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 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 SKEW NORMAL AND SKEW STUDENT-T DISTRIBUTIONS ON GARCH(1,1) MODEL

2021 ◽  
Vol 14 (1) ◽  
pp. 21-32
Author(s):  
Didit Budi Nugroho ◽  
Agus Priyono ◽  
Bambang Susanto
Keyword(s):  
Heavy Tails ◽  
Time Series Data ◽  
Financial Time Series ◽  
Linear Method ◽  
Real Data ◽  
Series Data ◽  
Model Parameters ◽  
Ratio Test ◽  
T Distribution ◽  
Skew Normal

The Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) type models have become important tools in financial application since their ability to estimate the volatility of financial time series data. In the empirical financial literature, the presence of skewness and heavy-tails have impacts on how well the GARCH-type models able to capture the financial market volatility sufficiently. This study estimates the volatility of financial asset returns based on the GARCH(1,1) model assuming Skew Normal and Skew Student-t distributions for the returns errors. The models are applied to daily returns of FTSE100 and IBEX35 stock indices from January 2000 to December 2017. The model parameters are estimated by using the Generalized Reduced Gradient Non-Linear method in Excel’s Solver and also the Adaptive Random Walk Metropolis method implemented in Matlab. The estimation results from fitting the models to real data demonstrate that Excel’s Solver is a promising way for estimating the parameters of the GARCH(1,1) models with non-Normal distribution, indicated by the accuracy of the estimation of Excel’s Solver. The fitting performance of models is evaluated by using log-likelihood ratio test and it indicates that the GARCH(1,1) model with Skew Student-t distribution provides the best fitting, followed by Student-t, Skew-Normal, and Normal distributions.

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 Pemodelan Pengukuran Luas Panen Padi Nasional Menggunakan Generalized Autoregressive Conditional Heteroscedastic Model (GARCH)

2014 ◽  
Vol 33 (1) ◽  
pp. 17
Author(s):  
Teuku Achmad Iqbal ◽  
Kusman Sadik ◽  
I Made Sumertajaya
Keyword(s):  
Time Series Data ◽  
Garch Model ◽  
Series Data ◽  
Percentage Error ◽  
Garch Models ◽  
Absolute Deviation ◽  
Significant Coefficient ◽  
Asymmetric Garch ◽  
Heteroscedastic Model ◽  
Better Than

This study was aimed to build a model for the estimation of national harvested area of rice by incorporating element of variant heterogeneity and the influence of asymmetry factors on time series data using five types of GARCH models, namely: symmetric GARCH, exponential asymmetric GARCH, quadratic asymmetric GARCH, Threshold GARCH, and non-linear asymmetric GARCH. Those models were compared and evaluated, and then the best model was used to predict the accuracy of the national rice harvested area. The results showed that two types of GARCH had significant coefficient, indicating the validity of the model. Those models were symmetric GARCH and quadratic GARCH models. Based on the value of mean absolute percentage error (MAPE) for the twelve month periods ahead, quadratic GARCH model was better than the symmetric GARCH model. Furthermore, based on the value of mean absolute deviation (MAD) and mean square error (MSE), quadratic GARCH model also seemed to be a better model than symmetric GARCH model. The best model can be used to predict the harvested area in the subsequent year.

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 Monitoring Persistence Change in Heavy-Tailed Observations

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 936
Author(s):  
Dan Wang
Keyword(s):  
Kernel Method ◽  
Alternative Hypothesis ◽  
Null Distribution ◽  
Real Data ◽  
Ratio Test ◽  
Finite Sample ◽  
Test Statistic ◽  
Bootstrap Approximation ◽  
Heavy Tailed ◽  
Better Than

In this paper, a ratio test based on bootstrap approximation is proposed to detect the persistence change in heavy-tailed observations. This paper focuses on the symmetry testing problems of I(1)-to-I(0) and I(0)-to-I(1). On the basis of residual CUSUM, the test statistic is constructed in a ratio form. I prove the null distribution of the test statistic. The consistency under alternative hypothesis is also discussed. However, the null distribution of the test statistic contains an unknown tail index. To address this challenge, I present a bootstrap approximation method for determining the rejection region of this test. Simulation studies of artificial data are conducted to assess the finite sample performance, which shows that our method is better than the kernel method in all listed cases. The analysis of real data also demonstrates the excellent performance of this method.

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 Is Islamic Bank More Stable Than Conventional Bank? Evidence From Islamic Rural Banks in Indonesia

2021 ◽  
Vol 12 (2) ◽  
pp. 294
Author(s):  
Agus Widarjono ◽  
M. B. Hendrie Anto ◽  
Faaza Fakhrunnas
Keyword(s):  
Time Series ◽  
Financial Stability ◽  
Time Series Data ◽  
Series Data ◽  
Z Score ◽  
Profit Rate ◽  
Return On Assets ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Better Than

This study investigates whether Islamic rural banks perform better than conventional rural banks as their competitor in Indonesia. To measure Islamic rural banks' financial performance, we apply financial stability using Z-score and profitability using the return on assets. We use monthly time series data from January 2009 to December 2018. The dynamic regression of the Autoregressive Distributed Lag (ARDL) model is then employed. The results report that the Z-Score of Islamic rural banks is higher than the Z-Score of conventional rural banks. This finding shows that Islamic rural banks are less risky than conventional rural banks. However, the Islamic rural banks' financial stability is very vulnerable to changes in equity, output, and inflation than conventional rural banks. Although the Islamic rural banks' profit rate is lower compared to conventional rural banks, it is considered more stable. The profit of Islamic rural banks is affected by size, equity, domestic output, and inflation.

scholarly journals TESTING OF THE BLACK SCHOLES AND GARCH MODELS IN LQ45 USING LONG STRADDLE STRATEGY IN 2009-2018

2021 ◽  
Vol 22 (1) ◽  
pp. 30-39
Author(s):  
Riko Hendrawan ◽  
Anggadi Sasmito
Keyword(s):  
Time Series Data ◽  
Mean Squared Error ◽  
Series Data ◽  
Call Option ◽  
Due Date ◽  
Average Percentage ◽  
Put Option ◽  
Option Contract ◽  
Black Scholes ◽  
Better Than

The purpose of this study is to examine the implementation of option contracts using Black Scholes and GARCH on the LQ45 index using the long straddle strategy. This study uses time-series data as a time frame for conducting research, using a sample of closing price data for the LQ 45 daily index for 2009-2018. For the test the model, we used the secondary data of the closing stock price index from February 28, 2009 to March 31, 2019The results of this study are seen by comparing the average percentage value of Average Mean Squared Error (AMSE) of Black Scholes and GARCH with the application of a long straddle strategy, where the smaller the percentage value, the better the model will be. Within one month of option contract due date, Black Scholes is better than GARCH, with an error value on the call option of 2.77% and the put option of 1.56%. Within two months of option contract due date, GARCH is better than Black Scholes, with an error value on the call option of 8.12% and the put option of 4.00%. Within three months of option contract due date, Black Scholes is better than GARCH, with an error value on the call option of 12.38% and on the put option of 5.50%. The long straddle strategy in the LQ45 index only reached a maximum of 60% of possible profits, with an average of around 30% possible profits.

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