Comparison of linear and non-linear GARCH models for forecasting volatility of select emerging countries

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sudhi Sharma ◽  
Vaibhav Aggarwal ◽  
Miklesh Prasad Yadav
Keyword(s):  
Global Financial Crisis ◽  
Emerging Countries ◽  
Expected Returns ◽  
Garch Models ◽  
Conditional Heteroscedasticity ◽  
Content Type ◽  
Volatility Models ◽  
Non Linear ◽  
Forecasting Volatility ◽  
Autoregressive Conditional Heteroscedasticity

PurposeSeveral empirical studies have proven that emerging countries are attractive destinations for Foreign Institutional Investors (FIIs) because of high expected returns, weak market efficiency and high growth that make them attractive destination for diversification of funds. But higher expected returns come coupled with high risk arising from political and economic instability. This study aims to compare the linear (symmetric) and non-linear (asymmetric) Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models in forecasting the volatility of top five major emerging countries among E7, that is, China, India, Indonesia, Brazil and Mexico.Design/methodology/approachThe volatility of financial markets of five major emerging countries has been empirically investigated for a period of two decades from January 2000 to December 2019 using univariate volatility models including GARCH 1, 1, Exponential Generalized Autoregressive Conditional Heteroscedasticity (E-GARCH 1, 1) and Threshold Generalized Autoregressive Conditional Heteroscedasticity (T-GARCH-1, 1) models. Further, to examine time-varying volatility, the distinctions of structural break have been captured in view of the global financial crisis of 2008. Thus, the period under the study has been segregated into pre- and post-crisis, that is, January 2001–December 2008 and January 2009–December 2019, respectively.FindingsThe findings indicate that GARCH (1, 1) model is superior to non-linear GARCH models for forecasting volatility because the effect of leverage is insignificant. China has been considered as most volatile, whereas India is volatile but positively skewed and Indonesia is the least volatile country. The results can help investors in better international diversification of their portfolio and identifying best suitable hedging opportunities.Practical implicationsThis study can help investors to construct a more risk-adjusted returns international portfolio. Further, it adds to the scant literature available on the inconclusive debate on the choice of linear versus non-linear models to forecast market volatility.Originality/valueEarlier studies related to univariate volatility models are mostly applications of the models. Only few studies have considered the robustness while applying the models. However, none of the studies to the best of the authors’ searches have considered these models for identifying the diversification opportunity among the emerging countries. Hence, this study is able to derive diversification and hedging opportunities by applying wide ranges of the statistical applications and models, that is, descriptive, correlations and univariate volatility models. It makes the study more rigorous and unique compared to the previous literature.

Volatility dynamics of Malaysian listed property companies within the Asian public property markets by using a switching regime approach

2019 ◽  
Vol 25 (1) ◽  
pp. 5-39
Author(s):  
Mohammad Muzzammil Zekri ◽  
Muhammad Najib Razali
Keyword(s):  
Global Financial Crisis ◽  
Content Type ◽  
Public Property ◽  
Switching Regime ◽  
Property Companies ◽  
Egarch Model ◽  
Modelling Techniques ◽  
The Global Financial Crisis ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Property Markets

Purpose This paper aims to examine the dynamic of volatility of Malaysian listed property companies within pan-Asian public property markets based on different volatility perspective over the past 18 years, especially during the global financial crisis (GFC). Design/methodology/approach This study uses several statistical methods and formulas for analysing the dynamic of volatility of Malaysian listed property companies such as exponential generalised autoregressive conditional heteroscedasticity (EGARCH) and Markov-switching (MS) EGARCH. The MS-EGARCH model provides new insights on the volatility dynamics of Malaysian listed property companies compared to conventional volatility modelling techniques, particularly EGARCH. Additionally, this paper will analyse the volatility movement based on three different sub-periods such as pre-GFC, GFC and post-GFC. Findings The findings reveal that the markets perform differently under different volatility conditions. Moreover, the application of MS-EGARCH provides a different view on the volatility dynamics compared to the conventional EGARCH model, as MS-EGARCH provides more comprehensive findings, especially during extreme market conditions. Originality/value This study contributes to the literature on the dynamics of Malaysian listed property companies within pan-Asian countries, as the approach for assessing the volatility performance based on different volatility conditions is less explored by previous researchers.

scholarly journals A Two-Regime Markov-Switching GARCH Active Trading Algorithm for Coffee, Cocoa, and Sugar Futures

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 1001 ◽  
Author(s):  
Oscar V. De la Torre-Torres ◽  
Dora Aguilasocho-Montoya ◽  
María de la Cruz del Río-Rama
Keyword(s):  
Markov Switching ◽  
Garch Models ◽  
Arch Model ◽  
Conditional Heteroscedasticity ◽  
Arch Models ◽  
Trading Decisions ◽  
Portfolio Managers ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Potential Use ◽  
The U.S

In the present paper we tested the use of Markov-switching Generalized AutoRegressive Conditional Heteroscedasticity (MS-GARCH) models and their not generalized (MS-ARCH) version. This, for active trading decisions in the coffee, cocoa, and sugar future markets. With weekly data from 7 January 2000 to 3 April 2020, we simulated the performance that a futures’ trader would have had, had she used the next trading algorithm: To invest in the security if the probability of being in a distress regime is less or equal to 50% or to invest in the U.S. three-month Treasury bill otherwise. Our results suggest that the use of t-student Markov Switching Component ARCH Model (MS-ARCH) models is appropriate for active trading in the cocoa futures and the Gaussian MS-GARCH is appropriate for sugar. For the specific case of the coffee market, we did not find evidence in favor of the use of MS-GARCH models. This is so by the fact that the trading algorithm led to inaccurate trading signs. Our results are of potential use for futures’ position traders or portfolio managers who want a quantitative trading algorithm for active trading in these commodity futures.

scholarly journals ARCH and GARCH Models: Quasi-Likelihood and Asymptotic Quasi-Likelihood Approaches

2020 ◽  
Author(s):  
Raed Alzghool
Keyword(s):  
Exchange Rate ◽  
Kernel Estimation ◽  
Estimation Procedure ◽  
Conditional Variance ◽  
Estimation Methods ◽  
Garch Models ◽  
Unknown Parameters ◽  
Conditional Heteroscedasticity ◽  
Garch Processes ◽  
Autoregressive Conditional Heteroscedasticity

This chapter considers estimation of autoregressive conditional heteroscedasticity (ARCH) and the generalized autoregressive conditional heteroscedasticity (GARCH) models using quasi-likelihood (QL) and asymptotic quasi-likelihood (AQL) approaches. The QL and AQL estimation methods for the estimation of unknown parameters in ARCH and GARCH models are developed. Distribution assumptions are not required of ARCH and GARCH processes by QL method. Nevertheless, the QL technique assumes knowing the first two moments of the process. However, the AQL estimation procedure is suggested when the conditional variance of process is unknown. The AQL estimation substitutes the variance and covariance by kernel estimation in QL. Reports of simulation outcomes, numerical cases, and applications of the methods to daily exchange rate series and weekly prices’ changes of crude oil are presented.

scholarly journals Volatility clustering, risk-return relationship and asymmetric adjustment in the Finnish housing market

2020 ◽  
Vol 13 (4) ◽  
pp. 661-688 ◽  
Author(s):  
Josephine Dufitinema
Keyword(s):  
Housing Market ◽  
Portfolio Management ◽  
House Price ◽  
Significant Risk ◽  
Price Volatility ◽  
Asymmetric Adjustment ◽  
Conditional Heteroscedasticity ◽  
Content Type ◽  
Risk Return ◽  
Autoregressive Conditional Heteroscedasticity

Purpose The purpose of this paper is to examine whether the house prices in Finland share financial characteristics with assets such as stocks. The studied regions are 15 main regions in Finland over the period of 1988:Q1-2018:Q4. These regions are divided geographically into 45 cities and sub-areas according to their postcode numbers. The studied type of dwellings is apartments (block of flats) divided into one-room, two rooms and more than three rooms apartment types. Design/methodology/approach Both Ljung–Box and Lagrange multiplier tests are used to test for clustering effects (autoregressive conditional heteroscedasticity effects). For cities and sub-areas with significant clustering effects, the generalized autoregressive conditional heteroscedasticity (GARCH)-in-mean model is used to determine the potential impact that the conditional variance may have on returns. Moreover, the exponential GARCH model is used to examine the possibility of asymmetric effects of shocks on house price volatility. For each apartment type, individual models are estimated; enabling different house price dynamics, and variation of signs and magnitude of different effects across cities and sub-areas. Findings Results reveal that clustering effects exist in over half of the cities and sub-areas in all studied types of apartments. Moreover, mixed results on the sign of the significant risk-return relationship are observed across cities and sub-areas in all three apartment types. Furthermore, the evidence of the asymmetric impact of shocks on housing volatility is noted in almost all the cities and sub-areas housing markets. These studied volatility properties are further found to differ across cities and sub-areas, and by apartment types. Research limitations/implications The existence of these volatility patterns has essential implications, such as investment decision-making and portfolio management. The study outcomes will be used in a forecasting procedure of the volatility dynamics of the studied types of dwellings. The quality of the data limits the analysis and the results of the study. Originality/value To the best of the author’s knowledge, this is the first study that evaluates the volatility of the Finnish housing market in general, and by using data on both municipal and geographical level, particularly.

The International Financial Reporting Standards 9 financial instruments, information quality and stock returns in the modern technology era

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Walaa Wahid ElKelish
Keyword(s):  
Stock Returns ◽  
Financial Reporting ◽  
Information Quality ◽  
Positive Impact ◽  
Modern Technology ◽  
Emerging Countries ◽  
Content Type ◽  
Financial Reporting Standards ◽  
Non Linear ◽  
Ifrs 9

PurposeThis paper investigates the relationship between information quality and stock returns during the International Financial Reporting Standards (IFRS 9) pre-adoption announcements and examines the influence of modern technology on these relationships across 24 emerging countries.Design/methodology/approachThis paper conducts an event study using data obtained from the DataStream, Osiris, International Telecommunication Union (ITU) and the World Bank databases from 2009 to 2014. The non-linear generalized additive model (GAM) was implemented to test the study hypotheses.FindingsResults indicate a significant positive non-linear relationship between low information quality and stock returns during IFRS 9 pre-adoption announcements. This result implies that IFRS 9 announcements have a positive impact on corporations with low pre-adoption quality information. This result is also more pronounced in small rather than large corporations and financial rather than nonfinancial institutions. Furthermore, modern technology plays a significant decisive antecedent role, while industry type has a moderating effect on the relationship between information quality and stock returns. The codified legal system has a positive impact on stock returns across emerging countries.Research limitations/implicationsData unavailability in some emerging countries.Practical implicationsThe empirical evidence provides useful guidelines for corporate managers, investors, international accounting standard-setters and regulators to improve financial reporting practices.Originality/valueThis paper extends the work of Armstrong et al. (2010); Onali et al. (2017) by including the impact of non-linear relationships using GAM analysis and the role of modern technology across emerging countries.

A study on volatility and return spillover of exchange-traded funds and their benchmark indices in India

2019 ◽  
Vol 46 (1) ◽  
pp. 19-39 ◽  
Author(s):  
Buvanesh Chandrasekaran ◽  
Rajesh H. Acharya
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Autoregressive Moving Average ◽  
Exchange Traded Funds ◽  
Volatility Spillover ◽  
Conditional Heteroscedasticity ◽  
Content Type ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Practical Implications

Purpose The purpose of this paper is to empirically examine the volatility and return spillover between exchange-traded funds (ETFs) and their respective benchmark indices in India. The paper uses time series data which consist of equity ETF and respective index returns. Design/methodology/approach The study uses autoregressive moving average–generalized autoregressive conditional heteroscedasticity and autoregressive moving average–exponential generalized autoregressive conditional heteroscedasticity models. The study uses data from the inception date of each ETF to December 2016. Findings The findings of the paper confirm that there is unidirectional return spillover from the benchmark index to ETF returns in most of the ETFs. Furthermore, ETF and benchmark index return have volatility persistence and show the presence of asymmetric volatility wherein a negative news has more influence on volatility compared to a positive news. Finally, unlike unidirectional return spillover, there is a bidirectional volatility spillover between ETF and benchmark index return. Practical implications The study has several practical implications for investors and regulators. A positive daily mean return over a fairly long period of time indicates that the passive equity ETFs can be a viable long-term investment option for ordinary investors. A bidirectional volatility spillover between the ETFs and benchmark index returns calls for the attention of the market regulators to examine the reasons for the same. Originality/value ETFs have seen fast growth in the Indian market in recent years. The present study considers the longest period data possible.

Long memory volatility in Asian stock markets

2017 ◽  
Vol 29 (3) ◽  
pp. 423-442 ◽  
Author(s):  
Geeta Duppati ◽  
Anoop S. Kumar ◽  
Frank Scrimgeour ◽  
Leon Li
Keyword(s):  
Long Memory ◽  
Moving Average ◽  
Realized Volatility ◽  
Theory And Practice ◽  
Long Term Memory ◽  
Conditional Heteroscedasticity ◽  
Term Memory ◽  
Content Type ◽  
Autoregressive Conditional Heteroscedasticity

Purpose The purpose of this paper is to assess to what extent intraday data can explain and predict long-term memory. Design/methodology/approach This article analysed the presence of long-memory volatility in five Asian equity indices, namely, SENSEX, CNIA, NIKKEI225, KO11 and FTSTI, using five-min intraday return series from 05 January 2015 to 06 August 2015 using two approaches, i.e. conditional volatility and realized volatility, for forecasting long-term memory. It employs conditional-generalized autoregressive conditional heteroscedasticity (GARCH), i.e. autoregressive fractionally integrated moving average (ARFIMA)-FIGARCH model and ARFIMA-asymmetric power autoregressive conditional heteroscedasticity (APARCH) models, and unconditional volatility realized volatility using autoregressive integrated moving average (ARIMA) and ARFIMA in-sample forecasting models to estimate the persistence of the long-term memory. Findings Given the GARCH framework, the ARFIMA-APARCH long-memory model gave the better forecast results signifying the importance of accounting for asymmetric information when modelling volatility in a financial market. Using the unconditional realized volatility results from the Singapore and Indian markets, the ARIMA model outperforms the ARFIMA model in terms of forecast performance and provides reasonable forecasts. Practical implications The issue of long memory has important implications for the theory and practice of finance. It is well-known that accurate volatility forecasts are important in a variety of settings including option and other derivatives pricing, portfolio and risk management. Social implications It could be said that using long-memory augmented models would give better results to investors so that they could analyse the market trends in returns and volatility in a more accurate manner and reach at an informed decision. This is useful to minimize the risks. Originality/value This research enhances the literature by estimating the influence of intraday variables on daily volatility. This is one of very few studies that uses conditional GARCH framework models and unconditional realized volatility estimates for forecasting long-term memory. The authors find that the methods complement each other.

scholarly journals Energy Security in Danger? A Comparative Analysis of Oil and Copper Supply

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 560
Author(s):  
Maciej Mróz
Keyword(s):  
Comparative Analysis ◽  
Crude Oil ◽  
Energy Security ◽  
New Technologies ◽  
Methodological Approach ◽  
Price Stability ◽  
Garch Models ◽  
Security Of Supply ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Conditional Heteroscedasticity

This study aims to examine energy security in terms of crude oil and copper supply. While oil remains the leading energy commodity globally, copper is crucial for many new technologies, foremost for RES. Therefore, both oil and copper are extremely important for current and future energy security. This article contains a bivariate methodological approach to a comparative analysis of oil and copper supply: determining supply security with an Index of security of supply, and examines price stability with generalized autoregressive conditional heteroscedasticity (GARCH) models. This research provides evidence that there are many differences but also significant similarities between these two completely different commodities in terms of both supply security and price stability. Facing the future for RES, significant demand may cause a threat to energy security on a previously unknown scale. Therefore this instability, both supply- and price-related, appears to be the main threat to future energy security.

scholarly journals Do We Need Stochastic Volatility and Generalised Autoregressive Conditional Heteroscedasticity? Comparing Squared End-Of-Day Returns on FTSE

Risks ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 12 ◽  
Author(s):  
David E. Allen ◽  
Michael McAleer
Keyword(s):  
Stochastic Volatility ◽  
Ordinary Least Squares ◽  
Conditional Heteroscedasticity ◽  
Daily Data ◽  
Volatility Models ◽  
Tail Behaviour ◽  
Comparable Performance ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Daily Returns ◽  
Better Than

The paper examines the relative performance of Stochastic Volatility (SV) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH) (1,1) models fitted to ten years of daily data for FTSE. As a benchmark, we used the realized volatility (RV) of FTSE sampled at 5 min intervals taken from the Oxford Man Realised Library. Both models demonstrated comparable performance and were correlated to a similar extent with RV estimates when measured by ordinary least squares (OLS). However, a crude variant of Corsi’s (2009) Heterogeneous Autoregressive (HAR) model, applied to squared demeaned daily returns on FTSE, appeared to predict the daily RV of FTSE better than either of the two models. Quantile regressions suggest that all three methods capture tail behaviour similarly and adequately. This leads to the question of whether we need either of the two standard volatility models if the simple expedient of using lagged squared demeaned daily returns provides a better RV predictor, at least in the context of the sample.

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