Probabilistic Learning and Optimization Applied to Quantitative Finance

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Chinthalapati:thesis,
  author =       "Venkata Lakshmipathi Raju Chinthalapati",
  title =        "Probabilistic Learning and Optimization Applied to
                 Quantitative Finance",
  school =       "Dept. of Mathematics, London School of Economics and
                 Political Science",
  year =         "2011",
  address =      "UK",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming,
                 computational, information-theoretic learning with
                 statistics, learning/statistics and optimisation,
                 theory and algorithms",
  URL =          "https://catalogue.lse.ac.uk/Record/1337837",
  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.",
  notes =        "Supervisor: Prof Martin Anthony",
  bibsource =    "OAI-PMH server at eprints.pascal-network.org",
  oai =          "oai:eprints.pascal-network.org:8629",
  URL =          "http://eprints.pascal-network.org/archive/00008629/",
}

Genetic Programming entries for V L Raju Chinthalapati

Citations