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Journal articles on the topic 'ARFIMA model'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Garafutdinov, Robert. "Formation of Investment Portfolios of Two Assets Based on Forecast Returns Using the ARFIMA-GARCH Model." Vestnik Volgogradskogo gosudarstvennogo universiteta. Ekonomika, no. 2 (July 2021): 130–36. http://dx.doi.org/10.15688/ek.jvolsu.2021.2.11.

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The paper tests the hypothesis that the formation of investment portfolios of two assets based on predicted returns obtained using fractal models with conditional heteroscedasticity (ARFIMA-GARCH) allows to obtain portfolios with better characteristics than those obtained using the ARFIMA model. A computational experiment on artificial data and real data from the Russian stock market was carried out. The software implementation of the hypothesis testing algorithm was carried out using Python and R programming languages. The following results were obtained. Average absolute forecast error of th

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Eğri˙oğlu, Erol, and Süleyman Günay. "Bayesian model selection in ARFIMA models." Expert Systems with Applications 37, no. 12 (2010): 8359–64. http://dx.doi.org/10.1016/j.eswa.2010.05.047.

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Szolgayová, Elena, Josef Arlt, Günter Blöschl, and Ján Szolgay. "Wavelet based deseasonalization for modelling and forecasting of daily discharge series considering long range dependence." Journal of Hydrology and Hydromechanics 62, no. 1 (2014): 24–32. http://dx.doi.org/10.2478/johh-2014-0011.

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Abstract Short term streamflow forecasting is important for operational control and risk management in hydrology. Despite a wide range of models available, the impact of long range dependence is often neglected when considering short term forecasting. In this paper, the forecasting performance of a new model combining a long range dependent autoregressive fractionally integrated moving average (ARFIMA) model with a wavelet transform used as a method of deseasonalization is examined. It is analysed, whether applying wavelets in order to model the seasonal component in a hydrological time series

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Panjaitan, Helmi, Alan Prahutama, and Sudarno Sudarno. "PERAMALAN JUMLAH PENUMPANG KERETA API MENGGUNAKAN METODE ARIMA, INTERVENSI DAN ARFIMA (Studi Kasus : Penumpang Kereta Api Kelas Lokal EkonomiDAOP IV Semarang)." Jurnal Gaussian 7, no. 1 (2018): 96–109. http://dx.doi.org/10.14710/j.gauss.v7i1.26639.

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Autoregressive Integrated Moving Average (ARIMA) is stationary time series model after differentiation. Differentiation value of ARIMA method is an integer so it is only able to model in the short term. The best model using ARIMA method is ARIMA([13]; 1; 0) with an MSE value of 1,870844. The Intervention method is a model for time series data which in practice has extreme fluctuations both up and down. In the data plot the number of train passengers was found to be extreme fluctuation. The data used was from January 2009 to June 2017 where fluctuation up significantly in January 2016 (T=85 to

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Diaz, John Francis T. "Do Scarce Precious Metals Equate to Safe Harbor Investments? The Case of Platinum and Palladium." Economics Research International 2016 (January 10, 2016): 1–7. http://dx.doi.org/10.1155/2016/2361954.

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This research establishes the predictability and safe harbor properties of two scarce precious metals, namely, platinum and palladium. Utilizing their spot prices, the study concludes intermediate memory in the return structures of both precious metals, which implies the instability of platinum and palladium returns’ persistency in the long run. However, both the ARFIMA-FIGARCH and the ARFIMA-FIAPARCH models confirm long-memory properties in the volatility of the two spot prices. The leverage effects phenomenon is not also present based on the ARFIMA-APARCH and ARFIMA-FIAPARCH models, which ma

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Masa, Argel S., and John Francis T. Diaz. "Long-memory Modelling and Forecasting of the Returns and Volatility of Exchange-traded Notes (ETNs)." Margin: The Journal of Applied Economic Research 11, no. 1 (2017): 23–53. http://dx.doi.org/10.1177/0973801016676012.

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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 an

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Boutahar, Mohamed. "Optimal prediction with nonstationary ARFIMA model." Journal of Forecasting 26, no. 2 (2007): 95–111. http://dx.doi.org/10.1002/for.1012.

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8

Garafutdinov, Robert. "Influence of some ARFIMA model parameters on the accuracy of financial time series forecasting." Applied Econometrics 62 (2021): 85–100. http://dx.doi.org/10.22394/1993-7601-2021-62-85-100.

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The influence of ARFIMA model parameters on the accuracy of financial time series forecasting on the example of artificially generated long memory series and daily log returns of RTS index is investigated. The investigated parameters are deviation of the integration order value from its «true» value, as well as the memory «length» considered by the model. Based on the research results, some practical recommendations for modeling using ARFIMA have been formulated.

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Mah, P. J. W., N. A. M. Ihwal, and N. Z. Azizan. "FORECASTING FRESH WATER AND MARINE FISH PRODUCTION IN MALAYSIA USING ARIMA AND ARFIMA MODELS." MALAYSIAN JOURNAL OF COMPUTING 3, no. 2 (2018): 81. http://dx.doi.org/10.24191/mjoc.v3i2.4887.

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Malaysia is surrounded by sea, rivers and lakes which provide natural sources of fish for human consumption. Hence, fish is one source of protein supply to the country and fishery is a sub-sector that contribute to the national gross domestic product. Since fish forecasting is crucial in fisheries management for managers and scientists, time series modelling can be one useful tool. Time series modelling have been used in many fields of studies including the fields of fisheries. In a previous research, the ARIMA and ARFIMA models were used to model marine fish production in Malaysia and the ARF

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10

Duppati, Geeta, Anoop S. Kumar, Frank Scrimgeour, and Leon Li. "Long memory volatility in Asian stock markets." Pacific Accounting Review 29, no. 3 (2017): 423–42. http://dx.doi.org/10.1108/par-02-2016-0009.

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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 integ

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Tan, Bin. "Estimation of Value-at-Risk Based on ARFIMA-FIAPARCH-SKST Model." Advanced Materials Research 601 (December 2012): 464–69. http://dx.doi.org/10.4028/www.scientific.net/amr.601.464.

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This paper focus mainly on some important stylized facts in financial market, such as long memory, asymmetry and leverage effect, and so on, and apply ARFIMA-APARCH-SKST model to measure dynamic Value at Risk, at the same time, ARMA-EGARCH(APARCH)-SKST, ARFIMA- FIEGARCH-SKST are used to compare empirical effect of different risk model, at last, we apply LRT method to test accuracy of risk model. Our results indicate that all models used in this paper can measure dynamic VaR at 95%, 99% and 99.5% confidence levels, and there is no significant difference for different risk model for different st

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Reisen, Valderio A., Manoel R. Sena Jr., and Silvia R. C. Lopes. "Error and Model Misspecification in ARFIMA Process." Brazilian Review of Econometrics 21, no. 1 (2001): 101. http://dx.doi.org/10.12660/bre.v21n12001.3193.

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Weron, Aleksander. "Mathematical Models for Dynamics of Molecular Processes in Living Biological Cells. A Single Particle Tracking Approach." Annales Mathematicae Silesianae 32, no. 1 (2018): 5–41. http://dx.doi.org/10.1515/amsil-2017-0019.

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Abstract In this survey paper we present a systematic methodology of how to identify origins of fractional dynamics. We consider three models leading to it, namely fractional Brownian motion (FBM), fractional Lévy stable motion (FLSM) and autoregressive fractionally integrated moving average (ARFIMA) process. The discrete-time ARFIMA process is stationary, and when aggregated, in the limit, it converges to either FBM or FLSM. In this sense it generalizes both models. We discuss three experimental data sets related to some molecular biology problems described by single particle tracking. They a

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Esarey, Justin. "Fractionally Integrated Data and the Autodistributed Lag Model: Results from a Simulation Study." Political Analysis 24, no. 1 (2016): 42–49. http://dx.doi.org/10.1093/pan/mpv032.

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Two contributions in this issue, Grant and Lebo and Keele, Linn, and Webb, recommend using an ARFIMA model to diagnose the presence of and estimate the degree of fractional integration, then either (i) fractionally differencing the data before analysis or, (ii) for cointegrated variables, estimating a fractional error correction model. But Keele, Linn, and Webb also present evidence that ARFIMA models yield misleading indicators of the presence and degree of fractional integration in a series with fewer than 1000 observations. In a simulation study, I find evidence that the simple autodistribu

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Mahmad Azan, Atiqa Nur Azza, Nur Faizatul Auni Mohd Zulkifly Mototo, and Pauline Jin Wee Mah. "The Comparison between ARIMA and ARFIMA Model to Forecast Kijang Emas (Gold) Prices in Malaysia using MAE, RMSE and MAPE." Journal of Computing Research and Innovation 6, no. 3 (2021): 22–33. http://dx.doi.org/10.24191/jcrinn.v6i3.225.

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Gold is known as the most valuable commodity in the world because it is a universal currency recognized by every single bank across the globe. Thus, many people were interested in investing gold since gold market was always steadier compared to other investment (Khamis and Awang, 2020). However, the credibility of gold was questionable due to the changes in gold prices caused by a variety of circumstances (Henriksen, 2018). Hence, information on the inflation of gold prices were needed to understand the trend in order to plan for the future in accordance with international gold price standards

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Delgado, Miguel A., Javier Hidalgo, and Carlos Velasco. "BOOTSTRAP ASSISTED SPECIFICATION TESTS FOR THE ARFIMA MODEL." Econometric Theory 27, no. 5 (2011): 1083–116. http://dx.doi.org/10.1017/s0266466610000642.

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This paper proposes bootstrap assisted specification tests for the autoregressive fractionally integrated moving average model based on the Bartlett Tp-process with estimated parameters whose limiting distribution under the null depends on the estimated model and the estimation method employed. The computation of the asymptotic critical values is not easy if at all possible under these circumstances. To circumvent this problem Delgado, Hidalgo, and Velasco (2005, Annals of Statistics 33, 2568–2609) proposed an asymptotically pivotal transformation of the Tp-process with estimated parameters. T

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Lieberman, Offer, Judith Rousseau, and David M. Zucker. "SMALL-SAMPLE LIKELIHOOD-BASED INFERENCE IN THE ARFIMA MODEL." Econometric Theory 16, no. 2 (2000): 231–48. http://dx.doi.org/10.1017/s0266466600162048.

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The autoregressive fractionally integrated moving average (ARFIMA) model has become a popular approach for analyzing time series that exhibit long-range dependence. For the Gaussian case, there have been substantial advances in the area of likelihood-based inference, including development of the asymptotic properties of the maximum likelihood estimates and formulation of procedures for their computation. Small-sample inference, however, has not to date been studied. Here we investigate the small-sample behavior of the conventional and Bartlett-corrected likelihood ratio tests (LRT) for the fra

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Tripathy, Naliniprava. "Testing of Long Memory in Indian Stock Market using ARFIMA model." Journal of Prediction Markets 9, no. 3 (2016): 23–39. http://dx.doi.org/10.5750/jpm.v9i3.1055.

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This paper examines the presence of long memory property and market cycles in the Indian stock market by using daily closing price of Nifty Index from May 2009 to April 2015. The study has used Unit Root Test, Autocorrelation Test, Rescaled Range (R/S) statistics and ARFIMA Models to determine the long memory property in Indian stock market. The result of the study shows that Indian stock market exhibits a high degree of positive long-term persistence. The results of ARFIMA model also indicates that stock price index exhibits strong evidence of long memory and contradicts the evidence against

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Hanifa, Rezky Dwi, Mustafid Mustafid, and Arief Rachman Hakim. "PEMODELAN AUTOREGRESSIVE FRACTIONALLY INTEGRATED MOVING AVERAGE DENGAN EFEK EXPONENTIAL GARCH (ARFIMA-EGARCH) UNTUK PREDIKSI HARGA BERAS DI KOTA SEMARANG." Jurnal Gaussian 10, no. 2 (2021): 279–92. http://dx.doi.org/10.14710/j.gauss.v10i2.29933.

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Time series data is a type of data that is often used to estimate future values. Long memory phenomenon often occurs in time series data. Long memory is a condition that shows a strong correlation between observations even though they are quite far away. This phenomenon can be overcome by modeling time series data using the Autoregressive Fractional Integrated Moving Average (ARFIMA) model. This model is characterized by a fractional difference value. ARFIMA (Autoregressive Fractional Integrated Moving Average) model assumes that the residuals are normally distributed, mutually independent, an

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Kartikasari, Puspita, Hasbi Yasin, and Di Asih I. Maruddani. "AUTOREGRESSIVE FRACTIONAL INTEGRATED MOVING AVERAGE (ARFIMA) MODEL TO PREDICT COVID-19 PANDEMIC CASES IN INDONESIA." MEDIA STATISTIKA 14, no. 1 (2021): 44–55. http://dx.doi.org/10.14710/medstat.14.1.44-55.

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Currently the emergence of the novel coronavirus (Sars-Cov-2), which causes the COVID-19 pandemic and has become a serious health problem because of the high risk causes of death. Therefore, fast and appropriate action is needed to reduce the spread of the COVID-19 pandemic. One of the way is to build a prediction model so that it can be a reference in taking steps to overcome them. Because of the nature of transmission of this disease which is so fast and massive cause extreme data fluctuations and between objects whose observational distances are far enough correlated with each other (long m

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Xiu, Jin, and Yao Jin. "Empirical study of ARFIMA model based on fractional differencing." Physica A: Statistical Mechanics and its Applications 377, no. 1 (2007): 138–54. http://dx.doi.org/10.1016/j.physa.2006.11.030.

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Pan, Jeh-Nan, and Su-Tsu Chen. "Monitoring long-memory air quality data using ARFIMA model." Environmetrics 19, no. 2 (2008): 209–19. http://dx.doi.org/10.1002/env.882.

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Baillie, Richard T., Ching-Fan Chung, and Margie A. Tieslau. "Analysing inflation by the fractionally integrated ARFIMA-GARCH model." Journal of Applied Econometrics 11, no. 1 (1996): 23–40. http://dx.doi.org/10.1002/(sici)1099-1255(199601)11:1<23::aid-jae374>3.0.co;2-m.

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Kartikasari, Puspita, Hasbi Yasin, and Di Asih I Maruddani. "ARFIMA Model for Short Term Forecasting of New Death Cases COVID-19." E3S Web of Conferences 202 (2020): 13007. http://dx.doi.org/10.1051/e3sconf/202020213007.

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COVID-19 is an infectious disease that can spread from one person to another and has a high potential for death. The infection of COVID-19 is spreading massive and fast that causes the extreme fluctuating data spread and long memory effects. One of the ways in which the death of COVID-19 can be reduce is to produce a prediction model that could be used as a reference in taking countermeasures. There are various prediction models, from regression to Autoregressive Fractional Integrated Moving Average (ARIMA), but it still shows shortcomings when disturbances occur from extreme fluctuations and

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Kondo Lembang, Ferry, Lexy Janzen Sinay, and Asrul Irfanullah. "ARFIMA Modelling for Tectonic Earthquakes in The Maluku Region." Indonesian Journal of Statistics and Its Applications 5, no. 1 (2021): 39–49. http://dx.doi.org/10.29244/ijsa.v5i1p39-49.

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Maluku Province is one of the regions in Indonesia with a very active and very prone earthquake intensity because it is a meeting place for 3 (three) plates, namely the Eurasian, Pacific and Australian plates. In the last 100 years, the history of tectonic earthquakes with tsunamis that occurred in Indonesia was 25-30% occurring in the Maluku Sea and Banda Sea. Based on this fact, this study aims to analyze the incidence of tectonic earthquakes that occurred in the Maluku region and its surroundings using the Autoregressive Fractionally Integrated Moving Averages (ARFIMA) model which has the a

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Balagula, Y. "Forecasting daily spot prices in the Russian electricity market with the ARFIMA model." Applied Econometrics 57 (2020): 89–101. http://dx.doi.org/10.22394/1993-7601-2020-57-89-101.

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Karemera, David, and John Cole. "ARFIMA Tests for Random Walks in Exchange Rates in Asian, Latin American and African-Middle Eastern Markets." Review of Pacific Basin Financial Markets and Policies 13, no. 01 (2010): 1–18. http://dx.doi.org/10.1142/s0219091510001846.

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This article examines fractional processes as alternatives to random walks in emerging foreign exchange rate markets. Sowell's (1992) joint maximum likelihood is used to estimate the ARFIMA parameters and test for random walks. The results show that, in most cases, the emerging market exchange rates follow fractionally integrated processes. Forecasts of exchange rates based on the fractionally integrated autoregressive moving average models are compared to those from the benchmark random walk models. A Harvey, Leybourne and Newbold (1997) test of equality of forecast performance indicates that

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Chung, Ching-Fan. "Calculating and analyzing impulse responses for the vector ARFIMA model." Economics Letters 71, no. 1 (2001): 17–25. http://dx.doi.org/10.1016/s0165-1765(00)00399-2.

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Hauser, Michael A., and Robert M. Kunst. "Forecasting high-frequency financial data with the ARFIMA-ARCH model." Journal of Forecasting 20, no. 7 (2001): 501–18. http://dx.doi.org/10.1002/for.803.

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Nguyen, Trang, Taha Chaiechi, Lynne Eagle, and David Low. "Growth enterprise market in Hong Kong." Journal of Asian Business and Economic Studies 27, no. 1 (2019): 19–34. http://dx.doi.org/10.1108/jabes-01-2019-0009.

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Purpose Growth enterprise market (GEM) in Hong Kong is acknowledged as one of the world’s most successful examples of small and medium enterprise (SME) stock market. The purpose of this paper is to examine the evolving efficiency and dual long memory in the GEM. This paper also explores the joint impacts of thin trading, structural breaks and inflation on the dual long memory. Design/methodology/approach State-space GARCH-M model, Kalman filter estimation, factor-adjustment techniques and fractionally integrated models: ARFIMA–FIGARCH, ARFIMA–FIAPARCH and ARFIMA–HYGARCH are adopted for the emp

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Graves, T., R. B. Gramacy, C. L. E. Franzke, and N. W. Watkins. "Efficient Bayesian inference for ARFIMA processes." Nonlinear Processes in Geophysics Discussions 2, no. 2 (2015): 573–618. http://dx.doi.org/10.5194/npgd-2-573-2015.

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Abstract. Many geophysical quantities, like atmospheric temperature, water levels in rivers, and wind speeds, have shown evidence of long-range dependence (LRD). LRD means that these quantities experience non-trivial temporal memory, which potentially enhances their predictability, but also hampers the detection of externally forced trends. Thus, it is important to reliably identify whether or not a system exhibits LRD. In this paper we present a modern and systematic approach to the inference of LRD. Rather than Mandelbrot's fractional Gaussian noise, we use the more flexible Autoregressive F

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Bazhenov, T., and D. Fantazzini. "Forecasting Realized Volatility of Russian stocks using Google Trends and Implied Volatility." Russian Journal of Industrial Economics 12, no. 1 (2019): 79–88. http://dx.doi.org/10.17073/2072-1633-2019-1-79-88.

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This work proposes to forecast the Realized Volatility (RV) and the Value-at-Risk (VaR) of the most liquid Russian stocks using GARCH, ARFIMA and HAR models, including both the implied volatility computed from options prices and Google Trends data. The in-sample analysis showed that only the implied volatility had a significant effect on the realized volatility across most stocks and estimated models, whereas Google Trends did not have any significant effect. The outof-sample analysis highlighted that models including the implied volatility improved their forecasting performances, whereas mode

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Fofana, Souleymane, Aliou Diop, and Ouagnina HILI. "Modeling of nonstationarity and long memory with RS-ARFIMA-GARCH model." African Journal of Applied Statistics 5, no. 2 (2018): 469–87. http://dx.doi.org/10.16929/ajas/469.225.

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Jaehwan Park and 김현숙. "Long Memory in LME Volatility through the ARFIMA and FIGARCH Model." Korean Journal of Financial Engineering 15, no. 4 (2016): 29–52. http://dx.doi.org/10.35527/kfedoi.2016.15.4.002.

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Liu Juan and Gao Jie. "Long-memory ARFIMA model for DNA sequences of influenza A virus." Acta Physica Sinica 60, no. 4 (2011): 048702. http://dx.doi.org/10.7498/aps.60.048702.

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Kwan, Wilson, Wai Keung Li, and Guodong Li. "On the estimation and diagnostic checking of the ARFIMA–HYGARCH model." Computational Statistics & Data Analysis 56, no. 11 (2012): 3632–44. http://dx.doi.org/10.1016/j.csda.2010.07.010.

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Chaâbane, Najeh. "A hybrid ARFIMA and neural network model for electricity price prediction." International Journal of Electrical Power & Energy Systems 55 (February 2014): 187–94. http://dx.doi.org/10.1016/j.ijepes.2013.09.004.

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Polotzek, Katja, and Holger Kantz. "An ARFIMA-based model for daily precipitation amounts with direct access to fluctuations." Stochastic Environmental Research and Risk Assessment 34, no. 10 (2020): 1487–505. http://dx.doi.org/10.1007/s00477-020-01833-w.

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Abstract Correlations in models for daily precipitation are often generated by elaborate numerics that employ a high number of hidden parameters. We propose a parsimonious and parametric stochastic model for European mid-latitude daily precipitation amounts with focus on the influence of correlations on the statistics. Our method is meta-Gaussian by applying a truncated-Gaussian-power (tGp) transformation to a Gaussian ARFIMA model. The speciality of this approach is that ARFIMA(1, d, 0) processes provide synthetic time series with long- (LRC), meaning the sum of all autocorrelations is infini

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Çatık, A. Nazif, and Mehmet Karaçuka. "A COMPARATIVE ANALYSIS OF ALTERNATIVE UNIVARIATE TIME SERIES MODELS IN FORECASTING TURKISH INFLATION." Journal of Business Economics and Management 13, no. 2 (2012): 275–93. http://dx.doi.org/10.3846/16111699.2011.620135.

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This paper analyses inflation forecasting power of artificial neural networks with alternative univariate time series models for Turkey. The forecasting accuracy of the models is compared in terms of both static and dynamic forecasts for the period between 1982:1 and 2009:12. We find that at earlier forecast horizons conventional models, especially ARFIMA and ARIMA, provide better one-step ahead forecasting performance. However, unobserved components model turns out to be the best performer in terms of dynamic forecasts. The superiority of the unobserved components model suggests that inflatio

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Xie, Chi, Zhou Mao, and Gang-Jin Wang. "Forecasting RMB Exchange Rate Based on a Nonlinear Combination Model of ARFIMA, SVM, and BPNN." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/635345.

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There are various models to predict financial time series like the RMB exchange rate. In this paper, considering the complex characteristics of RMB exchange rate, we build a nonlinear combination model of the autoregressive fractionally integrated moving average (ARFIMA) model, the support vector machine (SVM) model, and the back-propagation neural network (BPNN) model to forecast the RMB exchange rate. The basic idea of the nonlinear combination model (NCM) is to make the prediction more effective by combining different models’ advantages, and the weight of the combination model is determined

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Mah, Pauline Jin Wee, and Nur Nadhirah Nanyan. "A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA." MALAYSIAN JOURNAL OF COMPUTING 5, no. 1 (2020): 374. http://dx.doi.org/10.24191/mjoc.v5i1.6760.

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The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressi

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Otieno, Donald A., Rose W. Ngugi, and Nelson H. W. Wawire. "Effects of Interest Rate on Stock Market Returns in Kenya." International Journal of Economics and Finance 9, no. 8 (2017): 40. http://dx.doi.org/10.5539/ijef.v9n8p40.

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Debate on the stochastic behaviour of stock market returns, 3-month Treasury Bills rate, lending rate and their cointegrating residuals remains unsettled. This study examines the stochastic properties of the macroeconomic variables, stock market returns and their cointegrating residuals using an Autoregressive Fractionally Integrated Moving Average (ARFIMA) model. It also investigates Granger causality between the two measures of interest rate and stock market returns. The study uses monthly data from 1st January 1993 to 31st December 2015. The results indicate that the 3-month Treasury Bills

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Derbentsev, Vasily, Natalia Datsenko, Olga Stepanenko, and Vitaly Bezkorovainyi. "Forecasting cryptocurrency prices time series using machine learning approach." SHS Web of Conferences 65 (2019): 02001. http://dx.doi.org/10.1051/shsconf/20196502001.

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This paper describes the construction of the short-term forecasting model of cryptocurrencies’ prices using machine learning approach. The modified model of Binary Auto Regressive Tree (BART) is adapted from the standard models of regression trees and the data of the time series. BART combines the classic algorithm classification and regression trees (C&RT) and autoregressive models ARIMA. Using the BART model, we made a short-term forecast (from 5 to 30 days) for the 3 most capitalized cryptocurrencies: Bitcoin, Ethereum and Ripple. We found that the proposed approach was more accurate th

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Sukono, F., Eman Lesmana, Dwi Susanti, Herlina Napitupulu, and Yuyun Hidayat. "Estimation of Value-at-Risk Adjusted under the Capital Asset Pricing Model Based on ARMAX-GARCH Approach." Jurnal Matematika Integratif 15, no. 1 (2019): 29. http://dx.doi.org/10.24198/jmi.v15.n1.20931.29-37.

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Investors having an understanding of investment statistics are important. Especially quantitative tools related to investment risk measurement. Value-at-Risk Adjusted is one of the investment risk measurement tools, which assumes that returns are not normally distributed.This paper intends to measure investment risk based onValue-at-Risk Adjustedor called Modified Value-at-Risk under the Capital Asset Pricing Model. It is assumed that the return of the market index has a non-constant average and there is a long memory effect. The average of the return of the market index is estimated using ARF

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45

Sukono, F., Eman Lesmana, Dwi Susanti, Herlina Napitupulu, and Yuyun Hidayat. "Estimation of Value-at-Risk Adjusted under the Capital Asset Pricing Model Based on ARMAX-GARCH Approach." Jurnal Matematika Integratif 15, no. 1 (2019): 29. http://dx.doi.org/10.24198/jmi.v15i1.20931.

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Abstract:

Investors having an understanding of investment statistics are important. Especially quantitative tools related to investment risk measurement. Value-at-Risk Adjusted is one of the investment risk measurement tools, which assumes that returns are not normally distributed.This paper intends to measure investment risk based onValue-at-Risk Adjustedor called Modified Value-at-Risk under the Capital Asset Pricing Model. It is assumed that the return of the market index has a non-constant average and there is a long memory effect. The average of the return of the market index is estimated using ARF

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Olatayo, T. O., and A. F. Adedotun. "On the test and estimation of fractional parameter in ARFIMA model: bootstrap approach." Applied Mathematical Sciences 8 (2014): 4783–92. http://dx.doi.org/10.12988/ams.2014.46498.

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Sena, M. R., V. A. Reisen, and S. R. C. Lopes. "Correlated Errors in the Parameters Estimation of the ARFIMA Model: A Simulated Study." Communications in Statistics - Simulation and Computation 35, no. 3 (2006): 789–802. http://dx.doi.org/10.1080/03610910600716928.

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Mootamri, Imène. "Long Memory Process in Asset Returns with Multivariate GARCH Innovations." Economics Research International 2011 (September 7, 2011): 1–15. http://dx.doi.org/10.1155/2011/564952.

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The main purpose of this paper is to consider the multivariate GARCH (MGARCH) framework to model the volatility of a multivariate process exhibiting long-term dependence in stock returns. More precisely, the long-term dependence is examined in the first conditional moment of US stock returns through multivariate ARFIMA process, and the time-varying feature of volatility is explained by MGARCH models. An empirical application to the returns series is carried out to illustrate the usefulness of our approach. The main results confirm the presence of long memory property in the conditional mean of

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Yong, Jaime, and Anh Khoi Pham. "The long-term linkages between direct and indirect property in Australia." Journal of Property Investment & Finance 33, no. 4 (2015): 374–92. http://dx.doi.org/10.1108/jpif-01-2015-0005.

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Purpose– Investment in Australia’s property market, whether directly or indirectly through Australian real estate investment trusts (A-REITs), grew remarkably since the 1990s. The degree of segregation between the property market and other financial assets, such as shares and bonds, can influence the diversification benefits within multi-asset portfolios. This raises the question of whether direct and indirect property investments are substitutable. Establishing how information transmits between asset classes and impacts the predictability of returns is of interest to investors. The paper aims

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WAN Yong, ZHANG Dayong, and JIANG Zhenhuan. "Further Evidence of Long Memory in China��s Stock Market Based on ARFIMA Model." International Journal of Digital Content Technology and its Applications 7, no. 2 (2013): 612–21. http://dx.doi.org/10.4156/jdcta.vol7.issue2.75.

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