Nesrin Okay

Financial Time Series Prediction Using Mixture of Experts

This paper investigates the use of artificial neural networks (ANN) in risk estimation of asset returns. Istanbul Stock Exchange (ISE) index (XU100) is studied with a mixture of experts ANN architecture using daily data over a 12-year period. Results are compared to feed-forward neural networks, multilayer perceptron (MLP) and radial basis function (RBF) networks and recurrent neural networks (RNN). They are also compared to widely accepted Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) volatility model. These results suggest that mixture of experts (MoE) have the strength to capture the volatility in index return series and prepares a valuable basis for financial decision making.

Analysis of Innovation and Energy Profiles in the Turkish Manufacturing Sector

We present Turkey’s manufacturing-sector innovation data and, for the first time, analyze likely relationships among GDP growth, sectoral innovation intensities, energy consumptions, and energy-saving potentials. We detect a power-law-like relationship between the projected energy-saving potentials and realized energy consumptions of the manufacturing-sector groups. We observe that the energy consumptions of the sectors do not change significantly despite varying innovation levels during transitions from economic crisis and recovery periods. We conclude that the Turkey’s manufacturing sectors’ energy consumptions are insensitive to their innovation levels, or their innovation activities are not energy-efficiency- and energy-saving-oriented, reflecting Turkey’s past supply-oriented energy policy. The leader innovating sectors are, nevertheless, expected to contribute more to Turkey’s energy-saving and energyefficiency policies if their innovation potentials can be directed to achieve higher energy savings and energy efficiencies via government incentives within the agenda of the recent energy-efficiency and R&D laws.

Bayesian changepoint and time-varying parameter learning in regime switching volatility models

This paper 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 volatility models with an unknown number of changepoints. Modern auxiliary particle filtering techniques are used to calculate the posterior densities and online forecasts. This approach also automatically deals with the common ancestral path dependence problem faced in these type volatility models. The model is tested on Borsa Istanbul (BIST) formerly known as Istanbul Stock Exchange (ISE) market data using daily log returns. A full structural changepoint specification is defined in which all parameters of the conditional variance of the volatility models are dynamic. Finally, it is shown with simulation experiments that the proposed approach partitions the series into several regimes and learns the parameters of each regimes volatility model in parallel with the multiple changepoint detection process.

AUXILIARY PARTICLE FILTERING BASED MULTIPLE CHANGEPOINT DETECTION IN VOLATILITY MODELS

This paper provides a solution for the multiple changepoint detection problems in financial time series prediction without knowing the number and location of changepoints. The proposed approach is a Sequential Monte Carlo (SMC) method for estimating GARCH based volatility models which are subject to an unknown number of changepoints. Recent Auxiliary Particle Filtering (APF) techniques are used to calculate the posterior densities and forecasts in real-time. This approach also automatically deals with the common path dependence problem of these type volatility models. We studied on simulated volatility data using GARCH model and have shown that the proposed approach works well with the generated data. For the non-linearity multiple changepoint detection problem, APF is investigated over the simulated volatility to model the switching regimes. In this study, a full structural changepoint specification is defined in which all parameters of the conditional variance of GARCH are subject to change respectively.