scholarly journals Authors: Nurul Hanis Aminuddin Jafry ; Ruzanna Ab Razak ; Noriszura Ismail

2018 ◽  
pp. 646-652
Author(s):  
Nurul Hanis Aminuddin Jafry ◽  
Ruzanna Ab Razak ◽  
Noriszura Ismail
Keyword(s):  
Decision Making ◽  
Stock Markets ◽  
Dependence Structure ◽  
Time Varying ◽  
Sample Data

Studies on dependence between stock markets are important because of their implications on the process of decision-making in investment. Many previous studies measure the dependence between markets using static copula. However, in recent years, time-varying copula has been used as an alternative for measuring dependence due to its capability of capturing time-varying dependence between markets. This study uses both static and time-varying copulas to measure the dependence structure between Malaysia and major stock markets (US, UK and Japan) based on the sample data from year 2007 Q1 until year 2017 Q3. The results reveal that the best model for all pairs of indices is the time-varying SJC copula, which also reveals that the Malaysia-US pair has the weakest dependence structure compared to other pairs. In terms of lower and upper tails, the Malaysia-UK and the Malaysia-Japan pairs have the strongest dependence structure respectively. Evidence from this research suggests that diversifications involving Malaysia and US stock markets are not effective.

scholarly journals Multi-Horizon Dependence between Crude Oil and East Asian Stock Markets and Implications in Risk Management

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 294 ◽  
Author(s):  
Xiaojing Cai ◽  
Shigeyuki Hamori ◽  
Lu Yang ◽  
Shuairu Tian
Keyword(s):  
Risk Management ◽  
Crude Oil ◽  
Stock Markets ◽  
East Asian ◽  
Tail Dependence ◽  
Dependence Structure ◽  
Time Varying ◽  
Risk Hedging ◽  
Long Run ◽  
East Asian Stock Markets

This paper examines the dynamic dependence structure of crude oil and East Asian stock markets at multiple frequencies using wavelet and copulas. We also investigate risk management implications and diversification benefits of oil-stock portfolios by calculating and comparing risk and tail risk hedging performance. Our results provide strong evidence of time-varying dependence and asymmetric tail dependence between crude oil and East Asian stock markets at different frequencies. The level and fluctuation of their dependencies increase as time scale increases. Furthermore, we find the time-varying hedging benefits differ at investment horizons and reduced over the long run. Our results suggest that crude oil could be used as a hedge and safe haven against East Asian stock markets, especially in the short- and mid-term.

Volatility and dependence structures of Latin American stock markets

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Guilherme Cardoso ◽  
Karem Ribeiro ◽  
Luciano Carvalho
Keyword(s):  
Latin America ◽  
Latin American ◽  
Stock Markets ◽  
Dependence Structure ◽  
Subprime Crisis ◽  
Time Varying ◽  
Content Type ◽  
Macroeconomic Effects ◽  
The Relationship ◽  
Dependence Structures

PurposeRisk management has been crucial to investors and regulators for pursuing market diversification opportunities and developing strategies to ensure market stability. This study examines the dependence structures of volatility, related to co-movements and macroeconomic effects, among Latin American stock markets and the risk–return spectrum benefits in the Latin American market using time-varying returns and volatility forecasts within a multivariate structure.Design/methodology/approachThe sample comprised the largest stock markets in Latin America during the period from January 2000 to December 2017 and copulas and multivariate models were applied.FindingsThe results indicated that the copula with the best fit for modeling the dependence structure of the markets was symmetric Joe-Clayton with time-varying parameters. The dependence volatility structure was higher in the positive (upper tail) than in the negative (lower tail) returns, which may indicate that the Latin American markets had diversification benefits during downturns. Evidence of market coupling was found during times of the global crisis (subprime crisis) in Latin America. The presence of monetary and temporal effects over the dependence structures suggests that investors may obtain gains in a multivariate structure with copula distributions.Originality/valueThe findings will be of interest to researchers and practitioners for several reasons. First, this study contributes to the growing literature on the relationship between market dependence and volatility. Second, it indicates that the Latin American markets may present diversification advantages during downturns. Third, it informs the influence of macroeconomic effects on Latin American markets. The models that included the nonnormal and asymmetric characteristics of the financial market yielded better results in terms of less information loss and data adherence.

Segments Influencing Individual Investors Behavior And Their Investment Decision

2019 ◽  
Vol 118 (9) ◽  
pp. 154-160
Author(s):  
Dr. Kartikey Koti
Keyword(s):  
Decision Making ◽  
Social Factors ◽  
Stock Markets ◽  
Investment Decision ◽  
Individual Investors ◽  
Factors Affecting ◽  
The Social ◽  
Karnataka State ◽  
Essential Idea

The essential idea of this assessment is investigate the social factors affecting particular theorists' decisions making limit at Indian Stock Markets. In the examination coordinated standard of direct is Classified subject to two estimations the first is Heuristic (Decision making) and the resulting one is prospect.. For the assessment coordinated the data used is basic natured which is assembled through a sorted out survey from 100 individual money related authorities based out in Hubli and Dharwad city, Karnataka State in India on an accommodating way. The respondents were both sex and overwhelming part male were 68% . These theorists were having a spot with the age bundle between35-45 which is 38%. These respondents have completed their graduation were around 56%. These respondents had work inclusion of 5 to 10 years which is 45% and the majority of which were used in government portion which is 56%. Their compensation was between 4 to 6 Lakh and were fit for placing assets into business areas. The money related experts were widely masterminded placing assets into different portfolios like 32% in Share market and 20 % in Fixed store. These examiners mode to known various endeavor streets were through News, family and allies.  

Making investment decisions in stock markets using a forecasting-Markowitz based decision-making approaches

2019 ◽  
Vol 15 (2) ◽  
pp. 647-659 ◽  
Author(s):  
Zahra Moeini Najafabadi ◽  
Mehdi Bijari ◽  
Mehdi Khashei
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Stock Markets ◽  
Time Series Prediction ◽  
Investment Decisions ◽  
Rate Of Return ◽  
Expected Returns ◽  
Prediction Methods ◽  
Content Type ◽  
Markowitz Model

Purpose This study aims to make investment decisions in stock markets using forecasting-Markowitz based decision-making approaches. Design/methodology/approach The authors’ approach offers the use of time series prediction methods including autoregressive, autoregressive moving average and artificial neural network, rather than calculating the expected rate of return based on distribution. Findings The results show that using time series prediction methods has a significant effect on improving investment decisions and the performance of the investments. Originality/value In this study, in contrast to previous studies, the alteration in the Markowitz model started with the investment expected rate of return. For this purpose, instead of considering the distribution of returns and determining the expected returns, time series prediction methods were used to calculate the future return of each asset. Then, the results of different time series methods replaced the expected returns in the Markowitz model. Finally, the overall performance of the method, as well as the performance of each of the prediction methods used, was examined in relation to nine stock market indices.

Time-varying inter-market linkage of international stock markets

2008 ◽  
Vol 40 (19) ◽  
pp. 2501-2507 ◽  
Author(s):  
Y.-P. Hu ◽  
L. Lin ◽  
J.-W. Kao
Keyword(s):  
Stock Markets ◽  
Time Varying ◽  
International Stock Markets ◽  
Market Linkage

scholarly journals A Tutorial on Evaluating the Time-Varying Discrimination Accuracy of Survival Models Used in Dynamic Decision Making

2018 ◽  
Vol 38 (8) ◽  
pp. 904-916 ◽  
Author(s):  
Aasthaa Bansal ◽  
Patrick J. Heagerty
Keyword(s):  
Decision Making ◽  
Statistical Methods ◽  
Sensitivity And Specificity ◽  
Prognostic Model ◽  
Dynamic Decision Making ◽  
Time Dependent ◽  
Survival Models ◽  
Biliary Cirrhosis ◽  
Time Varying ◽  
Over Time

Many medical decisions involve the use of dynamic information collected on individual patients toward predicting likely transitions in their future health status. If accurate predictions are developed, then a prognostic model can identify patients at greatest risk for future adverse events and may be used clinically to define populations appropriate for targeted intervention. In practice, a prognostic model is often used to guide decisions at multiple time points over the course of disease, and classification performance (i.e., sensitivity and specificity) for distinguishing high-risk v. low-risk individuals may vary over time as an individual’s disease status and prognostic information change. In this tutorial, we detail contemporary statistical methods that can characterize the time-varying accuracy of prognostic survival models when used for dynamic decision making. Although statistical methods for evaluating prognostic models with simple binary outcomes are well established, methods appropriate for survival outcomes are less well known and require time-dependent extensions of sensitivity and specificity to fully characterize longitudinal biomarkers or models. The methods we review are particularly important in that they allow for appropriate handling of censored outcomes commonly encountered with event time data. We highlight the importance of determining whether clinical interest is in predicting cumulative (or prevalent) cases over a fixed future time interval v. predicting incident cases over a range of follow-up times and whether patient information is static or updated over time. We discuss implementation of time-dependent receiver operating characteristic approaches using relevant R statistical software packages. The statistical summaries are illustrated using a liver prognostic model to guide transplantation in primary biliary cirrhosis.

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