Multi-Process Analysis and Portfolio Optimization Based on Quantum Mechanics (QM) Under Risk Management in ASEAN Exchanges

2021 ◽  
pp. 46-61
Author(s):  
Chukiat Chaiboonsri ◽  
Satawat Wannapan
Keyword(s):  
Quantum Mechanics ◽  
Portfolio Optimization ◽  
Stock Prices ◽  
Financial Time Series ◽  
Process Analysis ◽  
Financial Time ◽  
Daily Data ◽  
Markowitz Portfolio ◽  
Future Data ◽  
Asean Countries

This research attempts to classify, predict, and manage the financial time-series trends of the large stock prices of significant companies in the development of e-commerce and e-business in the ASEAN countries. Moreover, the Markowitz portfolio optimization analysis based on quantum mechanics was utilized to find out the direction of e-commerce and e-business in the future. Data collection for this study consists of Maybank, PPB Group Berhad, Golden Agri-Resource, SingTel, and Global Logistic Properties. And the stock prices of those companies were carried out to this study from 2004 to 2018 by daily data. Interestingly, the empirical results would provide a possible solution and efficiently suggest a beneficial for the development of both e-commerce and e-business in the ASEAN countries. The commerce and business based on electronics in ASEAN, especially agribusiness, energy business, and telecommunication business, still play a major important role in the economy of ASEAN countries.

Multi-Process Analysis and Portfolio Optimization Based on Quantum Mechanics (QM) Under Risk Management in ASEAN Exchanges

2021 ◽  
pp. 400-415
Author(s):  
Chukiat Chaiboonsri ◽  
Satawat Wannapan
Keyword(s):  
Quantum Mechanics ◽  
Portfolio Optimization ◽  
Stock Prices ◽  
Financial Time Series ◽  
Process Analysis ◽  
Financial Time ◽  
Daily Data ◽  
Markowitz Portfolio ◽  
Future Data ◽  
Asean Countries

This research attempts to classify, predict, and manage the financial time-series trends of the large stock prices of significant companies in the development of e-commerce and e-business in the ASEAN countries. Moreover, the Markowitz portfolio optimization analysis based on quantum mechanics was utilized to find out the direction of e-commerce and e-business in the future. Data collection for this study consists of Maybank, PPB Group Berhad, Golden Agri-Resource, SingTel, and Global Logistic Properties. And the stock prices of those companies were carried out to this study from 2004 to 2018 by daily data. Interestingly, the empirical results would provide a possible solution and efficiently suggest a beneficial for the development of both e-commerce and e-business in the ASEAN countries. The commerce and business based on electronics in ASEAN, especially agribusiness, energy business, and telecommunication business, still play a major important role in the economy of ASEAN countries.

scholarly journals Deterministic chaos and forecasting in Amazon’s share prices

Equilibrium ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 253-273
Author(s):  
Michael Hanias ◽  
Stefanos Tsakonas ◽  
Lykourgos Magafas ◽  
Eleftherios I. Thalassinos ◽  
Loukas Zachilas
Keyword(s):  
Time Series ◽  
Chaos Theory ◽  
Stock Prices ◽  
Stock Price ◽  
Chaotic Behavior ◽  
Financial Time Series ◽  
Deterministic Chaos ◽  
Value Added ◽  
Share Prices ◽  
Daily Data

Research background: The application of non-linear analysis and chaos theory modelling on financial time series in the discipline of Econophysics. Purpose of the article: The main aim of the article is to identify the deterministic chaotic behavior of stock prices with reference to Amazon using daily data from Nasdaq-100. Methods: The paper uses nonlinear methods, in particular chaos theory modelling, in a case study exploring and forecasting the daily Amazon stock price. Findings & Value added: The results suggest that the Amazon stock price time series is a deterministic chaotic series with a lot of noise. We calculated the invariant parameters such as the maxi-mum Lyapunov exponent as well as the correlation dimension, managed a two-days-ahead forecast through phase space reconstruction and a grouped data handling method.

scholarly journals Use GARCH Models to Build a Econometric Model to Predict Average Daily Closing Prices of the Iraqi Stock Exchange for the Period 2013-2016

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 385-400
Author(s):  
Dr. Abed Ali Hamad ◽  
Dr. Ahmad Hussein Battal
Keyword(s):  
Stock Prices ◽  
Econometric Model ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Information Criterion ◽  
Garch Models ◽  
Financial Time ◽  
Egarch Model ◽  
Schwarz Criterion ◽  
Statistical Criteria

This research aims to build a standard model for the analysis and prediction of the average daily closing price fluctuations for companies registered in the Iraq Stock Exchange for the period 07/01/2013 to 30/06/2016, using the conditional generalized Heteroscedasticity Generalized Autoregressive (GARCH) models. As these models deal with the fluctuations that occur in the financial time series. The results of the analysis showed that the best model for predicting the volatility of average closing prices in the Iraq Stock Exchange is the EGARCH model (3,1), depending on the statistical criteria used in the preference between the models (Akaike Information Criterion, Schwarz Criterion), and these models can provide information for investors in order to reduce the risk resulting from fluctuations in stock prices in the Iraqi financial market.

scholarly journals Estimating and Forecasting Volatility of Financial Time Series in Pakistan with GARCH-type Models

2007 ◽  
Vol 12 (2) ◽  
pp. 115-149
Author(s):  
G.R. Pasha ◽  
Tahira Qasim ◽  
Muhammad Aslam
Keyword(s):  
Time Series ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Garch Models ◽  
Financial Time ◽  
Daily Data ◽  
Asymmetric Garch ◽  
Gjr Model ◽  
Forecasting Volatility ◽  
Tail Distributions

In this paper we compare the performance of different GARCH models such as GARCH, EGARCH, GJR and APARCH models, to characterize and forecast financial time series volatility in Pakistan. The comparison is carried out by comparing symmetric and asymmetric GARCH models with normal and fat-tailed distributions for the innovations, over short and long forecast horizons. The forecasts are evaluated according to a set of statistical loss functions. Daily data on the Karachi Stock Exchange (KSE) 100 index are analyzed. The empirical results demonstrate that the use of asymmetry in the GARCH models and the assumption of fat-tail distributions for the innovations improve the volatility forecasts. Overall, EGARCH fits the best while the GJR model, with both normal and non-normal innovations, seems to provide superior forecasting ability over short and long horizons.

Stochastic Variance Models in Discrete Time with Feedforward Neural Networks

2009 ◽  
Vol 21 (7) ◽  
pp. 1990-2008 ◽  
Author(s):  
Charles Andoh
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Discrete Time ◽  
Risk Taking ◽  
Stock Prices ◽  
Financial Time Series ◽  
Feedforward Neural Networks ◽  
Extra Information ◽  
Financial Time ◽  
Volatility Function

The study overcomes the estimation difficulty in stochastic variance models for discrete financial time series with feedforward neural networks. The volatility function is estimated semiparametrically. The model is used to estimate market risk, taking into account not only the time series of interest but extra information on the market. As an application, some stock prices series are studied and compared with the nonlinear ARX-ARCHX model.

scholarly journals The Effect of High Positive Autocorrelation on the Performance of Garch Family Models

2018 ◽  
Author(s):  
Ngozi G. Emenogu ◽  
Monday Osagie Adenomon
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Financial Time Series ◽  
Absolute Error ◽  
Series Data ◽  
Asset Return ◽  
Financial Time ◽  
Auto Regressive

This study compared the performance of five Family Generalized Auto-Regressive Conditional Heteroscedastic (fGARCH) models (sGARCH, gjrGARCH, iGARCH, TGARCH and NGARCH) in the presence of high positive autocorrelation. To achieve this, financial time series was simulated with autocorrelated coefficients as ρ = (0.8, 0.85, 0.9, 0.95, 0.99), at different time series lengths (as 250, 500, 750, 1000, 1250, 1500) and each trial was repeated 1000 times carried out in R environment using rugarch package. And the performance of the preferred model was judged using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Results from the simulation revealed that these GARCH models performances varies with the different autocorrelation values and at different time series lengths. But in the overall, NGARCH model dominates with 62.5% and 59.3% using RMSE and MAE respectively. We therefore recommended that investors, financial analysts and researchers interested in stock prices and asset return should adapt NGARCH model when there is high positive autocorrelation in the financial time series data.

scholarly journals GO-GJRSK Model with Application to Higher Order Risk-Based Portfolio

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1990
Author(s):  
Kei Nakagawa ◽  
Yusuke Uchiyama
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Financial Time Series ◽  
Higher Order ◽  
Estimation Methods ◽  
Time Series Modeling ◽  
Higher Order Moments ◽  
Financial Time ◽  
Proposed Model ◽  
Multivariate Financial Time Series

There are three distinguishing features in the financial time series, such as stock prices, are as follows: (1) Non-normality, (2) serial correlation, and (3) leverage effect. All three points need to be taken into account to model the financial time series. However, multivariate financial time series modeling involves a large number of stocks, with many parameters to be estimated. Therefore, there are few examples of multivariate financial time series modeling that explicitly deal with higher-order moments. Furthermore, there is no multivariate financial time series model that takes all three characteristics above into account. In this study, we propose the generalized orthogonal (GO)-Glosten, Jagannathan, and Runkle GARCH (GJR) model which extends the GO-generalized autoregressive conditional heteroscedasticity (GARCH) model and incorporates the three features of the financial time series. We confirm the effectiveness of the proposed model by comparing the performance of risk-based portfolios with higher-order moments. The results show that the performance with our proposed model is superior to that with baseline methods, and indicate that estimation methods are important in risk-based portfolios with higher moments.

A STOCHASTIC MODEL FOR MULTIFRACTAL BEHAVIOR OF STOCK PRICES

2004 ◽  
Vol 18 (04n05) ◽  
pp. 681-689 ◽  
Author(s):  
UMBERTO L. FULCO ◽  
MARCELO L. LYRA ◽  
FILIPPO PETRONI ◽  
MAURIZIO SERVA ◽  
GANDHI M. VISWANATHAN
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Financial Time Series ◽  
Probability Density Functions ◽  
Density Functions ◽  
Fat Tails ◽  
Volatility Clustering ◽  
Financial Time ◽  
Time Scaling ◽  
Multifractal Behavior

We investigate the general problem of how to model the kinematics of stock prices without considering the dynamical causes of motion. We propose a Markovian stochastic process which is able to reproduce the experimentally observed volatility clustering and fat tails in the probability density functions (PDF) of financial time series. More importantly, the process also reproduces the PDF time scaling, the power law memory of volatility and the apparent multifractality of the time series up to the time scale which is experimentally observable.

scholarly journals A First Application of Independent Component Analysis to Extracting Structure from Stock Returns

1997 ◽  
Vol 08 (04) ◽  
pp. 473-484 ◽  
Author(s):  
Andrew D. Back ◽  
Andreas S. Weigend
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Stock Returns ◽  
Stock Prices ◽  
Financial Time Series ◽  
Component Analysis ◽  
Independent Component ◽  
Signal Processing Technique ◽  
Independent Components ◽  
Financial Time

This paper explores the appliation of a signal processing technique known as independent component analysis (ICA) or blind source separation to multivariate financial time series such as a portfolio of stocks. The key idea of ICA is to linearly map the observed multivariate time series into a new space of statistically independent components (ICs). We apply ICA to three years of daily returns of the 28 largest Japanese stocks and compare the results with those obtained using principal component analysis. The results indicate that the estimated ICs fall into two categories, (i) infrequent large shocks (responsible for the major changes in the stock prices), and (ii) frequent smaller fluctuations (contributing little to the overall level of the stocks). We show that the overall stock price can be reconstructed surprisingly well by using a small number of thresholded weighted ICs. In contrast, when using shocks derived from principal components instead of independent components, the reconstructed price is less similar to the original one. ICA is shown to be a potentially powerful method of analyzing and understanding driving mechanisms in financial time series. The application to portfolio optimization is described in Chin and Weigend (1998).

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