DETECTING FOR SMOOTH STRUCTURAL CHANGES IN GARCH MODELS

2015 ◽  
Vol 32 (3) ◽  
pp. 740-791 ◽  
Author(s):  
Bin Chen ◽  
Yongmiao Hong
Keyword(s):  
Structural Breaks ◽  
Structural Changes ◽  
Garch Model ◽  
Parametric Bootstrap ◽  
Garch Models ◽  
Time Varying ◽  
Finite Samples ◽  
Time Varying Parameter ◽  
Break Points ◽  
Quasi Maximum Likelihood

Detecting and modeling structural changes in GARCH processes have attracted increasing attention in time series econometrics. In this paper, we propose a new approach to testing structural changes in GARCH models. The idea is to compare the log likelihood of a time-varying parameter GARCH model with that of a constant parameter GARCH model, where the time-varying GARCH parameters are estimated by a local quasi-maximum likelihood estimator (QMLE) and the constant GARCH parameters are estimated by a standard QMLE. The test does not require any prior information about the alternatives of structural changes. It has an asymptotic N(0,1) distribution under the null hypothesis of parameter constancy and is consistent against a vast class of smooth structural changes as well as abrupt structural breaks with possibly unknown break points. A consistent parametric bootstrap is employed to provide a reliable inference in finite samples and a simulation study highlights the merits of our test.

scholarly journals STRUCTURAL BREAKS DAN KETIDAKSTABILAN PERMINTAAN UANG DI INDONESIA

2018 ◽  
Vol 6 (2) ◽  
pp. 47-60
Author(s):  
Deviyantini Deviyantini ◽  
Iman Sugema ◽  
Tony Irawan
Keyword(s):  
Money Demand ◽  
Structural Breaks ◽  
Structural Changes ◽  
Demand Function ◽  
Time Series Data ◽  
Series Data ◽  
Time Varying ◽  
Economic Shocks ◽  
Money Demand Function ◽  
Time Varying Parameter

This research aims to identify the sources of instability of the money demand function (M1 and M2) due to structural changes that occur as a result of economic shocks. These shocks are technically shown by the presence of structural breaks in the data and can lead the parameters non-constancy. The instability of the money demand function was analyzed using the Gregory and Hansen test. The source of instability of the money demand was identified using time varying parameter model. This research used quarterly time series data from 1993Q1 to 2013Q4. The results show that the money demand function (M1 dan M2) is not cointegrated (unstable) and the source of the instability is exchange rate variable. Keywords: Stability money demand, Structural breaks, Time varying parameter model

scholarly journals STRUCTURAL BREAKS DAN KETIDAKSTABILAN PERMINTAAN UANG DI INDONESIA

2018 ◽  
Vol 6 (2) ◽  
pp. 47-60
Author(s):  
Deviyantini Deviyantini ◽  
Iman Sugema ◽  
Tony Irawan
Keyword(s):  
Money Demand ◽  
Structural Breaks ◽  
Structural Changes ◽  
Demand Function ◽  
Time Series Data ◽  
Series Data ◽  
Time Varying ◽  
Economic Shocks ◽  
Money Demand Function ◽  
Time Varying Parameter

This research aims to identify the sources of instability of the money demand function (M1 and M2) due to structural changes that occur as a result of economic shocks. These shocks are technically shown by the presence of structural breaks in the data and can lead the parameters non-constancy. The instability of the money demand function was analyzed using the Gregory and Hansen test. The source of instability of the money demand was identified using time varying parameter model. This research used quarterly time series data from 1993Q1 to 2013Q4. The results show that the money demand function (M1 dan M2) is not cointegrated (unstable) and the source of the instability is exchange rate variable. Keywords: Stability money demand, Structural breaks, Time varying parameter model

The Impact of Structural Breaks on Volatility Linkages between Asian Stock and Oil Futures Markets

2016 ◽  
Vol 24 (1) ◽  
pp. 31-64
Author(s):  
Sang Hoon Kang ◽  
Seong-Min Yoon
Keyword(s):  
Futures Markets ◽  
Structural Breaks ◽  
Global Financial Crisis ◽  
Garch Model ◽  
Time Varying ◽  
Oil Futures ◽  
Volatility Spillovers ◽  
Icss Algorithm ◽  
The Global Financial Crisis ◽  
The Impact

This paper investigates the impact of structural breaks on volatility spillovers between Asian stock markets (China, Hong Kong, India, Indonesia, Japan, Korea, Singapore, and Taiwan) and the oil futures market. To this end, we apply the bivariate DCC-GARCH model to weekly spot indices during the period 1998-2015. The results reveal significant volatility transmission for the pairs between the Asian stock and oil futures markets. Moreover, we find a significant variability in the time-varying conditional correlations between the considered markets during both bullish and bearish markets, particularly from early 2007 to the summer of 2008. Using the modified ICSS algorithm, we find several sudden changes in these markets with a common break date centred on September 15, 2008. This date corresponds to the collapse of Lehman Brothers which is considered as our breakpoint to define the global financial crisis. Also, we analyse the optimal portfolio weights and time-varying hedge ratios based on the estimates of the multivariate DCC-GARCH model. The results emphasize the importance of overweighting optimal portfolios between Asian stock and the oil futures markets.

scholarly journals Structural Breaks dan Ketidakstabilan Permintaan Uang di Indonesia

2017 ◽  
Vol 17 (2) ◽  
pp. 169-183
Author(s):  
Deviyantini Deviyantini ◽  
Iman Sugema ◽  
Tony Irawan
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Interest Rate ◽  
Money Demand ◽  
Structural Breaks ◽  
Demand Function ◽  
Series Data ◽  
Time Varying ◽  
Money Demand Function ◽  
Time Varying Parameter

Structural Breaks and Instability of Money Demand in IndonesiaThis research aims to identify the sources of instability of the money demand function (M1 and M2) due to structural changes that occur as a result of economic shocks. These shocks, are technically shown by the presence of structural breaks in the data and can lead the parameters non-constancy. The instability of the money demand function was analyzed using the Gregory and Hansen test. The source of instability of the money demand was identified using time varying parameter model. This research used quarterly time series data from 1993Q1 to 2013Q4. The result of Gregory and Hansen test indicates there is no long term equilibrium between variables (money demand, income, domestic interest rate, foreign interest rate, exchange rate, and inflation) in the model, neither M1 nor M2 model. On the other word, money demand function is unstable. The source of the instability is exchange rate variable.Keywords: Stability Money Demand; Structural Breaks; Time Varying Parameter ModelAbstrakPenelitian ini bertujuan untuk mengidentifikasi sumber-sumber ketidakstabilan fungsi permintaan uang (M1 dan M2) akibat dari perubahan struktural yang terjadi karena adanya guncangan ekonomi. Guncangan tersebut, yang secara teknis ditunjukkan oleh keberadaan structural breaks di dalam data, dapat menyebabkan parameter menjadi tidak konstan. Ketidakstabilan fungsi permintaan uang dianalisis dengan menggunakan Gregory and Hansen test. Sumber ketidakstabilan dari permintaan uang diidentifikasi dengan menggunakan time varying parameter model. Penelitian ini menggunakan data time series dalam bentuk kuartalan dari 1993Q1 sampai 2013Q4. Hasil Gregory and Hansen test menunjukkan bahwa tidak ada keseimbangan jangka panjang di antara variabel-variabel (permintaan uang, pendapatan, suku bunga domestik, suku bunga luar negeri, nilai tukar, dan inflasi) di dalam model, baik pada model M1 maupun M2. Dengan kata lain, fungsi permintaan uang tidak stabil. Sumber ketidakstabilan tersebut berasal dari variabel nilai tukar.

scholarly journals Conventional and Islamic stock markets: What about financial performance?

2017 ◽  
Vol 5 (3) ◽  
pp. 45
Author(s):  
Ben Rejeb ◽  
Mongi Arfaoui
Keyword(s):  
Stock Markets ◽  
Financial Crises ◽  
Structural Break ◽  
Weak Efficiency ◽  
Subprime Crisis ◽  
Time Varying ◽  
Causality Test ◽  
Time Varying Parameter ◽  
Break Points ◽  
Harmful Effects

The main objective of this paper is to analyse the performance of both Islamic and conventional stock market indices, particularly during the financial subprime crisis period. For this purpose, we use updated data including the recent financial instability periods and a relevant methodology based on the time varying parameter model combined with a GARCH specification, a Granger non-causal test and a structural break points technique. The empirical results show that the weak efficiency hypothesis is relatively verified in the Islamic context than in the conventional one. Moreover, we can conclude that Islamic markets are not fully immunised against the effects of financial crises and the strong financial fragilities. The results of the Granger non-causality test suggest that the Islamic stock markets have succeeded to relatively escape important part of the last subprime crisis harmful effects. This may encourage investment in this type of markets and therefore allows the strengthening of economic growth.

scholarly journals Modeling seasonality: An extension of the HEGY approach in the presence of two structural breaks

2008 ◽  
Vol 55 (4) ◽  
pp. 465-484 ◽  
Author(s):  
Ozlem Tasseven
Keyword(s):  
Structural Breaks ◽  
Structural Changes ◽  
Unit Roots ◽  
Testing Procedure ◽  
Small Sample ◽  
Seasonal Unit Roots ◽  
Monte Carlo Experiments ◽  
Turkish Economy ◽  
Annual Frequency ◽  
Break Points

In this paper the HEGY testing procedure (Hylleberg et al. 1990) of analyzing seasonal unit roots is tried to be re-examined by allowing for seasonal mean shifts with exogenous break points. Using some Monte Carlo experiments the distribution of the HEGY and the extended HEGY tests for seasonal unit roots subject to mean shifts and the small sample behavior of the test statistics have been investigated. Based on an empirical analysis upon the conventional money demand relationships in the Turkish economy, our results indicate that seasonal unit roots appear for the GDP deflator, real M2 and the expected inflation variables while seasonal unit roots at annual frequency seem to be disappear for the real M1 balances when the possible structural changes in one or more seasons at 1994 and 2001 crisis years have been taken into account. .

Structural change and economic growth in India: a comparative study of Punjab

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anirban Sanyal ◽  
Nirvikar Singh
Keyword(s):  
Economic Growth ◽  
Structural Change ◽  
Structural Changes ◽  
Green Revolution ◽  
State Level ◽  
Productivity Change ◽  
Time Varying ◽  
Content Type ◽  
Panel Regressions ◽  
Time Varying Parameter

Purpose The Green Revolution transformed agriculture in the Indian State of Punjab, with positive spillovers to the rest of India, but recently the state’s economy has fallen dramatically in rankings of per capita state output. Understanding the trajectory of Punjab’s economy has important lessons for all of India. Economic development is typically associated with changes in economic structure, but Punjab has remained relatively reliant on agriculture rather than shifting economic activity to manufacturing and services, where productivity growth might be greater. Design/methodology/approach The authors empirically examine structural change in the Punjab economy in the context of structural change and economic growth across the States of India. The authors calculate structural change indices and map their pattern over time. The authors estimate panel regressions and time-varying parameter regressions, as well as performing productivity change decompositions into within-sector and structural changes. Findings Panel regressions and time-varying-coefficient regressions suggest a significant positive influence of structural change on state-level growth. In addition, growth positively affected structural change across India’s states. The relative lack of structural change in Punjab’s economy is implicated in its relatively poor recent growth performance. Comparisons with a handful of other states reinforce this conclusion: Punjab’s lack of economic diversification is a plausible explanation for its lagging economic performance. Originality/value This paper performs a novel empirical analysis of structural change and growth, simultaneously using three different approaches: panel regressions, time-varying parameter regressions and productivity decompositions. To the best of the authors’ knowledge, it is the only paper we are aware of that combines these three approaches.

scholarly journals Quasi-maximum likelihood estimator of Laplace (1, 1) for GARCH models

2017 ◽  
Vol 15 (1) ◽  
pp. 1539-1548
Author(s):  
Haiyan Xuan ◽  
Lixin Song ◽  
Muhammad Amin ◽  
Yongxia Shi
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Strong Consistency ◽  
Garch Model ◽  
Garch Models ◽  
Parameter Vector ◽  
Likelihood Estimator ◽  
Comparison Results ◽  
Quasi Maximum Likelihood ◽  
The Garch Model

Abstract This paper studies the quasi-maximum likelihood estimator (QMLE) for the generalized autoregressive conditional heteroscedastic (GARCH) model based on the Laplace (1,1) residuals. The QMLE is proposed to the parameter vector of the GARCH model with the Laplace (1,1) firstly. Under some certain conditions, the strong consistency and asymptotic normality of QMLE are then established. In what follows, a real example with Laplace and normal distribution is analyzed to evaluate the performance of the QMLE and some comparison results on the performance are given. In the end the proofs of some theorem are presented.

scholarly journals Forecasting of the Nigeria Stock Returns Volatility Using GARCH Models with Structural Breaks

2020 ◽  
pp. 39-50
Author(s):  
Yakubu Musa ◽  
Ibrahim Adamu ◽  
Nasiru Sani Dauran
Keyword(s):  
Stock Returns ◽  
Structural Breaks ◽  
Structural Changes ◽  
Mean Reversion ◽  
Garch Models ◽  
Reversion Rate ◽  
Continuous Increase ◽  
Dummy Variable ◽  
Student T Distribution ◽  
T Distribution

This study examines the stock returns series using Symmetric and Asymmetric GARCH models with structural breaks in the presence of some varying distribution assumptions. Volatility models of Symmetric GARCH (1,1), Asymmetric Power GARCH (1,1) and GJR-GARCH(1,1) models were considered in estimating and measuring shock persistence,  leverage effects and mean reversion rate with structural breaks considering dummy variable  for these structural changes and varying distributions . The skewed student-t distribution is considered best distribution for the models; moreover findings showed the high persistence of shock in returns series for the estimated models. However, when structural breaks were incorporated in the estimated models by including dummy variable in the conditional variance equations of all the models, there was significant reduction of shock persistence parameter and mean reversion rate.  The study found the GJR-GARCH (1,1) with skewed student-t distribution best fit the series. The volatility was forecasted for 12 months period using GJR-GARCH (1,1) model and the values are compared with the actual values and the results indicates a continuous increase in unconditional variance.

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