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SUMMARY:Simulation of financial asset returns for strategic asset allocati
 on - Dr Joo Hee Lee\, Invesco
DTSTART:20180213T130000Z
DTEND:20180213T140000Z
UID:TALK100762@talks.cam.ac.uk
CONTACT:Dr Vivien Gruar
DESCRIPTION:The simulation of stochastic differential equations for Geomet
 ric Brownian Motion by Monte Carlo methods is a well-established technique
  in pricing options and many other derivatives (see e.g. Paul Glasserman\,
  2004\, Monte Carlo Methods in Financial Engineering (Springer)). This pro
 ject is concerned with extending this framework to the field of strategic 
 asset allocation (i.e. long-term investment decisions with typically more 
 than one financial assets in the portfolio).\n\nThe aim of this project is
  to design a financial-returns simulator for several correlated assets\, w
 hich is to generate a substantial number (>10\,000) of statistically equiv
 alent returns series in a multi-variate fashion. Using the setup\, we aim 
 to create a daily returns database that will exhibit properties obtained f
 rom the corresponding historical time-series data at different frequencies
 \, e.g. monthly or yearly.\n\nIn the investment management industry\, it i
 s often observed that financial returns are expressed interchangeably in p
 rice ratios\, i.e. simple returns\, and in log price differences\, i.e. co
 mpounded returns\, between two evaluation points in time\, i.e. investment
  horizons. While they are close enough to each other when the investment h
 orizon is short\, say 1 day\, the differences grow significantly with long
 er horizons. In addition\, these two measures have different properties in
  aggregation through time and across assets according to their mathematica
 l properties (A. Meucci\, Apr 2010\, GARP Risk Professional\, pp. 49-51). 
 As the simulated data will be compounded returns through discretization of
  a stochastic differential equation but will be aggregated across differen
 t assets\, we will adopt the solution Meucci (2010) put forward while maki
 ng sure that the statistical properties between historical data and simula
 ted data remain comparable at different frequency of data.\n\nIt is expect
 ed that the student to have some background knowledge on option pricing an
 d numerical analysis\, and programming skills in desirably R.\n\nThe proje
 ct student will then learn more than the basic aspects of: \n# strategic a
 sset allocation\; \n# financial modelling\; \n# connecting statistical and
  empirical data through mathematics. \n\nProgress permitting\, an opportun
 ity to apply the outcome to a real-world case could also be possible.\n\nT
 his project is to be carried out in Frankfurt as part of a large quantitat
 ive strategies team at Invesco consisting of more than 10 PhDs\, including
  the supervisor being a Cambridge alumna.\n\n
LOCATION:MR3 Centre for Mathematical Sciences
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