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Equity asset volatility modeling and forecasting provide key information for risk management, portfolio construction, financial decision making, and derivative pricing. Realized volatility models outperform autoregressive conditional heteroskedasticity and stochastic volatility models in out-of-sample forecasting. Gain in forecasting performance is achieved when models comprise volatility jump components...
The correct pricing of financial derivatives plays a key role in risk management and in hedge operations. Besides the Black and Scholes closed-form formula simplicity and good results for pricing European options, several of the assumptions used in the method may be unrealistic and influence the results significantly. In order to overcome this limitation, this paper suggests an evolving possibilistic...
This paper addresses stock market assets return volatility forecasting and possibilistic fuzzy modeling. A recursive possibilistic fuzzy modeling (rPFM) approach is suggested to deal with the identification of systems affected by outliers and noisy data due to the use of memberships and typicalities to cluster data. Since financial markets are affected by news, expectations and investors psychology,...
Financial interval time series (ITS) is a sequence of the highest and lowest values of financial data such as the highest and lowest prices of assets observed at successive time steps of a time interval. Price interval data carry key information to estimate price volatility, and provide valuable information to develop investment strategies. This paper suggests an evolving possibilisitc fuzzy modeling...
This paper suggests an evolving possibilistic approach for fuzzy modeling of time-varying processes. The approach is based on an extension of the possibilistic fuzzy c-means clustering and functional fuzzy rule-based modeling. Evolving possibilistic fuzzy modeling (ePFM) employs memberships and typicalities to recursively cluster data, and uses participatory learning to adapt the model structure as...
This paper suggests a recursive possibilistic approach for fuzzy modeling of time-varying processes. The approach is based on an extension of the possibilistic fuzzy c-means clustering and functional fuzzy rule-based modeling. Recursive possibilistic fuzzy modeling (rPFM) employs memberships and typicalities to cluster data. Functional fuzzy models uses affine functions in the fuzzy rule consequents...
This p aper introduces an evolving feedforward single hidden layer neural network with extreme learning. The evolving neural network simultaneously adapts its structure and updates its weights using recursive algorithms. Neurons in the hidden layer are added whenever necessary by the implicit nature of the input data. The number of neurons in the hidden layer is found using a recursive granulation...
In this paper a new approach to data stream evolving fuzzy model identification is given. The structure of the model is given in the form of Takagi-Sugeno and the partitioning of the input-output space is obtained using a fuzzy c-regression clustering method and the approach also involves the evolving properties. The method is given in a recursive form. The proposed approach is shown with two simple...
Because of the diversity of portfolios based on assets throughout international markets, exchange rate prediction plays an important role in risk management, asset allocation, and trading strategies. This paper aims to investigate the use of a recent paradigm of recurrent neural networks, echo state networks (ESNs), applied to forecasting and trading currency exchange rates. It does so by benchmarking...
Equity assets volatility modeling and forecasting are fundamental in risk management, portfolio construction, financial decision making and derivative pricing. The use of realized volatility models outperforms GARCH and related stochastic volatility models in out-of-sample forecasting. Gains in performance can be achieved by separately considering volatility jump components. This paper suggests an...
This paper suggests an approach to develop a class of evolving neural fuzzy networks with adaptive feature selection. The approach uses the neo-fuzzy neuron structure in conjunction with an incremental learning scheme that, simultaneously, selects the input variables, evolves the network structure, and updates the neural network weights. The mechanism of the adaptive feature selection uses statistical...
Genetic Fuzzy Systems have been successfully used as a modeling approach for numerous applications. There is an increasing interest on how to construct fuzzy models for different types of complex systems such as highly nonlinear, large-scale, multiobjective, and high-dimensional systems. Current state of the art indicates the use of fast and scalable evolutionary algorithms in complex fuzzy modeling...
This paper introduces a neural fuzzy network approach for evolving system modeling. The approach uses neofuzzy neurons and a neural fuzzy structure monished with an incremental learning algorithm that includes adaptive feature selection. The feature selection mechanism starts considering one or more input variables from a given set of variables, and decides if a new variable should be added, or if...
Evolving participatory learning fuzzy modeling is a flexible and effective method to handle real world complex systems. It is capable to process and learn from streams of data online, and is a natural candidate to find fuzzy rule-based model structures in dynamic environments. This paper extends the evolving participatory learning fuzzy approach for multi-input multi-output - MIMO - processes modeling...
The 2012 FUZZ-IEEE conference competition “Learning Fuzzy Systems from Data” aims to establish the empirical accuracy of fuzzy forecasting algorithms in the domain of prediction of the sales volume of petroleum products. Currently, there are no guidelines or consensus on a best practice methodology. This paper proposes evolving fuzzy linear regression trees (eFT) to extract from both, daily prices...
This paper introduces an approach to evolve fuzzy modeling that simultaneously performs adaptive feature selection. The model is a fuzzy linear regression tree whose topology can be continuously updated using statistical tests. A fuzzy linear regression tree is a fuzzy tree with linear model in each leaf. The number of tree nodes and the number of inputs can be updated for each new input. The precision...
During the recent decades, option pricing became an important topic in computational finance. The main issue is to obtain a model of option prices that reflects price movements observed in the real world. In this paper we address option pricing using an evolving fuzzy system model and Brazilian interest rate options pricing data. Evolving models are particularly appropriate since it gradually develops...
Out-of-equilibrium price dynamics are studied using agent-based computational models. We examine how agents with bounded rationality act in an environment in which they do not know precisely both relative prices and the level of the prices. We model imprecision and uncertainty with fuzzy numbers and use the theory of probabilistic sets as part of the simulation model. Our results explain both positive...
This paper introduces a new approach for evolving fuzzy modeling based on a tree structure. The system is a fuzzy linear regression tree whose topology can be continuously updated using a statistical model selection test. A fuzzy linear regression tree is a fuzzy tree with linear model in each leaf. The evolving linear regression approach is evaluated on a forecasting problem and its performance compared...
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