M. Serdar Yümlü

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This paper makes a comparison of global, feedback and smoothed-piecewise neural prediction models for financial time series (FTS) prediction problem. Each model is implemented by various neural network (NN) architectures: global model by a multilayer perceptron (MLP), feedback model by a recurrent neural network (RNN) and smoothed-piecewise model by a(More)
BAYESIAN CHANGEPOINT AND TIME-VARYING PARAMETER LEARNING IN REGIME SWITCHING VOLATILITY MODELS This dissertation proposes a combined state and piecewise time-varying parameter learning technique in regime switching volatility models using multiple changepoint detection. This approach is a Sequential Monte Carlo method for estimating GARCH & EGARCH based(More)
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