Browsing by Author "Safarzadeh, Omid"
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Item Open Access Exploring shock and volatility transmission between oil and Chinese industrial raw materials(Elsevier, 2021-01-07) Kirkulak-Uludag, B.; Safarzadeh, OmidThe main objective of this paper is to investigate the volatility spillover between oil prices and the Chinese industrial raw materials stock indices including oil, coal, iron and non-ferrous metals. In order to achieve this task, OPEC and WTI oil prices were used as oil benchmarks and several multivariate GARCH models were applied on daily closing prices of stock indices for the period from 2004 through 2014. Among the models, VAR-GARCH model fits the data best. The findings show significant volatility spillover between oil and the Chinese raw materials stock returns. However, the spillover is unidirectional and it is more apparent from past oil shocks to the Chinese raw materials stock returns. This unidirectional spillover can be attributed to the structure of the Chinese stock market, which is slightly integrating into the global stock markets and yet is detached from its own real economy. Moreover, the findings suggest that the conditional correlations of OPEC oil and the Chinese raw materials stock indices are higher than those of WTI oil and stock indices. Overall, the findings have important implications for both portfolio managers and policy makers in terms of risk management.Item Open Access Noise robust focal distance detection in laser material processing using CNNs and Gaussian processes(S P I E - International Society for Optical Engineering, 2022-05-17) Elahi, Sepehr; Polat, Can; Safarzadeh, Omid; Elahi, ParvizIn this work, we investigate the effects of noise on real-time focal distance control for laser material processing by generating the images of a sample at different focal lengths using Fourier optics and then designing, training, and testing a deep learning model in order to detect the focal distances from the simulated images with varying standard deviations of added noise. We simulate both input noise, such as noise due to surface roughness, and output noise, such as detection camera noise, by adding zero-mean Gaussian noise to the source wave and the simulated image, respectively, for different focal distances. We then train a convolutional neural network combined with a Gaussian process classifier to predict focus distances of noisy images together with confidence ratings for the predictions.