a second one in which we do not consider short sellin
g or more specifically that we can only buy assets. \n\nThe data that we u
se also consists of synthetic and real data\, the synthetic data consists
of two types the first one is structured gaussian and the second one is fr
om a simulated GARCH time series \, while the real data consists of two po
rtfolios one considering only traditional stocks and the other with a mix
of stocks and cryptocurrencies.\n\nWe hope to determine the efficacy of th
e bias reduction techniques to optimize financial data under multiple scen
arios.\n\nhttps://indico.fis.agh.edu.pl/event/69/contributions/207/
LOCATION:ONLINE
URL:https://indico.fis.agh.edu.pl/event/69/contributions/207/
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