Probabilistic Learning and Optimization Applied to Quantitative Finance
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @PhdThesis{Chinthalapati:thesis,
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author = "Venkata Lakshmipathi Raju Chinthalapati",
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title = "Probabilistic Learning and Optimization Applied to
Quantitative Finance",
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school = "Dept. of Mathematics, London School of Economics and
Political Science",
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year = "2011",
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address = "UK",
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month = sep,
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keywords = "genetic algorithms, genetic programming,
computational, information-theoretic learning with
statistics, learning/statistics and optimisation,
theory and algorithms, Markov processes",
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bibsource = "OAI-PMH server at eprints.pascal-network.org",
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oai = "oai:eprints.pascal-network.org:8629",
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URL = "http://www.lse.ac.uk/Mathematics/Research-Students/PhD-Roll-of-Honour",
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URL = "https://www.genealogy.math.ndsu.nodak.edu/id.php?id=192002",
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URL = "https://librarysearch.lse.ac.uk/permalink/f/1jad15a/44LSE_ALMA_DS21136217410002021",
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broken = "http://eprints.pascal-network.org/archive/00008629/",
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abstract = "his thesis concerns probabilistic learning theory and
stochastic optimisation and investigates applications
to a variety of problems arising in finance. In many
sequential decision tasks, the consequences of an
action emerge at a multitude of times after the action
is taken. A key problem is to find good strategies for
selecting actions based on both their short and long
term consequences. We develop a simulation-based,
two-timescale actor-critic algorithm for infinite
horizon Markov decision processes with finite state and
action spaces, with a discounted reward criterion. The
algorithm is of the gradient descent type, searching
the space of stationary randomised policies and using
certain simultaneous deterministic perturbation
stochastic approximation (SDPSA) gradient estimates for
enhanced performance. We apply our algorithm to a
mortgage refinancing problem and find that it obtains
the optimal refinancing strategies in a computationally
efficient manner. The problem of identifying pairs of
similar time series is an important one with several
applications in finance, especially to directional
trading, where traders try to spot arbitrage
opportunities. We use a variant of the Optimal Thermal
Causal Path method (obtained by adding a curvature term
and by using an approximation technique to increase the
efficiency) to determine the lead-lag structure between
a given pair of time-series. We apply the method to
various market sectors of NYSE data and extract highly
correlated pairs of time series. Because Genetic
Programming (GP) is known for its ability to detect
patterns such as the conditional mean and conditional
variance of a time series, it is potentially
well-suited to volatility forecasting. We introduce a
technique for forecasting 5-day annualised volatility
in exchange rates. The technique employs a series of
standard methods (such as MA, EWMA, GARCH and its
variants) alongside Genetic Programming forecasting
methods, dynamically opting for the most appropriate
technique at a given time, determined through
out-of-sample tests. A particular challenge with
volatility forecasting using GP is that, during
learning, the GP is presented with training data
generated by a noisy Markovian process, not something
that is modelled in the standard probabilistic learning
frameworks. We analyse, in a probabilistic model of
learning, how much such training data should be
presented to the GP in the learning phase for the
learning to be successful.",
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notes = "Supervisor: Prof Martin Anthony",
- }
Genetic Programming entries for
V L Raju Chinthalapati
Citations