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A multi-agent nonlinear Markov model of the order book     A multi-agent nonlinear   Markov model  order book       font style='font-size:12px;'> 2012/9/14
We introduce and treat rigorously a new multi-agent model of the limit order book. Our model is designed to explain a behavior of the market when new information a ecting the market arrives. Our model...
We develop algorithms for the numerical computation of the quadratic hedging strategy in incomplete markets modeled by pure jump Markov process. Using the Hamilton-Jacobi-Bellman approach, the value f...
In this paper we propose a new stochastic model based on a generalization of semi-Markov chains to study the high frequency price dynamics of traded stocks. We assume that the financial returns are de...
This article compares the reliability of a discrete-time and a continuous-time Markov chain model for estimating credit risk and for investigating loans of Chiao Tung Bank in Taiwan. The continuous-ti...
We introduce a new geometric approach that constructs a transition kernel of Markov chain. Our method always minimizes the av- erage rejection rate and even reduce it to zero in many relevant cases,...
We develop a nonparametric regression-based goodness-of-fit test for multifactor continuous-time Markov models using the conditional characteristic function, which often has a convenient closed form o...
Abstract: In a previous paper it was shown that a Markov-functional model with log-normally distributed rates in the terminal measure displays nonanalytic behaviour as a function of the volatility, wh...
The Markov property is a fundamental property in time series analysis and is often assumed in economic and …nancial modelling. We develop a new test for the Markov property using the conditional chara...
We develop a nonparametric regression-based goodness-of-fit test for multifactor continuous-time Markov models using the conditional characteristic function, which often has a convenient closed form o...
We consider a heat kernel approach for the development of stochastic pricing kernels. The kernels are constructed by positive propagators, which are driven by time-inhomogeneous Markov processes. We m...
This paper develops a matrix-variate adaptive Markov chain Monte Carlo (MCMC) methodology for Bayesian Cointegrated Vector Auto Regressions (CVAR). We replace the popular approach to sampling Bayesian...
Large trades in a financial market are usually split into smaller parts and traded incrementally over extended periods of time. We address these large trades as hidden orders. In order to identify an...
We apply a Coupled Markov Chain approach to model rating transitions and thereby default probabilities of companies. We estimate parameters by applying a maximum likeli-hood estimation using a large s...
We propose several statistics to test the Markov hypothesis forβ-mixing stationary processes sampled at discrete time intervals. Our tests are based on the Chapman–Kolmogorov equation. We establish th...
This paper formed part of a preliminary research report for a risk consultancy and academic research. Stochastic Programming models provide a powerful paradigm for decision making under uncertainty. I...

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