Multiresolution analysis of S&P500 time series

2018-01-01
KILIC, Deniz Kenan
Uğur, Ömür
Time series analysis is an essential research area for those who are dealing with scientific and engineering problems. The main objective, in general, is to understand the underlying characteristics of selected time series by using the time as well as the frequency domain analysis. Then one can make a prediction for desired system to forecast ahead from the past observations. Time series modeling, frequency domain and some other descriptive statistical data analyses are the primary subjects of this study: indeed, choosing an appropriate model is at the core of any analysis to make a satisfactory prediction. In this study Fourier and wavelet transform methods are used to analyze the complex structure of a financial time series, particularly, S&P500 daily closing prices and return values. Multiresolution analysis is naturally handled by the help of wavelet transforms in order to pinpoint special characteristics of S&P500 data, like periodicity as well as seasonality. Besides, further case study discussions include the modeling of S&P500 process by invoking linear and nonlinear methods with wavelets to address how multiresolution approach improves fitting and forecasting results.
ANNALS OF OPERATIONS RESEARCH

Suggestions

Multiresolution analysis of S&P500 time series
Kılıç, Deniz Kenan; Uğur, Ömür; Department of Financial Mathematics (2015)
Time series analysis is an essential research area for almost all people who are dealing with scientific and engineering problems. Main aim is to understand the underlying characteristics of the time series by using time as well as frequency domain analyses. Then one can make a prediction for the desired system to forecast observations ahead. Time series modeling, frequency domain analysis and some descriptive statistical analysis are main subjects of this thesis. Choosing an appropriate model is the main f...
Hybrid wavelet-neural network models for time series data
Kılıç, Deniz Kenan; Uğur, Ömür; Department of Financial Mathematics (2021-3-3)
The thesis aims to combine wavelet theory with nonlinear models, particularly neural networks, to find an appropriate time series model structure. Data like financial time series are nonstationary, noisy, and chaotic. Therefore using wavelet analysis helps better modeling in the sense of both frequency and time. S&P500 (∧GSPC) and NASDAQ (∧ IXIC) data are divided into several components by using multiresolution analysis (MRA). Subsequently, each part is modeled by using a suitable neural network structure. ...
Basis in nuclear Frechet spaces
Erkurşun, Nazife; Nurlu, Mehmet Zafer; Department of Mathematics (2006)
Existence of basis in locally convex space has been an important problem in functional analysis for more than 40 years. In this thesis the conditions for the existence of basis are examined. These thesis consist of three parts. The first part is about the exterior interpolative conditions. The second part deals with the inner interpolative conditions on nuclear frechet space. These are sufficient conditions on existence of basis. In the last part, it is shown that for a regular nuclear Köthe space the inner...
Parameter optimization of chemically activated mortaars containing high volumes of pozzolan by statistical design and analysis of experiments
Aldemir, Başak; Saatçioğlu, Ömer; Department of Industrial Engineering (2006)
This thesis illustrates parameter optimization of early and late compressive strengths of chemically activated mortars containing high volumes of pozzolan by statistical design and analysis of experiments. Four dominant parameters in chemical activation of natural pozzolans are chosen for the research, which are natural pozzolan replacement, amount of pozzolan passing 45 æm sieve, activator dosage and activator type. Response surface methodology has been employed in statistical design and analysis of experi...
Consensus clustering of time series data
Yetere Kurşun, Ayça; Batmaz, İnci; İyigün, Cem; Department of Scientific Computing (2014)
In this study, we aim to develop a methodology that merges Dynamic Time Warping (DTW) and consensus clustering in a single algorithm. Mostly used time series distance measures require data to be of the same length and measure the distance between time series data mostly depends on the similarity of each coinciding data pair in time. DTW is a relatively new measure used to compare two time dependent sequences which may be out of phase or may not have the same lengths or frequencies. DTW aligns two time serie...
Citation Formats
D. K. KILIC and Ö. Uğur, “Multiresolution analysis of S&P500 time series,” ANNALS OF OPERATIONS RESEARCH, pp. 197–216, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/31412.