scholarly journals Currency and Index Futures Markets Dynamics: Asymmetric Volatility, Leverage Effects and Long-memory Characteristics

2021 ◽  
Vol 39 (3) ◽  
Author(s):  
John Francis Diaz ◽  
Kai-Hong Goh, Imba Goh
Keyword(s):  
Long Memory ◽  
Futures Markets ◽  
Moving Average ◽  
Memory Models ◽  
Autoregressive Moving Average ◽  
Conditional Heteroskedasticity ◽  
Index Futures ◽  
Asymmetric Volatility ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Asymmetric Power

This research examines the performance of return and volatility models on the long-memory, asymmetric volatility, and leverage effects by comparing the two most active futures markets globally, Currency and Index Futures. The study uses daily data from the database Quandl.com website, from January 2000 to March 2018. This study utilizes two short-memory models, the autoregressive moving average – exponential generalized autoregressive conditional heteroskedasticity (ARMA-EGARCH); and  autoregressive moving average – asymmetric power autoregressive conditional heteroskedasticity (ARMA-APARCH); and two long-memory models, autoregressive fractionally-integrated moving average – fractionally-integrated exponential generalized autoregressive conditional heteroskedasticity (ARFIMA-FIEGARCH); and autoregressive fractionally-integrated moving average – fractionally-integrated asymmetric power autoregressive conditional heteroskedasticity (ARFIMA-FIAPARCH). The paper shows that portfolio managers and traders can benefit in holding Index futures, because of their steady returns, but with a relatively higher risk for the whole sample period. The study also finds that Currency futures has better safe-haven properties during crisis period, but Index futures performs better after crisis period. Findings suggest that both long-memory models are capable of accurate forecast, especially on the volatility of Currency and Index futures. The proper modelling of Currency and Index futures time-series data can provide traders, fund managers and investors in creating well-defined trading strategies, especially in high volatility regimes.

scholarly journals Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes

2005 ◽  
Vol 12 (1) ◽  
pp. 55-66 ◽  
Author(s):  
W. Wang ◽  
P. H. A. J. M Van Gelder ◽  
J. K. Vrijling ◽  
J. Ma
Keyword(s):  
Seasonal Variation ◽  
Moving Average ◽  
Arma Model ◽  
Autoregressive Moving Average ◽  
Conditional Heteroskedasticity ◽  
Daily Streamflow ◽  
Arch Effect ◽  
Monthly Streamflow ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Residual Series

Abstract. Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average) models for seasonal streamflow series). However, with McLeod-Li test and Engle's Lagrange Multiplier test, clear evidences are found for the existence of autoregressive conditional heteroskedasticity (i.e. the ARCH (AutoRegressive Conditional Heteroskedasticity) effect), a nonlinear phenomenon of the variance behaviour, in the residual series from linear models fitted to daily and monthly streamflow processes of the upper Yellow River, China. It is shown that the major cause of the ARCH effect is the seasonal variation in variance of the residual series. However, while the seasonal variation in variance can fully explain the ARCH effect for monthly streamflow, it is only a partial explanation for daily flow. It is also shown that while the periodic autoregressive moving average model is adequate in modelling monthly flows, no model is adequate in modelling daily streamflow processes because none of the conventional time series models takes the seasonal variation in variance, as well as the ARCH effect in the residuals, into account. Therefore, an ARMA-GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) error model is proposed to capture the ARCH effect present in daily streamflow series, as well as to preserve seasonal variation in variance in the residuals. The ARMA-GARCH error model combines an ARMA model for modelling the mean behaviour and a GARCH model for modelling the variance behaviour of the residuals from the ARMA model. Since the GARCH model is not followed widely in statistical hydrology, the work can be a useful addition in terms of statistical modelling of daily streamflow processes for the hydrological community.

Long-memory Modelling and Forecasting of the Returns and Volatility of Exchange-traded Notes (ETNs)

2017 ◽  
Vol 11 (1) ◽  
pp. 23-53 ◽  
Author(s):  
Argel S. Masa ◽  
John Francis T. Diaz
Keyword(s):  
Long Memory ◽  
Moving Average ◽  
Weak Form ◽  
Jel Classification ◽  
Warning Sign ◽  
Conditional Heteroskedasticity ◽  
Long Memory Process ◽  
Long Run ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Exchange Traded Notes

This research provides evidence in determining the predictability of exchange-traded notes (ETNs). It utilises commodity, currency and equity ETNs as data samples, and examines the performance of the three combinations of long-memory models, that is, autoregressive fractionally integrated moving average and generalised autoregressive conditional heteroskedasticity (ARFIMA-GARCH), autoregressive fractionally integrated moving average and fractionally integrated generalised autoregressive conditional heteroskedasticity (ARFIMA-FIGARCH) and autoregressive fractionally integrated moving average and hyperbolic generalised autoregressive conditional heteroskedasticity (ARFIMA-HYGARCH), and three forecasting horizons, that is, 1-, 5- and 20-step-ahead horizons, to model ETNs returns and volatilities. The article finds long-memory processes in ETNs; however, dual long-memory process in returns and volatilities is not verified. The research also poses a challenge to the weak-form efficiency hypothesis of Fama (1970) because lagged changes determine future values, especially in volatility. The findings also show that differences in the characteristics of commodity, currency and equity ETNs are not concluded because of similarities in ETN traits and several insignificant results. However, the presence of intermediate memory was identified, and should serve as a warning sign for investors not to keep these investments in the long run. Lastly, the ARFIMA-FIGARCH model has a slight edge over the ARFIMA-GARCH and ARFIMA-HYGARCH specifications using 1-, 5- and 20-forecast horizons. JEL Classification: G11, G17

THE SPILLOVER AND LEVERAGE EFFECTS OF EQUITY EXCHANGE-TRADED NOTES (ETNS)

2019 ◽  
Vol 19 (03) ◽  
pp. 1950013
Author(s):  
JO-HUI CHEN ◽  
JOHN FRANCIS DIAZ
Keyword(s):  
Moving Average ◽  
The Other ◽  
Autoregressive Moving Average ◽  
Conditional Heteroskedasticity ◽  
Negative Relation ◽  
Stock Indices ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Exchange Traded Notes

This research utilizes the Autoregressive Moving Average–General Autoregressive Conditional Heteroskedasticity (ARMA–GARCH) and Autoregressive Moving Average–Exponential General Autoregressive Conditional Heteroskedasticity (ARMA–EGARCH) in studying the spillover and leverage effects of returns and volatilities of seven equity exchange-traded notes (ETNs) and their tracked stock indices. This study finds positive returns transmissions between the two investment instruments. Unilateral influence and bilateral relationships also exist that may help investors in finding investment clues to approximate possible movements of ETNs about stock indices and vice versa. This paper also observes negative returns and volatility transmissions that may caution traders in the possible reversal of movement of the other instrument. Disinvestments, transfer of allocation, and inverse investing strategies are some of the possible reasons attributable to this negative relation.

scholarly journals Has COVID-19 Changed the Hedge Effectiveness of Bitcoin?

2021 ◽  
Vol 9 ◽  
Author(s):  
Yinpeng Zhang ◽  
Panpan Zhu ◽  
Yingying Xu
Keyword(s):  
Futures Markets ◽  
Vector Autoregression ◽  
Spot Market ◽  
Futures Market ◽  
Conditional Heteroskedasticity ◽  
Volatility Spillover ◽  
Empirical Results ◽  
Dynamic Correlation ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Gold Futures

The Bitcoin market has become a research hotspot after the outbreak of Covid-19. In this paper, we focus on the relationships between the Bitcoin spot and futures. Specifically, we adopt the vector autoregression-dynamic correlation coefficient-generalized autoregressive conditional heteroskedasticity (VAR-DCC-GARCH) model and vector autoregression-Baba, Engle, Kraft, and Kroner-generalized autoregressive conditional heteroskedasticity (VAR-BEKK-GARCH) models and calculate the hedging effectiveness (HE) value to investigate the dynamic correlation and volatility spillover and assess the risk reduction of the Bitcoin futures to spot. The empirical results show that the Bitcoin spot and futures markets are highly connected; second, there exists a bi-directional volatility spillover between the spot and futures market; third, the HE value is equal to 0.6446, which indicates that Bitcoin futures can indeed hedge the risks in the Bitcoin spot market. Furthermore, we update the data to the post-Covid-19 period to do the robustness checks. The results do not change our conclusion that Bitcoin futures can hedge the risks in the Bitcoin spot market, and besides, the post-Covid-19 results indicate that the hedging ability of Bitcoin futures increased. Finally, we test whether the gold futures can be used as a Bitcoin spot market hedge, and we further control other cryptocurrencies to illustrate the hedging ability of the Bitcoin futures to the Bitcoin spot. Overall, the empirical results in this paper will surely benefit the related investors in the Bitcoin market.

Stock Returns and Long-range Dependence

2019 ◽  
pp. 097215091986696
Author(s):  
Alexander Ayertey Odonkor ◽  
Emmanuel Nkrumah Ababio ◽  
Emmanuel Amoah- Darkwah ◽  
Richard Andoh
Keyword(s):  
Stock Returns ◽  
Stock Prices ◽  
Long Memory ◽  
Stock Exchange ◽  
Moving Average ◽  
Efficient Market Hypothesis ◽  
Conditional Heteroskedasticity ◽  
Relevant Market ◽  
Figarch Model ◽  
Ghana Stock Exchange

This article studies the long memory behaviour of stock returns on the Ghana Stock Exchange. The estimates employed are based on the daily closing prices of seven stocks on the Ghana Stock Exchange. The results of the autoregressive fractionally integrated moving average-fractionally integrated generalized autoregressive conditional heteroskedasticity (ARFIMA-FIGARCH) model suggest that the stock returns are characterized by a predictable component; this demonstrates a complete departure from the efficient market hypothesis suggesting that relevant market information was only partially reflected in the changes in stock prices. This pattern of time dependence in stock returns may allow for past information to be used to improve the predictability of future returns.

The Information Content of ASX SPI 200 Implied Volatility

2016 ◽  
Vol 19 (01) ◽  
pp. 1650002 ◽  
Author(s):  
Hassan Tanha ◽  
Michael Dempsey
Keyword(s):  
Information Content ◽  
Implied Volatility ◽  
Conditional Heteroskedasticity ◽  
Index Futures ◽  
Call Options ◽  
Autoregressive Conditional Heteroskedasticity

In Australia, the equivalent of a US VIX indicator has recently become available. In response, we consider whether the information captured in the implied volatility of options on the Australian SPI 200 Futures index is superior to the information content of a generalized autoregressive conditional heteroskedasticity (GARCH) approach to volatility prediction. We conclude that the implied volatility of at-the-money (ATM) call options on the SPI 200 Index futures is more powerful, dominating other modes of moneyness options as well as GARCH predictions.

scholarly journals Model ARIMA-GARCH Pada Peramalan Harga Saham PT. Jasa Marga (Persero)

2021 ◽  
Vol 3 (3) ◽  
pp. 164-170
Author(s):  
Fransisca Trisnani Ardikha Putri ◽  
Etik Zukhronah ◽  
Hasih Pratiwi
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Moving Average ◽  
Garch Model ◽  
Conditional Heteroskedasticity ◽  
Autoregressive Integrated Moving Average ◽  
Autoregressive Conditional Heteroskedasticity

Abstract– PT Jasa Marga is a great reputation company, the leader in comparable businesses, has a steady income, and paying dividends consistently. This paper aims to find the best model to forecast stock price of PT Jasa Marga using ARIMA-GARCH. The data used is daily stock price of PT Jasa Marga from March 2020 to March 2021. Autoregressive Integrated Moving Average (ARIMA) is a method that can be used to forecast stock prices. However, an economical data tend to have heteroscedasticity problems, one of the methods used to overcome them is Generalized Autoregressive Conditional Heteroskedasticity (GARCH). Future stock price of PT Jasa Marga is forecasted with ARIMA-GARCH model.  The data is modeled with ARIMA first, if there is heteroscedasticity, combine the model with GARCH model. The result of this study indicated that ARIMA (1, 1, 1) – GARCH (2, 2) is the best model, with MAPE 1,5647 Abstrak– PT Jasa Marga adalah perusahaan yang reputasinya baik, terdepan di perusahaan-perusahaan sejenis, stabil pendapatannya, dan pembayaran devidennya konsisten. Paper ini bertujuan untuk mencari model terbaik dalam meramalkan harga saham PT Jasa Marga menggunakan ARIMA-GARCH. Data harga saham yang diolah yaitu data sekunder dari PT Jasa Marga pada Maret 2020 hingga Maret 2021. Autoregressive Integrated Moving Average (ARIMA) sebagai metode yang dapat dimanfaatkan guna meramalkan harga saham. Akan tetapi, data tentang ekonomi cenderung memiliki masalah heteroskedastisitas, metode yang umum dipakai untuk mengatasinya adalah Generalized Autoregressive Conditional Heteroskedasticity (GARCH). Harga saham PT Jasa Marga diramalkan dengan model ARIMA-GARCH.  Data terlebih dahulu dimodelkan dengan ARIMA, jika didapati adanya heteroskedastisitas, maka model tersebut dikombinasikan dengan GARCH. Penelitian ini menghasilkan ARIMA (1,1,1)-GARCH(2,2) sebagai model terbaik dengan MAPE 1,5647.

scholarly journals PERFORMANCE OF MULTIPLE LINEAR REGRESSION AND AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODELS IN PREDICTING ANNUAL TEMPERATURES OF OGUN STATE, NIGERIA

2017 ◽  
Vol 16 (1) ◽  
pp. 50-62
Author(s):  
I. JIBRIL ◽  
J. J. MUSA ◽  
P. O.O. DADA ◽  
H. E. IGBADUN ◽  
J. M. MOHAMMED ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Long Memory ◽  
Regression Models ◽  
Moving Average ◽  
Arma Model ◽  
Autoregressive Moving Average ◽  
Linear Regression Models ◽  
Ogun State ◽  
Maximum Temperatures

The performance of Autoregressive Moving Average and Multiple Linear Regression Models in predicting minimum and maximum temperatures of Ogun State is herein reported. Maximum and Minimum temperatures data covering a period of 29 years (1982 -2009) obtained from the Nigerian Meteorological Agency (NiMet), Abeokuta office, Nigeria, were used for the analyses. The data were first processed and aggregated into annual time series. Mann-Kendal non-parametric test and spectral analysis were carried out to detect whether there is trend, seasonal pattern, and either short or long memory in the time series. Mann-Kendal Z-values obtained are –0.47 and –2.03 for minimum and maximum temperatures respectively, indicating no trend, though the plot shows a slight change. The Lo’s R/S Q(N,q) values for minimum and maximum temperatures are 3.67 and 4.43, which are not within the range 0.809 and 1.862, thus signifying presence of long memory. The data was divided into two and the first 20 years data was used for model development, while the remaining was used for validation. Autoregressive Moving Average (ARMA) model of order (5, 3) and Autoregressive (AR) model of order 2 are found best for predicting minimum and maximum temperatures respectively. Multiple Linear Regression (MLR) model with 4 features (moving average, exponential moving average, rate of change and oscillator) were fitted for both temperatures. The ARMA and AR models were found to perform better with Mean Absolute Percentage Error (MAPE) values of -2.89 and -1.37 for minimum and maximum temperatures, compared with the Multiple Linear Regression Models with MAPE values of 141 and 876 respectively. Results of ARMA model can be relied on in generating forecast of temperature of the study area because of their minimal error values. However, it is recommended other climatic elements that were not captured in this paper due to unavailability of information be considered too in order to see which model is best for them.  

scholarly journals Impact Of Financial Crisis On Hedging Effectiveness Of Futures Contracts: Evidence From The National Stock Exchange Of India

2015 ◽  
Vol 10 (2) ◽  
pp. 69-88 ◽  
Author(s):  
Kapil Gupta ◽  
Mandeep Kaur
Keyword(s):  
Financial Crisis ◽  
Stock Exchange ◽  
Moving Average ◽  
Futures Contracts ◽  
Conditional Heteroskedasticity ◽  
Vector Autoregressive ◽  
Hedging Effectiveness ◽  
Autoregressive Conditional Heteroskedasticity ◽  
The 2008 Financial Crisis ◽  
The Impact

Abstract The present study examines the impact of the 2008 financial crisis on the hedging effectiveness of three index futures contracts traded on the National Stock Exchange of India for near, next and far month contracts over the sample period of January 2000 – June 2014. The hedge ratios were calculated using eight methods; Naive hedging, Ederington’s Model, Autoregressive Integrated Moving Average, Vector Autoregressive, Vector Error Correction Methodology, Generalized Autoregressive Conditional Heteroskedasticity, Exponential Generalized Autoregressive Conditional Heteroscedasticity and Threshold Generalized Autoregressive Conditional Heteroskedasticity. The study finds an improvement in hedging effectiveness during the post-crisis period, which implies that during the high-volatility period hedging effectiveness also improves. It was also found that near month futures contracts are a more effective tool for hedging as compared to next and far month contracts, which imply that liquidity is a more important determinant of hedging effectiveness than hedge horizons. The study also finds that a time-invariant hedge ratio is more efficient than time-variant hedging. Therefore, knowledge of sophisticated econometrical tools does not help to improve hedge effectiveness.

Sign in / Sign up

Export Citation Format

Share Document