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Speaker: Max Reppen
Title: An optimal dividend problem with stochastic cash flows
Abstract: We study an optimal dividend problem under a bankruptcy constraint where firms face a trade-off between potential bankruptcy and extraction of owner profits.
In contrast to previous works, more general cash flows, including Ornstein-Uhlenbeck processes, are considered.
The talk focuses on a study of qualitative properties both analytically and numerically, but rigorous proofs of existence of an optimal strategy, dynamic programming, as well as uniqueness of the Hamilton-Jacobi-Bellman equation are established.
We find that the optimal strategy is both a barrier and a band strategy and that it includes voluntary liquidation in parts of the state space.
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Speaker: Dieter Hendricks
Title: Detecting intraday financial market states using temporal clustering
Abstract: We propose the application of a high-speed maximum likelihood clustering algorithm to detect temporal financial market states, using correlation matrices estimated from intraday market microstructure features. We first determine the ex-ante intraday temporal cluster configurations to identify market states, and then study the identified temporal state features to extract state signature vectors which enable online state detection. The state signature vectors serve as low-dimensional state descriptors which can be used in learning algorithms for optimal planning in the high-frequency trading domain. We present a feasible scheme for real-time intraday state detection from streaming market data feeds. This study identifies an interesting hierarchy of system behaviour which motivates the need for time-scale-specific state space reduction for participating agents.
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Speaker: Davi ValladÃ£o
Title: Risk-constrained dynamic asset allocation via stochastic dual dynamic programming
Abstract: Latest approaches in the literature of Stochastic Dynamic Dual Programming (SDDP)
introduce risk aversion via time consistent dynamic risk measures. The objective function
is defined as a recursive formulation of a one-period coherent risk measures, usually the
convex combination of expectation and Conditional Value at Risk (CVaR). The recursive
model ensures time consistent policies and has a suitable economic interpretation of a
certainty equivalent. In practical applications however, a decision maker must
define the relative weights between expectation and CVaR to represent his risk aversion,
which is non-intuitive user-defined risk aversion parameter.
In this work, we propose an asset allocation model motivated by the actual decision pro-
cess in the financial market. Hedge funds hire managers to propose trading strategies
that maximize expected returns while risk departments impose constraints to strategies
with a high level of risk. We focus our developments on risk-constrained models arguing
it is reasonable to assume that an investor knows how much he is willing to lose in a
given period. Our approach assumes a Markov dependence of asset returns and imposes
one-period CVaR constraints ensuring a relative complete recourse time consistent model.
As opposed to recursive risk measures in the objective function, our model has a straight-
forward lower bound, considering maximization problem, and a direct way of representing
the risk-reward trade-off in a time varying efficient frontier.
Joint work with T. A. Silva and M. Poggi