Garch Models Research Papers - Academia.edu (original) (raw)

This paper proposes and compares portfolio selection models under the assumption that the portfolios of returns follow a GARCH type process. We compute the price/return distribution at some future time approximating the GARCH process with a Markov chain. We consider either a GARCH(1,1) model or an asymmetric GARCH type model (E-GARCH, GJR-GARCH) . We present an expost comparison of portfolio selection strategies applied to some assets of the US Market. Since the optimization problems present more local optima, we implement an heuristic algorithm for the global optimum in order to overcome the intrinsic computational complexity of the models.

- by
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- Markov chains, Global Optimization, Garch Models, Heuristic

This paper studies time-varying market efficiency in twenty emerging and five developed equity markets for the period covering from January 2000 to June 2017 by using daily returns from MSCI indices and draws a comparison between emerging and developed market efficiency. I used four methods to test for efficiency in emerging market, GARCH-M (1,1) model (Generalised autoregressive conditional heteroscedasticity in mean) to test degree of volatility over the time, Autocorrelation test to test for correlation between two values, Runs test to test for randomness of values and Long memory by using rolling regression estimates. The results from these tests show that, most of the emerging markets are inefficient in weak form and developed markets are efficient in weak form. Some of the emerging markets like Brazil, India, China, Taiwan, Korea and Turkey show significant improvement in recent years. Comparison between emerging and developed market reveals that, information flows efficiently and symmetrically in developed markets compare to emerging markets. In addition, I observed that subprime crisis had a huge impact on emerging markets and created economic imbalance around the world.

Volatility in the stock return is an integral part of stock market with the alternating bull and bear phases. In the bullish market, the share prices soar high and in the bearish market share prices fall down and these ups and downs determine the return and volatility of the stock market. Volatility is a symptom of a highly liquid stock market. Volatility of returns in financial markets can be a major stumbling block for attracting investment. In this study, we use the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to model volatility. The analysis was done using a time series data for the period 1st January 2008 to I0th April 2012 on 18 banks in India and empirical findings revealed that all banks stock return series reports an evidence of time varying volatility which exhibits clustering and high persistence.

- by Ekta Rajliwal
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- Finance, Accounting, Volatility (Financial Econometrics), Volatility

The aim of this paper was to accurately and efficiently forecast from multivariate generalized autoregressive conditional heteroscedastic models. The Rotated Dynamic Conditional Correlation (RDCC) model with the Normal, Student’s-t and Multivariate Exponential Power distributions for errors were used to account for heavy tails commonly observed in financial time series data. The daily stock price data of Karachi, Bombay, Kuala Lumpur and Singapore stock exchanges from January 2008 to December 2017 were used. The predictive capability of RDCC models, with various error distributions, in forecasting one-day-ahead Value-at-Risk (VaR) was assessed by several back-testing procedures. The empirical results of the study revealed that the RDCC model with Student’s-t distribution produced more accurate and reliable risk forecasts than other competing models. To cite this article [Farid, S. & Iqbal, F. (2020). Forecasting Value-at-Risk of Asian Stock Markets Using the RDCC-GARCH Model Under D...

- by Farhat Iqbal
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- Volatility (Financial Econometrics), Volatility, Value at Risk, Garch Models

The estimation of inflation volatility is important to Central Banks as it guides their policy initiatives for achieving and maintaining price stability. This paper employs three models from the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) family with a view to providing a parsimonious approximation to the dynamics of Nigeria’s inflation volatility between 1996 and 2011. Of the competing models, the asymmetric TGARCH (1,1) provides an appropriate paradigm for explaining the dynamics of headline and core CPI volatilities in Nigeria, while the symmetric GARCH (1,1) was found to be adequate for food CPI. The results are quite revealing. Firstly, model outcomes indicate high persistence parameters for the core and food CPI, implying that the impacts of inflation shocks on their volatilities die away very slowly. However, the impact of inflation shocks on headline volatility die out rather quickly. Secondly, substantial evidence of asymmetric effect was found for bot...

Modeling and forecasting the volatility of Brazilian asset returns: a realized

- by Marcelo Medeiros
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- Risk Management, Risk Analysis, Volatility Forecasting, Value at Risk
- by jimmy jones
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- Applied Mathematics, Banking, Volatility Forecasting, Market efficiency

This brief focuses on risk management of a portfolio within the Crédit Agricole Group of Morocco.
The purpose of this project is to manage a portfolio of stocks based on micro-macro economic rations.
The loss of our portfolio is estimated by using the parametric VaR and Monte Carlo,that we have
implemented on VBA.
Then, we calculated the price of an option, using the Monte Carlo simulation and Black & Scholes
model,on the most dominant value in our portfolio. All with estimating volatility by GARCH(1.1 )
model .
Keywords : Value at risk, option, volatility, VBA, portfolio, GARCH (1.1), Monte Carlo, Black &
Scholes.

- by ABDELLAH TAHIRI
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- Applied Mathematics, Statistics, Portfolio Management, Probability and statistics

In European countries, the last decade has been characterized by a deregulation of power production and electricity became a commodity exchanged in proper markets. This resulted in an increasing interest of the scientific community on electricity exchanges for modeling both market activity and price process. This paper analyzes electricity spot-prices of the Italian Power Exchange (IPEX) and proposes three different methods to model prices time series: a discrete-time univariate econometric model (ARMA-GARCH) and two computational-intelligence techniques (Neural Network and Support Vector Machine). Price series exhibit a strong daily seasonality, addressed by analyzing separately a series for each of the 24 hours. One-day ahead forecasts of hourly prices have been considered so to compare the prediction performances of three different methods, with respect to the canonical benchmark model based on the random walk hypothesis. Results point out that Support Vector Machine methodology gives better forecasting accuracy for price time series, closely followed by the econometric technique.

- by Giulia Gallo
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- Support Vector Machines, Artificial Neural Networks, GARCH, Electricity prices

The study aims to extend the GARCH type volatility models to their nonlinear TAR (Tong, 1990) and STAR-based (Terasvirta, 1994) counter parts where both the conditional mean and the conditional variance processes follow TAR and STAR nonlinearity. The paper further investigates the models under their fractional integration and asymmetric power variants. The STAR-based models are LSTARLST- GARCH, LSTAR-LST-FIGARCH, LSTAR-LST-FIPGARCH and LSTAR-LSTFIAPGARCH models, which may be easily applied to model and forecast various financial time series. In the empirical section, an application is provided to model the daily returns in WTI crude oil prices considering the regime shifts the crude oil prices were subject to during history. Models are evaluated in terms of their out-of-sample forecasting capabilities with equal forecast accuracy tests and also in terms of various error criteria. The results suggest that volatility clustering, asymmetry and nonlinearity characteristics are modeled m...

- by ÖZGÜR ERSİN
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- Economics, Volatility, GARCH, Garch Models

Return is the major attribute of an investment asset that can be considered as a random variable. The variability in return can be expressed as volatility. Forecasting volatility and modelling are the most prolific areas for the research. Volatility and Leverage effect are the two crucial stipulations to study market contradictions and trends that prevail for a drawn-out period. It is observed that when volatility beams the markets soar and when markets roar the volatility fades away. Leverage has a larger scope in managing volatility when investors tend to shuffle their positions. This literature aims to identify the volatility clustering and leverage effect caused to NSE NIFTY 50 index. The study contrasts volatility clustering using symmetric model of i.e., GARCH (1,1). Leverage effects is studied and compared using TGARCH and EGARCH models.

- by Publishing India Group and +1
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- Clustering, Garch Models

This study aims to highlight the importance of GARCH models in the volatility modeling and forecasting as a mechanism for crisis management and early warning. After presenting the theoretical background of the models have been applied at the level of nine Arab stock exchanges indicators, namely: Abu Dhabi, Bahrain, Dubai, Egypt, Kuwait, Morocco, Oman, Qatar and Saudi Arabia, using daily data between 2007 and 2012 (1304 daily observation). The study concluded there is the problem of Heteroskedasticity and continuity in shock in light of the crisis, which imposes the use of GARCH models.

- by Ali Bendob
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- Financial Crisis of 2008/2009, Garch Models

It is crucial to model, quantify and understand the variables and dynamics that underlie the well-known extreme behaviour of spot electricity prices in wholesale markets. We explicitly model the conditional volatility and skewness of electricity prices. A GARCH-type model allowing for time-varying volatility and skewness, which is estimated assuming a Gram-Charlier expansion of the normal density function, is presented. This model is applied to data from Pennsylvania- New Jersey, NordPool and Victoria (Australia) markets. We document the existence of a rich structure in the conditional skewness of spot prices and we show the relationship between skewness and demand-supply related variables.

- by Pablo Villaplana
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- Spain, Garch Models, Conditional Volatility

El presente Trabajo Fin de Master plantea como objetivo la aplicación práctica del modelo GARCH y distribuciones de colas anchas en el cálculo del riesgo de mercado, mediante metodología VeR Paramétrica, en una cartera de renta variable en el periodo de 1/1/2014 al 31/12/2018 para los principales títulos del sector bancario y financiero que cotizan en el mercado bursátil español y que pertenecen al IBEX-35. Se escogen los valores cotizados de Santander, BBVA, CaixaBank, Sabadell y Bankia. Se realiza metodología empírica mediante técnicas matemáticas, estadísticas y econométricas. Se comparan resultados obtenidos entre distribución normal y distribución T-Student (colas anchas), y según la forma de cálculo de la volatilidad (Estándar o GARCH). Finalmente se realiza el Backtesting en el periodo de 1/1/2019 al 30/6/2019 para los resultados de la cartera y se establecen las conclusiones finales del estudio.

This study is a comparative investigation in the field of GARCH estimation. Short and long term market risk components are estimated based on financial market indices of eight countries including members of the advanced G10 (United Nations, Japan, Germany, Canada) and members of the recently emerging (Brazil, India, China, Turkey) group respectively with the final aim to detect country specific patterns of volatility as well as differences between mature and emerging markets. Six models of conditional volatility including GARCH, ApARCH, T-GARCH, GJR-GARCH, E-GARCH and CGARCH as well as their semi-parametric extensions and the SPLINE-GARCH model are introduced and applied to the data respectively. Results provide supportive evidence that long term volatility is a time variant oscillatory evolving function and that the net impact on short term conditional volatility depends either on sign and size of shocks. Differences in exposure, magnitude of shocks and persistence of short term volatility are derived via comparison of the GARCH coefficients. While volatility appears to be more persistent in the mature market case, emerging market countries display higher systematic risk levels. The impact of the recent Global Financial Crisis on long and short term volatility components, in turn, is significantly higher for the mature market cases while emerging markets albeit display contagion effect but are more susceptible to region or country specific shocks. The results are in consent with the volatility co-movement hypothesis and constitute a starting point for extension to mutlivariate grounds

L’obiettivo del progetto di tesi è quello di mostrare gli aspetti teorici necessari alla realizzazione di una piattaforma web-based di supporto agli investimenti finanziari, attraverso la messa a disposizione di una serie di strumenti... more

- by Giorgio Bella
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- Web Applications, Garch Models

Je voudrais tout d'abord remercier Laurence BROZE et Jean-Michel ZAKOÏAN d'avoir accepté la direction de ma thèse. Ils m'ont guidé durant celle-ci et m'ont apporté le soutien nécessaire. De part leurs qualités pédagogiques, leurs précieux conseils, leurs stimulants encouragements et leur disponibilité, j'ai pu mener à bien ce travail dans des conditions favorables. Egalement, ils m'ont permis d'approfondir mes connaissances lors de différentes rencontres scientifiques en France et en Belgique (CORE) qu'ils ont organisées. Bien évidemment, je remercie Michel CARBON et Michael ROCKINGER de l'honneur qu'ils me font d'être les rapporteurs de cette thèse, du temps et du soin qu'ils ont apporté à la rédaction de leurs rapports. Mes remerciements vont aussi à Christian FRANC& d'avoir accepté la présidence du jury. Je tiens à remercier Olivier TORRÈS, pour sa disponibilité, ses qualités humaines et avec qui j'ai eu des discussions fructueuses. Il m'a aidé et encouragé. Mille mercis à Luc CHAMPARNAUD, Jérôme FONCEL, Frédéric JOUNEAU-SION, Ouafae BENRABAH, Elias OULD-SAID pour leurs encouragements et leur amitié. Un grand merci à tous les membres de GREMARS de Lille 3 pour leur accueil chaleureux. Je tiens à exprimer ma reconnaissance à toute ma famille, mes amis et tous ceux qui m'ont aidé de près et de loin.

- by SAIDI Youssef
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- Nonlinear dynamics, Financial Markets, Regime change, Garch Models

Volatility in financial markets, particularly stock exchange markets, is an important issue that concerns theorists
and practitioners. Over the past 30 years, there has been a vast literature for modeling the temporal dependencies
in volatility of financial markets. Also, more recently researches have been examining the asymmetry and
non-linear properties in variance of financial assets, rather than the conditional mean. In this study, a
comprehensive empirical analysis of the mean return and conditional variance of Turkish Financial Markets is
performed by using various GARCH models. CGARCH and TGARCH appear to be superior for modeling the
volatility of financial instruments in Turkey during the years 2002–2014. It is also found that return series of all
markets include; leptokurtosis, asymmetry, volatility clustering, and long memory.
Keywords: asymmetric GARCH, volatility, financial markets, forecasting, BIST

- by Mehmet Pekkaya
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- Finance, Time series Econometrics, Garch Models
- by mihir dash
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- Economics, Garch Models, Vanilla Options Pricing

The aim of this chapter is to provide a detailed empirical example of autoregressive conditional heteroskedasticity (ARCH) model and selected generalized ARCH models. Before the ARCH/GARCH models are estimated, several calculations and tests should be done. The mean model is determined using the autocorrelation function and partial autocorrelation function and also the unit root test. The existence of ARCH effect is tested using ARCH-LM test. After these steps are done, then ARCH/ GARCH models can be estimated. All these theoretical aspects are applied to Sofia Stock Indexes (SOFIX) using EViews 9 software package. The windows and output of EViews are presented. To show the output's academic writing format researchers' outputs are presented in a table.

- by Erginbay Uğurlu
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- GARCH, Garch Models, Volatility Modeling and Forecasting

The aim and objective of this study is to model and forecast the stock index volatility of Shanghai and Shenzhen index. The volatility is estimated by employing three GARCH-type models standard GARCH, GJR-GARCH and the EGARCH. Their performance are compared based on three statistical errors, the mean absolute error, root mean squared and mean absolute percentage and then by the Mincer-Zarnowitz regression. The EGARCH outperforms its counterparts in all three statistical errors and by the regression based analysis. These empirical results are of important significance to portfolio management and risk management processes.

- by Alero Arubi
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- Finance, Economics, Development Economics, Chinese Studies

The importance of volatility for all market participants has led to the development and application of various econometric models. The most popular models in modelling volatility are GARCH type models because they can account excess kurtosis and asymmetric effects of financial time series. Since standard GARCH(1,1) model usually indicate high persistence in the conditional variance, the empirical researches turned to GJR-GARCH model and reveal its superiority in fitting the asymmetric heteroscedasticity in the data. In order to capture both asymmetry and nonlinearity in data, the goal of this paper is to develop a parsimonious NN model as an extension to GJR-GARCH model and to determine if GJR-GARCH-NN outperforms the GJR-GARCH model.

- by Josip Arnerić and +1
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- Neural Networks, Emerging Markets, Garch Models, Conditional Volatility

Using data from 35 futures options markets from eight separate exchanges, we test how well the implied volatilities (IVs) embedded in option prices predict subsequently realized volatility (RV) in the underlying futures. We find that for this broad array of futures options, IV performs well in a relative sense. For a large majority of the commodities studied, the implieds outperform historical volatility (HV) as a predictor of the subsequently RV in the underlying futures prices over the remaining life of the option. Indeed, in most markets examined, regardless of whether it is modeled as a simple moving average or in a GARCH framework, HV contains no economically significant predictive information beyond what is already incorporated in IV. These findings add to previous research that has focused on currency and crude oil futures by extending the analysis into a very broad array of contracts and exchanges. Our results are consistent with the hypothesis that futures options markets in general, with their minimal trading frictions, are efficient.

- by Andrew Szakmary
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- Applied Mathematics, Banking, Volatility Forecasting, Market efficiency

Financial asset returns are known to be conditionally heteroskedastic and generally non-normally distributed, fat-tailed and often skewed. These features must be taken into account to produce accurate forecasts of Value-at-Risk (VaR). We provide a comprehensive look at the problem by considering the impact that different distributional assumptions have on the accuracy of both univariate and multivariate GARCH models in out-of-sample VaR prediction. The set of analyzed distributions comprises the normal, Student, Multivariate Exponential Power and their corresponding skewed counterparts. The accuracy of the VaR forecasts is assessed by implementing standard statistical backtesting procedures used to rank the different specifications. The results show the importance of allowing for heavy-tails and skewness in the distributional assumption with the skew-Student outperforming the others across all tests and confidence levels. Econometrics 2016, 4, 3 2 of 27

- by Manuela Braione and +1
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- Portfolio Management, Value at Risk, Garch Models

The generalized autoregressive conditional heteroskedasticity (GARCH) type models are used to investigate the volatility of Bangladesh stock market. The findings of the study demonstrate that the index volatility characteristics changes over time. The article shows that the data are divided into three sub-periods: pre crisis, crisis, and post crisis. Accordingly, the results of the findings indicate changes in the GARCH-type models parameter, risk premium and persistence of volatility in different periods. A significant " low-yield associated with high-risk " phenomenon is detected in the crisis period and the " leverage effect " occurs in each periods. The investors are irrational which is based on assumption of risk and return characteristics of assets. Consequently, the market is not as mature as developed market. It is found in the article that the threshold generalized autoregressive conditional heteroskedasticity (TGARCH) model is more accurate for the model accuracy. Additionally, statistic error measurements indicate that GARCH model is more efficient than others and it has also more forecasting ability.

- by Roni Bhowmik, PhD and +2
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- Garch Models, Stock Market Volatility, Dhaka Stock Exchange

This paper explores the relationship between volume and volatility in the Australian Stock Market in the context of a generalized autoregressive conditional heteroskedasticity (GARCH) model. In contrast to other studies who only examine the interaction of GARCH and volume effects on a small number of stocks, we examine these effects on the entire available data for the Australian All Ordinaries Index. We also emphasize on the impact of firm size and trading volume. Our results indicate that GARCH model testing and estimation is impacted by firm size and trading volume. Specifically, our analysis produces the following major findings. First, generally, daily trading volume, used as a proxy for information arrival time, is shown to have significant explanatory power regarding the variance of daily returns. Second, the actively traded stocks which may have a larger number of information arrivals per day have a larger impact of volume on the variance of daily returns. Third, we find that low trading volume and small firm lead to a higher persistence of GARCH effects in the estimated models. Fourth, unlike the elimination effect for the top most active stocks, in general, the elimination of both autoregressive conditional heteroskedasticity (ARCH) and GARCH effects by introducing the volume variable on all other stocks on average is not as much as that for the top most active stocks. Fifth, the elimination of both ARCH and GARCH effects by introducing the volume variable is higher for stocks in the largest volume and/or the largest market capitalization quartile group. Our findings imply that the earlier findings in the literature were not a statistical fluke and that, unlike most anomalies, the volume effect on volatility is not likely to be eliminated after its discovery. In addition, our findings reject the pure random walk hypothesis for stock returns.

- by Wing-keung Wong
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- Volatility, Volume, Garch Models

We consider the estimation of a random level shift model for which the series of interest is the sum of a short memory process and a jump or level shift component. For the latter component, we specify the commonly used simple mixture model such that the component is the cumulative sum of a process which is 0 with some probability (1−α) and is a random variable with probability α. Our estimation method transforms such a model into a linear state space with mixture of normal innovations, so that an extension of Kalman filter algorithm can be applied. We apply this random level shift model to the logarithm of absolute returns for the S&P 500, AMEX, Dow Jones and NASDAQ stock market return indices. Our point estimates imply few level shifts for all series. But once these are taken into account, there is little evidence of serial correlation in the remaining noise and, hence, no evidence of long-memory. Once the estimated shifts are introduced to a standard GARCH model applied to the returns series, any evidence of GARCH effects disappears. We also produce rolling out-ofsample forecasts of squared returns. In most cases, our simple random level shifts model clearly outperforms a standard GARCH(1,1) model and, in many cases, it also provides better forecasts than a fractionally integrated GARCH model. JEL Classification Number: C22.

- by Pierre Perron
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- Econometrics, Empirical Finance, Forecasting, Kalman Filter

Nosúltimos anos, o mercado de dólar comercial experimentou grande volatilidade no Brasil. Os episódios de alta forte e rápida do dólar comercial provocaram prejuízos para diversas empresas brasileiras com dívida nesta moeda. A queda do dólar entre 2002 e 2008 provocou perdas para o setor exportador. Nesse contexto, o objetivo deste estudo foi examinar a efetividade do Hedge no mercado futuro de dólar comercial, negociado na BM&FBovespa, no período de dezembro de 2001 a fevereiro de 2009. A determinação da razão de Hedge foi feita de quatro maneiras alternativas: a) ingênua ou 1-1, na qual o Hedger toma uma posição totalmente inversaà sua posiçãoà vista; b) MQO-Mínimos Quadrados Ordinários; c) GARCH (Generalized Autoregressive Conditional Heteroscedasticity) Simétrico Bivariado; d) GARCH Assimétrico Bivariado. Os resultados do estudo mostraram que houve uma melhoria significante na efetividade do hedge utilizando modelos GARCH em relaçãoàs estratégias ingênuas e MQO.

- by Marcelo Cabus Klotzle
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- Garch Models

This paper aims to measure the nature of volatility in the cryptocurrency
market before and during Covid-19 pandemic period. To achieve this goal, the Wald test, Granger Causality and Generalized Autoregressive Conditional Heteroskedasticity (1,1) have been applied considering the daily US dollar dominated closing prices of 15 leading cryptocurrencies and volatility index (VIX- CBOE) from 1 January, 2019 to 5 June, 2020. The presence of structural breaks in all the selected cryptocurrencies is observed which result in erroneous forecasting in cryptocurrency market. The small size of cryptocurrency market hinders the risk diversification. It is further noticed that cryptocurrencies are exposed to the systematic bubble risks and therefore it is very unpredictable. Inclusion of cryptocurrencies in the portfolio along with conventional
instruments like stocks, bonds, precious metals, commodities, and paper
currencies may gear up the overall return on investment and increase the possibility of risk diversification if necessary investment precautions are taken.

This paper investigates volatility spillovers in the stock market in Japan during the COVID-19 pandemic by using GARCH family models. The empirical analysis is focused on the dynamics of the NIKKEI 225 stock market index during the sample period from July 30, 1998, to January 24, 2022. In other words, the sample period covers both the period of the global financial crisis (GFC) and the COVID-19 pandemic. The econometrics includes GARCH (1,1), GJR (1,1), and EGARCH (1,1) models. By applying GARCH family models, this empirical study also examines the long-term behavior of the Japanese stock market. The Japanese stock market is much more stable and efficient than emerging or frontier markets characterized by higher volatility and lower liquidity. The paper establishes that NIKKEI 225 index dynamics is different in intensity in the case of the two most recent extreme events analyzed, namely the global financial crisis (GFC)of 2007-2008 and the COVID-19 pandemic. The findings confirmed the presence of the leverage effect during the sample period. Moreover, the empirical results identified the presence of high volatility in the sample returns of the selected stock market. Nevertheless, the econometric framework showed that the negative implications of the GFC were much more severe and caused more significant contractions compared to the COVID-19 pandemic for the Japanese stock market. This study contributes to the existing literature by providing additional empirical evidence on the long-term behavior of the stock market in Japan, especially in the context of extreme events.

Options are instruments which have the special property of limiting the downside risk, while not limiting the upside potential, thus their use in hedging. The share of the options market in the Indian capital market has increased to 64% in just over a decade. The trading turnover of options in the FY11 was Rs. 193,95,710 crore, and the trading volume generated by options market was almost two times that of the volume generated in the cash market and futures market put together. So trading and pricing of stock option have occupied an important place in the Indian derivatives market. Volatility is a critical factor influencing the option pricing; however, it is an extremely difficult factor to forecast. Hence the crucial problem lies with the accurate estimation of volatility. The estimated volatility can be used to determine future prices of the stock or the stock option. Empirical research has shown that using historical volatility in different option pricing models leads to pricing biases. The GARCH (1, 1) model can be a solution for this

- by mihir dash
- •
- Garch Models, Vanilla Options Pricing

This study introduces a state-of-the-art volatility forecasting method for container shipping freight rates. Over the last decade, the container shipping industry has become very unpredictable. The demolition of the shipping conferences system in 2008 for all trades calling at a port in the European Union (EU) and the global financial crisis in 2009 have affected the container shipping freight market adversely towards a depressive and non-stable market environment with heavily fluctuating freight rate movements. At the same time, the approaches of forecasting container freight rates using econometric and time series modelling have been rather limited. Therefore, in this paper, we discuss contemporary container freight rate dynamics in an attempt to forecast for the Far East to Northern Europe trade lane. Methodology-wise, we employ autoregressive integrated moving average (ARIMA) as well as the combination of ARIMA and autoregressive conditional eteroscedasticity (ARCH) model, which we call ARIMARCH. We observe that ARIMARCH model provides comparatively better results than the existing freight rate forecasting models while performing short-term forecasts on a weekly as well as monthly level. We also observe remarkable influence of recurrent general rate increases on the container freight rate volatility.

- by Ziaul H Munim
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- Artificial Intelligence, Forecasting, Shipping/ Transport Logistics, Shipping

Financial series tend to be characterized by volatility and this characteristic affects both financial series of developed markets and emerging markets. Because of the emerging markets have provided major investment opportunities in last decades their volatility has been widely investigated in the literature. The most popular volatility models are the Autoregressive Conditional Heteroscedastic (ARCH) or Generalized Autoregressive Conditional Heteroscedastic (GARCH) models. This paper aims to investigate the volatility of Bucharest Stock Exchange, BET index as an emerging capital market and compare forecasting power for volatility of this index during 2000-2014. To do this, this paper use GARCH, TARCH, EGARCH and PARCH models against Generalized Error distribution. We estimate these models then we compare the forecasting power of these GARCH type models in sample period. The results show that the EGARCH is the best model by means of forecasting performance.

We study performance of Islamic and conventional indices of the Gulf Cooperation Council (GCC) countries in the wake of financial crisis of 2008 and test whether Islamic indices were less risky than conventional indices. We make use of data of the six GCC markets as well as the Dow Jones Islamic Market Index GCC. The mean and variance of each of the indices are analyzed based on augmented GARCH models. The results show that the financial crisis impacted on the mean returns of Bahrain, the other indices remained unaffected. The financial crisis, however, impacted volatility in three GCC markets (Kuwait, Bahrain, and the UAE), while the impact on the remaining markets (Saudi Arabia, Oman, and Qatar) and the Islamic index was insignificant. More interestingly, we show that the Islamic index did not exhibit lower volatility than its conventional counterparts.

- by Héla Miniaoui
- •
- Financial Crisis, Garch Models, GCC, Islamic Investment

Forecasting models based on the assumption that returns are normally distributed do not perform sufficiently on shallow markets. These models are more likely to fail in the estimation of the extreme points that can be reached especially at high volatility markets, and this situation is led to investors in predicting volatility. In the volatility forecasting of crypto money, which is seen as an alternative investment tool for the financial investors, single volatility models such as, ARCH, GARCH, T-GARCH, GARCH-M, E-GARCH, and I-GARCH and long memory models (AP-GARCH and C-GARCH) was utilized. In addition, the most suitable model was tried to be tested among the models used for volatility estimation. In this context, the price data of Bitcoin, Ethereum and Ripple cryptocurrency with the highest market value in the crypto money market have been utilized between 24/08/2016-07/05/2018. According to the results of the research, for Bitcoin and Ethereum, the volatility effect of the shocks is permanent and the effect of the positive shocks is more than that of the negative shocks, whereas for Ripple, the volatility effect of the shocks is transient and the passivity of the volatility is short.

- by Ibrahim Korkmaz Kahraman and +1
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- Volatility, Bitcoin, Garch Models, Ripple

This survey reviews the existing literature on the most relevant Bayesian inference methods for univariate and multivariate GARCH models. The advantages and drawbacks of each procedure are outlined as well as the advantages of the Bayesian approach versus classical procedures. The paper makes emphasis on recent Bayesian non-parametric approaches for GARCH models that avoid imposing arbitrary parametric distributional assumptions. These novel approaches implicitly assume infinite mixture of Gaussian distributions on the standardized returns which have been shown to be more flexible and describe better the uncertainty about future volatilities. Finally, the survey presents an illustration using real data to show the flexibility and usefulness of the non-parametric approach.

- by Nour Ibrahime
- •
- Econometrics, Statistics, Machine Learning, Volatility

The study aims to extend the GARCH type volatility models to their nonlinear TAR (Tong, 1990) and STAR-based (Terasvirta, 1994) counter parts where both the conditional mean and the conditional variance processes follow TAR and STAR nonlinearity. The paper further investigates the models under their fractional integration and asymmetric power variants. The STAR-based models are LSTAR-LST-GARCH, LSTAR-LST-FIGARCH, LSTAR-LST-FIPGARCH and LSTAR-LST-FIAPGARCH models, which may be easily applied to model and forecast various financial time series. In the empirical section, an application is provided to model the daily returns in WTI crude oil prices considering the regime shifts the crude oil prices were subject to during history. Models are evaluated in terms of their out-of-sample forecasting capabilities with equal forecast accuracy tests and also in terms of various error criteria. The results suggest that volatility clustering, asymmetry and nonlinearity characteristics are modeled more efficiently as compared to their single regime variants, such as the GARCH, FIGARCH and FIAPGARCH models. Further, the outof-sample results suggest that the LSTAR-LST-FIAPGARCH model provides the best forecasting accuracy in terms of RMSE and MSE error criteria.

- by Özgür Ersin
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- GARCH, Garch Models, Nonlinear econometrics, Nonlinear Time series Models

This paper investigates the performance of various conditional volatility models to forecast the second moment of tanker freight rates. Justified by existing theoretical and empirical evidence, we focus on asymmetric Markov regime-switching models to study the major global routes for long-haul trade of crude oil during the sample period from June 2000 to May 2015. Moreover, in contrast to a number of existing studies, we examine seasonally adjusted freight rates. We find that regime-switching GARCH models outperform their single-regime complements in terms of in-sample fit and out-of-sample forecasting accuracy. In particular, the asymmetric MRS-EGARCH and MRS-APARCH exhibit superior in- and out-of-sample performance. To additionally examine the applicability in freight risk management, we compare Value-at-Risk and Expected Shortfall forecasts. Our results show that accounting for volatility regimes and asymmetry does not enhance the performance of one-day-ahead forecasts of either risk measure for both long and short trading positions.

- by Thomas Walther
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- Risk Management, Volatility Forecasting, GARCH, Value at Risk

This paper examines the relationship between stock market (KSE-100), money market (M2 and 180 days T-bill rate), and foreign exchange market (ER: PKR/USD) in Pakistan by using monthly data covering the period from 2000:M1 to 2015:M12. The study investigates long-run equilibrium relationship between these three financial markets by employing Johansen and Juselius [1] cointegration tests. Long-run and short-run causality relationship between stock market and other macroeconomic variables is also established by employing vector error correction model (VECM) and pairwise granger causality tests. The results of multivariate cointegration test (trace test) indicate a one cointegrating vector, and the significant normalized cointegrating coefficients are evident of long run equilibrium relationship between all the selected variables. Negative and significant ECT (−1) for all variables during full sample period witness the presence of long-run causality connection among variables, while during the military regime and democratic regime, significant difference of long-run causal connections are identified across the regimes. Moreover, the results of granger causality test also indicate that there are significant variations in the causality relationship among variables across the regimes. Therefore, it is essential for forecasting, planning and policy making to consider the importance of political governance system while analyzing the historical cointegration among financial market and make the necessary adjustments accordingly.

- by Roni Bhowmik, PhD and +1
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- Stock Markets, Volatility, Garch Models

Novel model specifications that include a time-varying long-run component in the dynamics of realized covariance matrices are proposed. The modeling framework allows the secular component to enter the model either additively or as a multiplicative factor, and to be specified parametrically, using a MIDAS filter, or non-parametrically. Estimation is performed by maximizing a Wishart quasi-likelihood function. The one-step ahead forecasting performance is assessed by means of three approaches: model confidence sets, minimum variance portfolios and Value-at-Risk. The results illustrate that the proposed models outperform benchmarks incorporating a constant long-run component both in and out-of-sample. (Luc Bauwens), manuela.braione@uclouvain.be (Manuela Braione), storti@unisa.it (Giuseppe Storti) 1 Luc Bauwens and Manuela Braione acknowledge support of the "Communauté française de Belgique" through contract "Projet d'Actions de Recherche Concertées 12/17-045", granted by the "Académie universitaire Louvain".

- by Manuela Braione and +2
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- Multivariate modeling, Garch Models, Realized Covariance

This paper examines the extent of contagion and interdependence across the six Asian emerging countries stock markets (e.g., Bangladesh, China, India, Malaysia, the Philippine, and South Korea) and then try to quantify the extent of the Asian emerging market fluctuations which are described by intra-regional contagion effect. These markets experienced both fast growth and key upheaval during the sample period, and thus, provide potentially rich information on the nature of border market interactions. Using the daily stock market index data from January 2002 to December 2016 (breaking the 15 years data set into three sub periods; pre-crisis, crisis, and post crisis periods); particularly make attention to the global financial crisis of 2007∼2008. The return and volatility spillovers are modeled through the GARCH (generalized autoregressive conditional heteroscedasticity), pairwise Granger causality tests, and the forecast error variance decomposition in a generalized VAR (vector auto regression) models. This paper shows that volatility and return spillovers behave very differently over time, during the pre-crisis, crisis, and post crisis periods. Importantly, Asian emerging stock markets interaction is less before the global financial crisis period. The return and volatility spillover indices touch their respective historical peaks during the global financial crisis 2007∼2008, however Bangladeshi market faces this condition in 2009∼2010.

- by Roni Bhowmik, PhD and +1
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- Finance, Stock Markets, Global Financial Crisis, Volatility
- by Francisco López-Herrera and +2
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- Language Variation and Change, Financial Risk Management, Risk Management, Stock Markets

Interest is in evaluating, by Markov chain Monte Carlo (MCMC) simulation, the expected value of a function with respect to a, possibly unnormalized, probability distribution. A general purpose variance reduction technique for the MCMC estimator, based on the zero-variance principle introduced in the physics literature, is proposed. Conditions for asymptotic unbiasedness of the zero-variance estimator are derived. A central limit theorem is also proved under regularity conditions. The potential of the idea is illustrated with real applications to probit, logit and GARCH Bayesian models. For all these models, a central limit theorem and unbiasedness for the zero-variance estimator are proved (see the supplementary material available on-line).

- by Antonietta Mira
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- Mathematics, Computer Science, Statistics, Statistical Computing
- by Atilla Gökçe
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- Volatility (Financial Econometrics), ARCH models, Ekonomi, Iktisat

In recent years, the increasing use of digital currency has been featured in the banking
environment and payment market. The Bitcoin, as the main representative of digital currencies,
reached the $ 29 billion mark traded in the last 12 months within an average level of
capitalization at $ 5 billion. The Court of Justice of the European Union has already taken the
first steps towards the recognition of Bitcoin as a currency, which is not shared with economists,
who has the main criticism of high volatility in their negotiated prices. In this paper, we will
use the model I-GARCH to establish an indicator of volatility of Bitcoin and also for a portfolio
of traditional currencies that represent more than 70% of international trading volumes, to try
to determine how far Bitcoin is in relation to other currencies in terms of volatility at this time.

- by Paulo Celso Lavinas Barbosa
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- Volatility, Bitcoin, Garch Models, Digital Currency