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 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

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.

Estimating Long-Term Volatility on National Stock Exchange of India

2019 ◽  
pp. 229-237
Keyword(s):  
Stock Exchange ◽  
Stock Index ◽  
Garch Models ◽  
Conditional Heteroskedasticity ◽  
Large Sample ◽  
Market Capitalization ◽  
Stock Indices ◽  
Autoregressive Conditional Heteroskedasticity

The main objective of this chapter is to provide an elaborate framework on the long-term volatility of the National Stock Exchange of India based on Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The CNX-100 index is one of the most diversified Indian stock indices which includes over 38 sectors of the economy. This stock index represents about 81.57% of the free-floating market capitalization of stocks listed on the National Stock Exchange (NSE) of India from March 2014. Moreover, this book chapter empirically tested volatility clusters of CNX100 index using a large sample database from October 2007 to July 2014.

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.

Forecasting developed and BRICS stock markets with cryptocurrencies and gold: generalized orthogonal generalized autoregressive conditional heteroskedasticity and generalized autoregressive score analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ahmed Jeribi ◽  
Achraf Ghorbel
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Value At Risk ◽  
Developed Countries ◽  
The Other ◽  
Conditional Heteroskedasticity ◽  
Content Type ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Developed Stock Markets ◽  
Generalized Orthogonal

PurposeThe purpose of this paper is threefold. First, it models and forecasts the risk of the five leading cryptocurrencies, stock market indices (developed and BRICS) and gold returns. Second, it conducts different backtesting procedures forecasts. Third, it focuses on the hedging potential of cryptocurrencies and gold.Design/methodology/approachThe authors used the generalized autoregressive score (GAS) models to model and forecast the risk of cryptocurrencies, stock market indices and gold returns. They conduct different backtesting procedures of the 1% and 5%-value-at-risk (VaR) forecasts. They also use the generalized orthogonal generalized autoregressive conditional heteroskedasticity (GO-GARCH) model to explore the hedging potential of cryptocurrencies by estimating the dynamic conditional correlation between cryptocurrencies and gold, on the one hand, and stock markets on the other hand.FindingsWhen conducting different backtesting procedures of VaR, our finding suggests that Bitcoin has the highest VaR among cryptocurrencies and Gold and the BRICS indices returns have lower VaR compared to the developed countries. Finally, we provide evidence that the risks among developed stock markets can be hedged by Bitcoin and Gold. Bitcoin can be considered as the new Gold for these economies. Unlike Bitcoin, Gold can be considered as a hedge for Chinese and Indian investors. However, Gold and Bitcoin can be considered as diversifier assets for the other BRICS economies while Dash and Monero are diversifier assets for developed stock markets.Originality/valueThe first paper's empirical contribution lies in analyzing optimal forecast models for cryptocurrencies (other than Bitcoin) returns and risk. The second contribution consists of studying the hedging potential of five leading cryptocurrencies. To the best of our knowledge, no previous studies have investigated the role of cryptocurrencies for BRICS investors.

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.

scholarly journals THE ROLE OF MASS COMMUNICATIONS TO THE MARKET INTERVENTIONS OF RICE COMMODITY IN INDONESIA (Peran Komunikasi Massa terhadap Intervensi Pasar Komoditas Beras di Indonesia)

2019 ◽  
Vol 9 (2) ◽  
pp. 173-186
Author(s):  
Kumara Jati
Keyword(s):  
Time Series ◽  
Mass Communication ◽  
Moving Average ◽  
Time Series Model ◽  
Conditional Heteroskedasticity ◽  
Structural Time Series ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Structural Time ◽  
Rice Policy ◽  
The Government

AbstractThe rice is a staple food for the people and significantly contributes to economic development in Indonesia. Occasionally a market intervention should be implemented by the Government of Indonesia during the low harvest season to control and to manage the price of rice and the inflation, so low-income society could meet their basic needs. This study examines how communication aspect is really important as a part of market intervention mechanism to control the price and the stock of rice in Indonesia. Autoregressive and Moving Average, Autoregressive Conditional Heteroskedasticity / Generalized Autoregressive Conditional Heteroskedasticity, and the Structural Time-Series Model are applied  with a dummy variable on daily and monthly data of the stock and the price of rice from January 1, 2015 until June 27, 2016. It can be inferred from the data that the form of mass communication by the government to relevant stakeholders (channel distribution and consumers) can run well, especially in order to maintain the supply and the price stabilization of rice. Nevertheless, the ARMA(1,1)-GARCH(1,1) model with dummy variables, inter alia mass communication, and also the number of market operations and rice policy, are not so influential on the price of rice, but more influence on the stock of rice. Then, the Structural Time-Series Model shows that the fluctuation of price and stock is affected by seasonal and cycle components especially more fluctuated in the month of January-March. Therefore, the relevant authorities are expected to maximize the rice policy in order to maintain the price stability in the short term, medium term and long term. AbstrakBeras merupakan makanan pokok bagi masyarakat dan secara signifikan berkontribusi terhadap pembangunan ekonomi di Indonesia. Terkadang intervensi pasar harus dilaksanakan oleh pemerintah diluar musim panen untuk mengendalikan dan mengelola harga beras dan inflasi, sehingga masyarakat berpenghasilan rendah dapat memenuhi kebutuhan mereka. Penelitian ini mengkaji bagaimana aspek komunikasi sangat penting sebagai mekanisme intervensi pasar untuk mengendalikan harga dan stok beras di Indonesia. Autoregressive and Moving Average and Autoregressive Conditional Heteroskedasticity / Generalized Autoregressive Conditional Heteroskedasticity serta the Structural Time-Series Model  digunakan dengan variabel dummy pada data stok dan harga beras, baik harian maupun bulanan, antara 1 Januari 2015 hingga 27 Juni 2016. Hasil analisis menyimpulkan bahwa komunikasi massa oleh pemerintah kepada pihak-pihak yang berkepentingan (pelaku usaha dan konsumen) dapat berjalan dengan baik terutama untuk menjaga pasokan dan stabilitas harga beras. Namun demikian, model ARMA(1,1)-GARCH(1,1) dengan variabel dummy yaitu komunikasi massa, serta jumlah operasi pasar dan kebijakan beras kurang berpengaruh terhadap harga beras namun lebih berpengaruh terhadap stok beras. Kemudian, the Structural Time-Series Model menunjukkan bahwa naik turunnya harga dan stok beras berasal dari komponen musiman dan siklus terutama lebih berfluktuasi pada bulan Januari-Maret. Oleh karena itu, otoritas terkait diharapkan dapat memaksimalkan kebijakan beras untuk menjaga stabilitas harga dan stok beras dalam jangka pendek, menengah, dan panjang. 

scholarly journals A cyclic queueing network with dependent exponential service times

1978 ◽  
Vol 15 (03) ◽  
pp. 573-589 ◽  
Author(s):  
Patricia A. Jacobs
Keyword(s):  
Moving Average ◽  
Queueing Network ◽  
The Other ◽  
Autoregressive Moving Average ◽  
Moving Average Process ◽  
Virtual Waiting Time ◽  
Exponential Random Variables ◽  
Service Times ◽  
Number Of Customers ◽  
Autoregressive Moving Average Process

A cyclic queueing network with two servers and a finite number of customers is studied. The service times for server 1 form an earma(1,1) process (exponential mixed autoregressive moving average process both of order 1) which is a sequence of positively correlated exponential random variables; the process in general is not Markovian. The service times for the other server are independent with a common exponential distribution. Limiting results for the number of customers in queue and the virtual waiting time at server 1 are obtained. Comparisons are made with the case of independent exponential service times for server 1.

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