Abstract: In this paper, we proposed a nonparallel hyperplanes classier for multi-class classication, termed as NHCMC. This method inherits the idea of multiple birth support vector machine(MBSVM), that is the "max" decision criterion instead of the "min" one, but it has the incomparable advantages than MBSVM. First, the optimization problems in NHCMC can be solved eciently by sequential minimization optimization (SMO) without needing to compute the large inverses matrices before training as SVMs usually do; Second, kernel trick can be applied directly to NHCMC, which is superior to existing MBSVM. Experimental results on lots of data sets show the eciency of our method in multi-class classication accuracy.

Abstract: One-way coupling often occurs in multi-dimensional stochastic models in finance. In this paper, we develop a highly efficient Monte Carlo (MC) method for pricing European options under a N-dimensional one-way coupled model, where N is arbitrary. The method is based on a combination of (i) the powerful dimension and variance reduction technique, referred to as drMC, developed in Dang et. al (2014), that exploits this structure, and (ii) the highly effiective multilevel MC (mlMC) approach developed by Giles (2008). By first applying Step (i), the dimension of the problem is reduced from N to 1, and as a result, Step (ii) is essentially an application of mlMC on a 1-dimensional problem. Numerical results show that, through a careful construction of the ml-dr estimator, improved efficiency expected from the Milstein timestepping with first order strong convergence can be achieved. Moreover, our numerical results show that the proposed ml-drMC method is significantly more efficient than the mlMC methods currently available for multi-dimensional stochastic problems.

Abstract: This paper presents a Hierarchical Hidden Markov Model used to capture the USD/COP market sentiment dynamics choosing from uptrend or downtrend latent regimes based on observed feature vector realizations calculated from transaction prices and wavelet-transformed order book volume dynamics. The HHMM learned a natural switching buy/uptrend sell/downtrend trading strategy using a training-validation framework over one month of market data. The model was tested on the following two months, and its performance was reported and compared to results obtained from randomly classified market states and a feed-forward Neural Network. This paper also separately assessed the contribution to the model’s performance of the order book information and the wavelet transformation.

Abstract: There are plenty of researches dedicated to financial system stability, which takes significant place in prevention of financial crisis and its consequences. However banking system and external environment interaction and customers behaviour influence on the banking system stability are poorly studied. Current paper propose agent-based model of banking system and its external environment. We show how customers behaviour characteristics affect a banking system stability. Optimal interval for total environmental funds towards banking system wealthy is performed.