scholarly journals Modeling Conditional Dependence of Stock Returns Using a Copula-based GARCH Model

2017 ◽  
Vol 6 (2) ◽  
pp. 32
Author(s):  
Eun-Joo Lee ◽  
Noah Klumpe ◽  
Jonathan Vlk ◽  
Seung-Hwan Lee
Keyword(s):  
Stock Returns ◽  
Time Series Data ◽  
Financial Time Series ◽  
Garch Model ◽  
Series Data ◽  
Future Price ◽  
Investment Strategies ◽  
Volatility Clustering ◽  
Technology Stocks ◽  
Stock Portfolio

Investigating dependence structures of stocks that are related to one another should be an important consideration in managing a stock portfolio, among other investment strategies. To capture various dependence features, we employ copula to overcome the limitations of traditional linear correlations. Financial time series data is typically characterized by volatility clustering of returns that influences an estimate of a stock’s future price. To deal with the volatility and dependence of stock returns, this paper provides procedures of combining a copula with a GARCH model which leads to the construction of a multivariate distribution. Using the copula-based GARCH approach that describes the tail dependences of stock returns, we carry out Monte Carlo simulations to predict a company’s movements in the stock market. The procedures are illustrated in two technology stocks, Apple and Samsung.

scholarly journals Futures trading and the underlying stock volatility: A case of the FTSE/JSE TOP 40

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Mr. Rabson Magweva ◽  
Mrs. Magret Munyimi ◽  
Mr. Justine Mbudaya
Keyword(s):  
Time Series Data ◽  
Financial Time Series ◽  
Garch Model ◽  
Stock Index ◽  
Futures Contracts ◽  
Series Data ◽  
Policy And Practice ◽  
Stock Volatility ◽  
Top 40 ◽  
The Impact

Purpose: This study analyzed the impact of listing and trading futures contracts on the underlying stock index volatility behavior. The FTSE/JSE TOP 40 index was the index of interest.Methodology: To capture the non-constant variance of the residuals, a modified Generalised Autoregressive Conditionally Heteroscedasticity (GARCH) model was adopted. This model was used was adopted given that financial time series data exhibited ARCH effects. The GARCH model was estimated after dividing the sample period into pre-and post-futures eras.Findings: The research findings point towards stabilization effects on underlying stock volatility and refute the suggestion that futures markets improve the dissemination of information to the corresponding spot markets. On the same note, the introduction of futures increased the volatility persistence of index returns.Unique contribution to theory, policy, and practice: This paper applied a modified-GARCH by incorporating a dummy variable to the traditional GARCH model. The study used an emerging economy as a case study which makes the results and conclusions more specific and applicable. On the same note, the study covered the pre-and post-global crisis of 2007/8 in a Sub-Saharan nation. In practice, stock markets are encouraged to introduce futures contracts on highly volatile spot market assets.

scholarly journals Stock Returns And Volatility İn An Emerging Equity Market. Evidence From Kenya

2016 ◽  
Vol 12 (4) ◽  
pp. 79 ◽  
Author(s):  
David Ndwiga ◽  
Peter W Muriu
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Risk Premium ◽  
Time Series Data ◽  
Stock Exchange ◽  
Significant Risk ◽  
Series Data ◽  
Volatility Clustering ◽  
Cross Listing ◽  
Daily Returns

This study investigates volatility pattern of Kenyan stock market based on time series data which consists of daily closing prices of NSE Index for the period 2ndJanuary 2001 to 31st December 2014. The analysis has been done using both symmetric and asymmetric Generalized Autoregressive Conditional Heteroscedastic (GARCH) models. The study provides evidence for the existence of a positive and significant risk premium. Moreover, volatility shocks on daily returns at the stock market are transitory. We do not find any significant leverage effect. Introduction of the new regulations on foreign investors with a 25% minimum reserve of the issued share capital going to local investors (in 2002), introduction of live trading, cross listing in Uganda and Tanzania stock exchange (in 2006) and change in equity settlement cycle from T+4 to T+3 (in 2011) significantly reduce volatility clustering. The onset of US tapering increase the daily mean returns significantly while reducing conditional volatility.

scholarly journals Volatility Estimation Using Symmetric and Asymmetric Models in Oil Exporting Emerging Markets

2019 ◽  
Vol 11 (1) ◽  
pp. 41
Author(s):  
Latha Sreeram
Keyword(s):  
Size Effect ◽  
Emerging Markets ◽  
Stock Returns ◽  
Risk Premium ◽  
Time Series Data ◽  
Information Criterion ◽  
Good News ◽  
Series Data ◽  
Volatility Clustering ◽  
Egarch Model

The study empirically investigates the volatility pattern of thirteen emerging economies which are predominantly oil exporting countries. It is based on the time series data which consists of monthly closing price data of their index for a ten-year period from 01 January 2008 to 31 December 2017. Emerging markets are considered as investment destinations due to the presence of risk premium which has made the stock markets of these countries more volatile. Added to this is that these countries underwent crisis due to the sharp decline in crude oil prices as they were primarily dependent on oil exports. Hence it is a significant to study the volatility behavior of these countries.  The study has been done by employing both symmetric and asymmetric models of generalized autoregressive conditional heteroscedastic. As per Akaike Information Criterion (AIC), Log likelihood and Schwarz Information Criterion (SIC) the study provides evidence that GARCH (1,1) and TGARCH(1,1) estimations are found to be the most appropriate model that fits symmetric and asymmetric volatility respectively for all the thirteen countries. There was evidence of volatility clustering and leptokurtic in all the countries considered in the study. While EGARCH model revealed no support of existence of leverage on the stock returns, TGARCH supported existence of leverage in case of four countries. The tests for asymmetries in volatility indicate the size effect of the news, reaffirmed through the results of sign bias tests and news impact curves, which indicate that the size effect is stronger for bad news than the good news for countries which supported existence of leverage.

SPLINE ESTIMATION OF A SEMIPARAMETRIC GARCH MODEL

2015 ◽  
Vol 32 (4) ◽  
pp. 1023-1054 ◽  
Author(s):  
Rong Liu ◽  
Lijian Yang
Keyword(s):  
Time Series Data ◽  
Kernel Method ◽  
Financial Time Series ◽  
Garch Model ◽  
Real Data ◽  
Series Data ◽  
Link Function ◽  
Numerical Performance ◽  
Spline Estimation ◽  
Better Than

The semiparametric GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) model of Yang (2006, Journal of Econometrics 130, 365–384) has combined the flexibility of a nonparametric link function with the dependence on infinitely many past observations of the classic GARCH model. We propose a cubic spline procedure to estimate the unknown quantities in the semiparametric GARCH model that is intuitively appealing due to its simplicity. The theoretical properties of the procedure are the same as the kernel procedure, while simulated and real data examples show that the numerical performance is either better than or comparable to the kernel method. The new method is computationally much more efficient than the kernel method and very useful for analyzing large financial time series data.

scholarly journals Recent developments in volatility modeling and applications

2006 ◽  
Vol 2006 ◽  
pp. 1-23 ◽  
Author(s):  
A. Thavaneswaran ◽  
S. S. Appadoo ◽  
C. R. Bector
Keyword(s):  
Normal Distribution ◽  
High Frequency ◽  
Time Series Data ◽  
Financial Time Series ◽  
Empirical Studies ◽  
High Frequency Data ◽  
Series Data ◽  
Frequency Data ◽  
Arma Process ◽  
Volatility Clustering

In financial modeling, it has been constantly pointed out that volatility clustering and conditional nonnormality induced leptokurtosis observed in high frequency data. Financial time series data are not adequately modeled by normal distribution, and empirical evidence on the non-normality assumption is well documented in the financial literature (details are illustrated by Engle (1982) and Bollerslev (1986)). An ARMA representation has been used by Thavaneswaran et al., in 2005, to derive the kurtosis of the various class of GARCH models such as power GARCH, non-Gaussian GARCH, nonstationary and random coefficient GARCH. Several empirical studies have shown that mixture distributions are more likely to capture heteroskedasticity observed in high frequency data than normal distribution. In this paper, some results on moment properties are generalized to stationary ARMA process with GARCH errors. Application to volatility forecasts and option pricing are also discussed in some detail.

scholarly journals An Application of the Associate Hopfield Network for Pattern Matching in Chart Analysis

2021 ◽  
Vol 11 (9) ◽  
pp. 3876
Author(s):  
Weiming Mai ◽  
Raymond S. T. Lee
Keyword(s):  
Time Series ◽  
Pattern Matching ◽  
Time Series Data ◽  
Financial Time Series ◽  
Noisy Data ◽  
Hopfield Neural Network ◽  
Hopfield Network ◽  
Series Data ◽  
Financial Time ◽  
Chart Patterns

Chart patterns are significant for financial market behavior analysis. Lots of approaches have been proposed to detect specific patterns in financial time series data, most of them can be categorized as distance-based or training-based. In this paper, we applied a trainable continuous Hopfield Neural Network for financial time series pattern matching. The Perceptually Important Points (PIP) segmentation method is used as the data preprocessing procedure to reduce the fluctuation. We conducted a synthetic data experiment on both high-level noisy data and low-level noisy data. The result shows that our proposed method outperforms the Template Based (TB) and Euclidean Distance (ED) and has an advantage over Dynamic Time Warping (DTW) in terms of the processing time. That indicates the Hopfield network has a potential advantage over other distance-based matching methods.

scholarly journals Financial innovation and economic growth: evidence from Zimbabwe

2016 ◽  
Vol 13 (2) ◽  
pp. 65-75 ◽  
Author(s):  
Alex Bara ◽  
Calvin Mudzingiri
Keyword(s):  
Economic Growth ◽  
Developing Countries ◽  
Time Series Data ◽  
Financial Time Series ◽  
Financial Innovation ◽  
Dark Side ◽  
Series Data ◽  
Distributed Lag ◽  
Long Run ◽  
Causality Tests

The role of financial innovation on economic growth in developing countries has not been actively pursued. Stemming from the finance-growth nexus, literature suggests that financial innovation has a relationship to growth, which could be either positive or negative. Implicitly, financial innovation has a good and a dark side that affects growth. This study establishes the causal relationship between financial innovation and economic growth in Zimbabwe empirically. Using the Autoregressive Distributed Lag (ARDL) bounds tests and Granger causality tests on financial time series data of Zimbabwe for the period 1980-2013, the study finds that financial innovation has a relationship to economic growth that varies depending on the variable used to measure financial innovation. A long-run, growth-driven financial innovationis confirmed, with causality running from economic growth to financial innovation. Bi-directional causality also exists after conditionally netting-off financial development. Policies that enhance economic growth inter-twined with financial innovation are essential, if developing countries, such as Zimbabwe, aim to maximize economic development

scholarly journals The dynamic relationship between stock returns and trading volumes in Nepalese stock market

2020 ◽  
Vol 23 (2) ◽  
pp. 161-172
Author(s):  
Prem Lal Adhikari
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Trading Volume ◽  
Time Series Data ◽  
Stock Exchange ◽  
Causal Effect ◽  
Empirical Relationship ◽  
Insurance Companies ◽  
Series Data ◽  
Dynamic Relationship

 In finance, the relationship between stock returns and trading volume has been the subject of extensive research over the past years. The main motivation for these studies is the central role that trading volume plays in the pricing of financial assets when new information comes in. As being interrelated and interdependent subjects, a study regarding the trading volume and stock returns seem to be vital. It is a well-researched area in developed markets. However, very few pieces of literature are available regarding the Nepalese stock market that explores the association between trading volume and stock return. Realizing this fact, this paper aims to examine the empirical relationship between trading volume and stock returns in the Nepalese stock market using time series data. The study sample is comprised of 49 stocks traded on the Nepal Stock Exchange (NEPSE) from mid-July 2011 to mid-July 2018. This study examines the Granger Causality relationship between stock returns and trading volume using the bivariate VAR model used by de Medeiros and Van Doornik (2008). The study found that the overall Nepalese stock market does not have a causal relationship between trading volume and return on the stock. In the case of sector-wise study, there is a unidirectional causality running from trading volume to stock returns in commercial banks and stock returns to trading volume in finance companies, hydropower companies, and insurance companies. There is no indication of any causal effect in the development bank, hotel, and other sectors. This study also finds that there is no evidence of bidirectional causality relationships in any sector of the Nepalese stock market.

scholarly journals Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Md. Rabiul Islam ◽  
Md. Rashed-Al-Mahfuz ◽  
Shamim Ahmad ◽  
Md. Khademul Islam Molla
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Financial Time Series ◽  
Arma Model ◽  
Series Data ◽  
Discrete Wavelet ◽  
Multivariate Empirical Mode Decomposition ◽  
Financial Time ◽  
Mode Decomposition

This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD) is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA) model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the overall prediction. The ARMA model works better for stationary signal. With multiband representation, each subband becomes a band-limited (narrow band) signal and hence better prediction is achieved. The performance of the proposed MEMD-ARMA model is compared with classical EMD, discrete wavelet transform (DWT), and with full band ARMA model in terms of signal-to-noise ratio (SNR) and mean square error (MSE) between the original and predicted time series. The simulation results show that the MEMD-ARMA-based method performs better than the other methods.

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