scholarly journals Modeling and Prediction of the Volatility of the Freight Rate in the Roadway Freight Market of China

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Wei Xiao ◽  
Mi Gan ◽  
Hongling Liu ◽  
Xiaobo Liu
Keyword(s):  
Moving Average ◽  
Freight Rate ◽  
Autoregressive Integrated Moving Average ◽  
Out Of Sample ◽  
Egarch Model ◽  
Trend Forecasting ◽  
Asymmetric Garch ◽  
Dynamic Time ◽  
Freight Market ◽  
Trucking Sector

The trucking sector is an essential part of the logistic system in China, carrying more than 80% of its goods. The complexity of the trucking market leads to tremendous uncertainty in the market volatility. Hence, in this highly competitive and vital market, trend forecasting is extremely difficult owing to the volatility of the freight rate. Consequently, there is interest in accurately forecasting the freight volatility for truck transportation. In this study, to represent the degree of variation of a freight rate series in the trucking sector over time, we first introduce truck rate volatility (TRV). This investigation utilizes the generalized autoregressive conditional heteroskedasticity (GARCH) family of methods to estimate the dynamic time-varying TRV using the real trucking industry transaction data obtained from an online freight exchange (OFEX) platform. It explores the ability of forecasting with and without reestimation at each step of the conventional GARCH models, a neural network exponential GARCH (NN-EGARCH) model, and a traditional forecasting technique, the autoregressive integrated moving average (ARIMA) approach. The empirical results from the southwest China trucking data indicate that the asymmetric GARCH-type models capture the characteristics of the TRV better than those with Gaussian distributions and that the leverage effects are observed in the TRV. Furthermore, the NN-EGARCH performs better in in-sample forecasting than other methods, whereas ARIMA performs similarly in out-of-sample TRV forecasting with reestimation. However, the Diebold–Mariano test indicates the better forecasting ability of ARIMA than the NN-EGARCH in the out-of-sample periods. The findings of this study can benefit truckers and shippers to capture the tendency change of the market to conduct their business plan, increase their look-to-buy rate, and avoid market risk.

scholarly journals Are Combined Tourism Forecasts Better at Minimizing Forecasting Errors?

Forecasting ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 211-229
Author(s):  
Ulrich Gunter ◽  
Irem Önder ◽  
Egon Smeral
Keyword(s):  
Moving Average ◽  
Tourism Demand ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Forecast Combinations ◽  
The Stability ◽  
Forecasting Errors ◽  
Macroeconomic Environment

This study, which was contracted by the European Commission and is geared towards easy replicability by practitioners, compares the accuracy of individual and combined approaches to forecasting tourism demand for the total European Union. The evaluation of the forecasting accuracies was performed recursively (i.e., based on expanding estimation windows) for eight quarterly periods spanning two years in order to check the stability of the outcomes during a changing macroeconomic environment. The study sample includes Eurostat data from January 2005 until August 2017, and out of sample forecasts were calculated for the last two years for three and six months ahead. The analysis of the out-of-sample forecasts for arrivals and overnights showed that forecast combinations taking the historical forecasting performance of individual approaches such as Autoregressive Integrated Moving Average (ARIMA) models, REGARIMA models with different trend variables, and Error Trend Seasonal (ETS) models into account deliver the best results.

scholarly journals Forecasting the Finnish house price returns and volatility: a comparison of time series models

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Josephine Dufitinema
Keyword(s):  
Moving Average ◽  
House Price ◽  
Arma Model ◽  
Price Volatility ◽  
Data Set ◽  
Content Type ◽  
Out Of Sample ◽  
Egarch Model ◽  
House Price Volatility ◽  
Arfima Model

Purpose The purpose of this paper is to compare different models’ performance in modelling and forecasting the Finnish house price returns and volatility. Design/methodology/approach The competing models are the autoregressive moving average (ARMA) model and autoregressive fractional integrated moving average (ARFIMA) model for house price returns. For house price volatility, the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model is competing with the fractional integrated GARCH (FIGARCH) and component GARCH (CGARCH) models. Findings Results reveal that, for modelling Finnish house price returns, the data set under study drives the performance of ARMA or ARFIMA model. The EGARCH model stands as the leading model for Finnish house price volatility modelling. The long memory models (ARFIMA, CGARCH and FIGARCH) provide superior out-of-sample forecasts for house price returns and volatility; they outperform their short memory counterparts in most regions. Additionally, the models’ in-sample fit performances vary from region to region, while in some areas, the models manifest a geographical pattern in their out-of-sample forecasting performances. Research limitations/implications The research results have vital implications, namely, portfolio allocation, investment risk assessment and decision-making. Originality/value To the best of the author’s knowledge, for Finland, there has yet to be empirical forecasting of either house price returns or/and volatility. Therefore, this study aims to bridge that gap by comparing different models’ performance in modelling, as well as forecasting the house price returns and volatility of the studied market.

scholarly journals A Correcting Note on Forecasting Conditional Variance Using ARIMA vs. GARCH Model

2019 ◽  
Vol 11 (5) ◽  
pp. 145
Author(s):  
Mohammad Naim Azimi ◽  
Seyed Farhad Shahidzada
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Conditional Variance ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Out Of Sample ◽  
Moving Average Model ◽  
Catch Up ◽  
Autoregressive Conditional Heteroscedasticity

In this study, we demonstrate that a common approach in using the Autoregressive Integrated Moving Average model is not efficient to forecast all types of time series data and most specially, the out-of-sample forecasting of the time series that exhibits clustering volatility. This gap leads to introduce a competing model to catch up with the clustering volatility and conditional variance for which, we empirically document the efficient and lower error use of the Generalized Autoregressive Conditional Heteroscedasticity model instead.

Forecasting disaggregated tourist arrivals in Croatia

2016 ◽  
Vol 23 (1) ◽  
pp. 78-98 ◽  
Author(s):  
Nicholas Apergis ◽  
Andrea Mervar ◽  
James E. Payne
Keyword(s):  
Fourier Transformation ◽  
Moving Average ◽  
The Other ◽  
Autoregressive Moving Average ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Models ◽  
Seasonal Time Series ◽  
Out Of Sample ◽  
The City

This study examines the performance of four alternative univariate seasonal time series forecasting models (seasonal autoregressive integrated moving average [SARIMA], SARIMA with Fourier transformation, ARAR, and fractionally integrated autoregressive-moving average) of tourist arrivals to 20 Croatian counties and the City of Zagreb. Both in-sample and out-of-sample forecasts reveal that the SARIMA model with Fourier transformation consistently outperforms the other models across the respective regions investigated.

Automatic Identification of Autoregressive Integrated Moving Average Time Series

1982 ◽  
Vol 14 (3) ◽  
pp. 156-166 ◽  
Author(s):  
Chin-Sheng Alan Kang ◽  
David D. Bedworth ◽  
Dwayne A. Rollier
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Automatic Identification ◽  
Autoregressive Integrated Moving Average

PREDIKSI HARGA SAHAM PT. BRI, Tbk. MENGGUNAKAN METODE ARIMA (Autoregressive Integrated Moving Average)

2014 ◽  
Vol 14 (2) ◽  
pp. 60
Author(s):  
Greis S Lilipaly ◽  
Djoni Hatidja ◽  
John S Kekenusa
Keyword(s):  
Stock Price ◽  
Moving Average ◽  
Secondary Data ◽  
Research Use ◽  
Autoregressive Integrated Moving Average ◽  
Minimum Data ◽  
Research Show

PREDIKSI HARGA SAHAM PT. BRI, Tbk. MENGGUNAKAN METODE ARIMA (Autoregressive Integrated Moving Average) Greis S. Lilipaly1) , Djoni Hatidja1) , John S. Kekenusa1) ABSTRAK Metode ARIMA adalah salah satu metode yang dapat digunakan dalam memprediksi perubahan harga saham. Tujuan dari penelitian ini adalah untuk membuat model ARIMA dan memprediksi harga saham PT. BRI, Tbk. bulan November 2014. Penelitian menggunakan data harga saham  harian  maksimum dan minimum PT. BRI, Tbk. Data yang digunakan yaitu data sekunder yang diambil dari website perusahaan PT. BRI, Tbk. sejak 3 Januari 2011 sampai 20 Oktober 2014 untuk memprediksi harga saham bulan November 2014. Dari hasil penelitian menunjukkan bahwa data tahun 2011 sampai Oktober 2014 bisa digunakan untuk memprediksi harga saham bulan November 2014. Hasilnya model ARIMA untuk harga saham maksimum adalah ARIMA (2,1,3) dan harga saham minimum adalah model (2,1,3) yang dapat digunakan untuk memprediksi data bulan November 2014 dengan validasi prediksi yang diambil pada bulan Oktober 2014 untuk selanjutnya dilakukan prediksi bulan November 2014. Kata Kunci: Metode ARIMA, PT. BRI, Tbk., Saham THE PREDICTION STOCK PRICE OF PT. BRI, Tbk. USE ARIMA METHOD (Autoregressive Integrated Moving Average) ABSTRACT ARIMA method is one of the method that used to prediction the change of stock price. The purpose of this research is to make model of ARIMA and predict stock price of PT. BRI, Tbk. in November 2014. The research use maximum and minimum data of stock price daily of PT. BRI, Tbk. Data are used is secondary data that taking from website of PT. BRI, Tbk. since January 3rd 2011 until October 20th 2014 to predict stock price in November 2014. From this research show that data from 2011 until October 2014 can be used to predict the stock price in November 2014. The result of ARIMA’s model for the maximum stock price is ARIMA (2,1,3) and the minimum stock price is (2,1,3) can use to predict the data on November 2014 with predict validation that take on October 2014 and with that predict November 2014. Keywords: ARIMA method, PT. BRI, Tbk., Stock

Hugh Jackman vs. Skin Cancer Awareness Month: Promoting Public Interest in Basal Cell Carcinoma (Preprint)

2020 ◽  
Author(s):  
Trevor Torgerson ◽  
Jennifer Austin ◽  
Jam Khojasteh ◽  
Matt Vassar
Keyword(s):  
Social Media ◽  
Skin Cancer ◽  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Basal Cell ◽  
Moving Average ◽  
Public Awareness ◽  
Sun Exposure ◽  
Cancer Awareness ◽  
Autoregressive Integrated Moving Average

BACKGROUND Public awareness for BCC is particularly important, as its major risk factors — increased sun exposure and number of sunburns — are largely preventable. OBJECTIVE Determine whether social media posts from celebrities has an affect on public awareness of basal cell carcinoma. METHODS We used Google Trends to investigate whether public awareness for basal cell carcinoma (BCC) increased following social media posts from Hugh Jackman. To forecast the expected search interest for BCC, melanoma and sunscreen in the event that each celebrity had not posted on social media, we used the autoregressive integrated moving average (ARIMA) algorithm. RESULTS We found that social media posts from Hugh Jackman, a well-known actor, increased relative search interest above the expected search interest calculated using an ARIMA forecasting model. CONCLUSIONS Our results also suggest that increasing awareness by Skin Cancer Awareness Month may be less effective for BCC, but a celebrity spokesperson has the potential to increase awareness. BCC is largely preventable, so increasing awareness could lead to a decrease in incidence.

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

Sign in / Sign up

Export Citation Format

Share Document