Nonlinear Dependence Structure in Emerging and Advanced Stock Markets

2019 ◽  
pp. 210-221
Author(s):  
Roengchai Tansuchat ◽  
Woraphon Yamaka
Keyword(s):  
Stock Markets ◽  
Nonlinear Dependence ◽  
Dependence Structure

scholarly journals A study of co-movements between U.S. and Latin American stock markets: A cross-bicorrelations perspective

DYNA ◽  
2016 ◽  
Vol 83 (196) ◽  
pp. 143-148 ◽  
Author(s):  
Semei Coronado-Ramirez ◽  
Omar Rojas-Altamirano ◽  
Rafael Romero-Meza ◽  
Francisco Venegas-Martínez
Keyword(s):  
Stock Market ◽  
Latin American ◽  
Stock Markets ◽  
Nonlinear Dependence ◽  
Standard And Poor's ◽  
Non Linear ◽  
Market Indexes

<p>This work applies a test that detects dependence between pairs of variables. The kind of dependence is a non-linear one, and the test is known as cross-bicorrelation, which is associated with Brooks and Hinich [1]. We study dependence periods between U.S. Standard and Poor's 500 (SP500), used as a benchmark, and six Latin American stock market indexes: Mexico (BMV), Brazil (BOVESPA), Chile (IPSA), Colombia (COLCAP), Peru (IGBVL) and Argentina (MERVAL). We have found windows of nonlinear dependence and comovement between the SP500 and the Latin American stock markets, some of which coincide with periods of crisis, leading to an interpretation of a possible contagion or interdependence.</p>

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.

scholarly journals ESTIMASI NILAI CONDITIONAL VALUE AT RISK MENGGUNAKAN FUNGSI GAUSSIAN COPULA

2015 ◽  
Vol 4 (4) ◽  
pp. 188
Author(s):  
HERLINA HIDAYATI ◽  
KOMANG DHARMAWAN ◽  
I WAYAN SUMARJAYA
Keyword(s):  
At Risk ◽  
Confidence Level ◽  
Value At Risk ◽  
Risk Measure ◽  
Copula Function ◽  
Gaussian Copula ◽  
Nonlinear Dependence ◽  
Conditional Value At Risk ◽  
Dependence Structure ◽  
Measurement Tools

Copula is already widely used in financial assets, especially in risk management. It is due to the ability of copula, to capture the nonlinear dependence structure on multivariate assets. In addition, using copula function doesn’t require the assumption of normal distribution. There fore it is suitable to be applied to financial data. To manage a risk the necessary measurement tools can help mitigate the risks. One measure that can be used to measure risk is Value at Risk (VaR). Although VaR is very popular, it has several weaknesses. To overcome the weakness in VaR, an alternative risk measure called CVaR can be used. The porpose of this study is to estimate CVaR using Gaussian copula. The data we used are the closing price of Facebook and Twitter stocks. The results from the calculation using 90%  confidence level showed that the risk that may be experienced is at 4,7%, for 95% confidence level it is at 6,1%, and for 99% confidence level it is at 10,6%.

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.

Capital Market Integration of Selected ASEAN Countries and its Investment Implications

2017 ◽  
pp. 5-33
Author(s):  
Hung Quang Do ◽  
László Kónya ◽  
Bhatti M. Ishaq
Keyword(s):  
Capital Market ◽  
Stock Markets ◽  
Market Integration ◽  
The Philippines ◽  
Dependence Structure ◽  
Global Finance ◽  
Capital Market Integration ◽  
International Stock Markets ◽  
The Us ◽  
Asean Countries

This paper investigates the dynamic integration of ASEAN6 stock markets (Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam) with international stock markets (the US, the ASEAN bloc, and Asia) in an ARMA-EGARCH-M and a vector autoregression models (VAR) using weekly price returns from January 2000 to October 2015. The interaction channels between these markets provide valuable information to investors about possible investment gateways into these ASEAN6 countries. The dependence structure of unexpected returns between the US and ASEAN6 countries, and contagion of the Global Finance Crisis (GFC) are explored in the paper. The results indicate that investors from the US and Asia could gain diversification benefits by investing in the stock markets of Indonesia, Malaysia, the Philippines, Singapore and Thailand. At the same time, ASEAN investors might wish to invest in Vietnam for their investment diversification. However, the Vietnamese market is found to be highly dependent on the US and Asian markets.

COVID-19 Pandemic and the Dependence Structure of Global Stock Markets

2021 ◽  
Author(s):  
Faheem Aslam ◽  
Khurrum Mughal ◽  
Saqib Aziz ◽  
Muhammad Farooq Ahmad ◽  
Dhoha Trabelsi
Keyword(s):  
Stock Markets ◽  
Dependence Structure ◽  
Global Stock Markets ◽  
Global Stock

Conditional dependence structure between oil prices and international stock markets

2019 ◽  
Vol 14 (2) ◽  
pp. 439-467 ◽  
Author(s):  
Wajdi Hamma ◽  
Bassem Salhi ◽  
Ahmed Ghorbel ◽  
Anis Jarboui
Keyword(s):  
Stock Markets ◽  
Oil Prices ◽  
Conditional Variance ◽  
Optimal Portfolio ◽  
Dependence Structure ◽  
Hedging Strategy ◽  
Content Type ◽  
Conditional Dependence ◽  
Hedge Ratios ◽  
Stock Portfolio

Purpose The purpose of this paper is to analyze the optimal hedging strategy of the oil-stock dependence structure. Design/methodology/approach The methodology consists to model the data over the daily period spanning from January 02, 2002 to May 19, 2016 by a various copula functions to better modeling the dependence between crude oil market and stock markets, and to use dependence coefficients and conditional variance to calculate optimal portfolio weights and optimal hedge ratios, and to suggest the best hedging strategy for oil-stock portfolio. Findings The findings show that the Gumbel copula is the best model for modeling the conditional dependence structure of the oil and stock markets in most cases. They also indicate that the best hedging strategy for oil price by stock market varies considerably over time, but this variation depends on both the index introduced and the model used. However, the conditional copula method with skewed student more effective than the other models to minimize the risk of oil-stock portfolio. Originality/value This research implication can be valuable for portfolio managers and individual investors who seek to make earnings by diversifying their portfolios. The findings of this study provide evidence of the importance of stock assets for making an optimal portfolio consisting of oil in the case of investments in oil and stock markets. This paper attempts to fill the voids in the literature on volatility among oil prices and stock markets in two important areas. First, it uses copulas to investigate the conditional dependence structure of the oil crude and stock markets in the oil exporting and importing countries. Second, it uses the dependence coefficients and conditional variance to calculate dynamic hedge ratios and risk-minimizing optimal portfolio weights for oil–stock.

A MULTIVARIATE PURE-JUMP MODEL WITH MULTI-FACTORIAL DEPENDENCE STRUCTURE

2012 ◽  
Vol 15 (04) ◽  
pp. 1250028 ◽  
Author(s):  
ROBERTO MARFÈ
Keyword(s):  
Jump Processes ◽  
Stock Index ◽  
Nonlinear Dependence ◽  
Dependence Structure ◽  
New Approach ◽  
Variance Gamma Process ◽  
Jump Model ◽  
Multivariate Simulation ◽  
Linear And Nonlinear ◽  
The Difference

In this work we propose a new approach to build multivariate pure jump processes. We introduce linear and nonlinear dependence, without restrictions on marginal properties, by imposing a multi-factorial structure separately on both positive and negative jumps. Such a new approach provides higher flexibility in calibrating nonlinear dependence than in other comparable Lévy models in the literature. Using the notion of multivariate subordinator, this modeling approach can be applied to the class of univariate Lévy processes which can be written as the difference of two subordinators. A common example in the financial literature is the variance gamma process, which we extend to the multivariate (multi-factorial) case. The model is tractable and a straightforward multivariate simulation procedure is available. An empirical analysis documents an accurate multivariate fit of stock index returns in terms of both linear and nonlinear dependence. An example of multi-asset option pricing emphasizes the importance of the proposed multivariate approach.

Review of dependence modeling in hydrology and water resources

2016 ◽  
Vol 40 (4) ◽  
pp. 549-578 ◽  
Author(s):  
Zengchao Hao ◽  
Vijay P. Singh
Keyword(s):  
Water Resources ◽  
Nonlinear Dependence ◽  
Future Research ◽  
Dependence Structure ◽  
Structure Modeling ◽  
Dependence Modeling ◽  
The Past ◽  
Extreme Dependence ◽  
Vine Copula ◽  
Hydrology And Water Resources

Various methods have been developed over the past five decades for dependence modeling of multivariate variables in hydrology and water resources, but there has been no overall review of techniques commonly used in the field. This paper, therefore, introduces several methods focusing on dependence structure modeling, including parametric distribution, entropy, copula, and nonparametric. Recent advances in modeling dependences mainly reside in nonlinear dependence modeling (including extreme dependence) with flexible marginal distributions, and in high-dimension dependence modeling via the vine copula construction with flexible dependence structures. Strengths and limitations of different methods and avenues for future research, such as dependence modeling in a changing climate, are discussed to aid water resource planners and managers in the selection and application of suitable techniques.

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