scholarly journals The Performance of ARIMAX Model and Vector Autoregressive (VAR) Model in Forecasting Strategic Commodity Price in Indonesia

2017 ◽  
Vol 124 ◽  
pp. 189-196 ◽  
Author(s):  
Wiwik Anggraeni ◽  
Kuntoro Boga Andri ◽  
Sumaryanto ◽  
Faizal Mahananto
Keyword(s):  
Commodity Price ◽  
Var Model ◽  
Vector Autoregressive

MORTALITY FORECASTING WITH A SPATIALLY PENALIZED SMOOTHED VAR MODEL

2020 ◽  
pp. 1-29
Author(s):  
Le Chang ◽  
Yanlin Shi
Keyword(s):  
Population Data ◽  
Coefficient Matrix ◽  
Gradient Algorithm ◽  
Var Model ◽  
Vector Autoregressive ◽  
Mortality Forecasting ◽  
Dependency Structure ◽  
Svar Model ◽  
The Uk ◽  
Sparsity Structure

Abstract This paper investigates a high-dimensional vector-autoregressive (VAR) model in mortality modeling and forecasting. We propose an extension of the sparse VAR (SVAR) model fitted on the log-mortality improvements, which we name “spatially penalized smoothed VAR” (SSVAR). By adaptively penalizing the coefficients based on the distances between ages, SSVAR not only allows a flexible data-driven sparsity structure of the coefficient matrix but simultaneously ensures interpretable coefficients including cohort effects. Moreover, by incorporating the smoothness penalties, divergence in forecast mortality rates of neighboring ages is largely reduced, compared with the existing SVAR model. A novel estimation approach that uses the accelerated proximal gradient algorithm is proposed to solve SSVAR efficiently. Similarly, we propose estimating the precision matrix of the residuals using a spatially penalized graphical Lasso to further study the dependency structure of the residuals. Using the UK and France population data, we demonstrate that the SSVAR model consistently outperforms the famous Lee–Carter, Hyndman–Ullah, and two VAR-type models in forecasting accuracy. Finally, we discuss the extension of the SSVAR model to multi-population mortality forecasting with an illustrative example that demonstrates its superiority in forecasting over existing approaches.

MODELING THE INTERACTIONS OF STOCK PRICE AND EXCHANGE RATE IN MALAYSIA

2009 ◽  
Vol 54 (04) ◽  
pp. 605-619 ◽  
Author(s):  
MOHD TAHIR ISMAIL ◽  
ZAIDI BIN ISA
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Stock Prices ◽  
Stock Price ◽  
Price Index ◽  
Composite Index ◽  
Regime Shifts ◽  
Var Model ◽  
Vector Autoregressive ◽  
Stock Price Index

After the East Asian crisis in 1997, the issue of whether stock prices and exchange rates are related or not have received much attention. This is due to realization that during the crisis the countries affected saw turmoil in both their currencies and stock markets. This paper studies the non-linear interactions between stock price and exchange rate in Malaysia using a two regimes multivariate Markov switching vector autoregression (MS-VAR) model with regime shifts in both the mean and the variance. In the study, the Kuala Lumpur Composite Index (KLCI) and the exchange rates of Malaysia ringgit against four other countries namely the Singapore dollar, the Japanese yen, the British pound sterling and the Australian dollar between 1990 and 2005 are used. The empirical results show that all the series are not cointegrated but the MS-VAR model with two regimes manage to detect common regime shifts behavior in all the series. The estimated MS-VAR model reveals that as the stock price index falls the exchange rates depreciate and when the stock price index gains the exchange rates appreciate. In addition, the MS-VAR model fitted the data better than the linear vector autoregressive model (VAR).

scholarly journals Forecasting Cattle Prices in the Presence of Structural Change

1992 ◽  
Vol 24 (2) ◽  
pp. 11-22 ◽  
Author(s):  
Barry K. Goodwin
Keyword(s):  
Structural Change ◽  
A Priori ◽  
Arima Model ◽  
Var Model ◽  
Impulse Response Functions ◽  
Cattle Industry ◽  
Vector Autoregressive ◽  
Economic Relationships ◽  
New Information ◽  
Out Of Sample

AbstractRecent empirical research and developments in the cattle industry suggest several reasons to suspect structural change in economic relationships determining cattle prices. Standard forecasting models may ignore structural change and may produce biased and misleading forecasts. Vector autoregressive (VAR) models that allow parameters to vary with time are used to forecast quarterly cattle prices. The VAR procedures are flexible in that they allow the identification of structural change that begins at an a priori unknown point and occurs gradually. The results indicate that the lowest RMSE for out-of-sample forecasts of cattle prices is obtained using a gradually switching VAR model. However, differences between the gradually switching VAR model and a univariate ARIMA model are not strongly significant. Impulse response functions indicate that adjustments of cattle prices to new information have become faster in recent years.

Can energy commodities affect energy blockchain-based cryptos?

2019 ◽  
Vol 36 (4) ◽  
pp. 682-699 ◽  
Author(s):  
Ikhlaas Gurrib
Keyword(s):  
Natural Gas ◽  
Crude Oil ◽  
Autoregressive Model ◽  
Structural Breaks ◽  
Price Index ◽  
Principal Component ◽  
Commodity Price ◽  
Var Model ◽  
Content Type ◽  
Heating Oil

Purpose The purpose of this paper is to shed fresh light into whether an energy commodity price index (ENFX) and energy blockchain-based crypto price index (ENCX) can be used to predict movements in the energy commodity and energy crypto market. Design/methodology/approach Using principal component analysis over daily data of crude oil, heating oil, natural gas and energy based cryptos, the ENFX and ENCX indices are constructed, where ENFX (ENCX) represents 94% (88%) of variability in energy commodity (energy crypto) prices. Findings Natural gas price movements were better explained by ENCX, and shared positive (negative) correlations with cryptos (crude oil and heating oil). Using a vector autoregressive model (VAR), while the 1-day lagged ENCX (ENFX) was significant in estimating current ENCX (ENFX) values, only lagged ENCX was significant in estimating current ENFX. Granger causality tests confirmed the two markets do not granger cause each other. One standard deviation shock in ENFX had a negative effect on ENCX. Weak forecasting results of the VAR model, support the two markets are not robust forecasters of each other. Robustness wise, the VAR model ranked lower than an autoregressive model, but higher than a random walk model. Research limitations/implications Significant structural breaks at distinct dates in the two markets reinforce that the two markets do not help to predict each other. The findings are limited by the existence of bubbles (December 2017-January 2018) which were witnessed in energy blockchain-based crypto markets and natural gas, but not in crude oil and heating oil. Originality/value As per the authors’ knowledge, this is the first paper to analyze the relationship between leading energy commodities and energy blockchain-based crypto markets.

VECTOR AUTOREGRESSIVE PROCESSES WITH NONLINEAR TIME TRENDS IN COINTEGRATING RELATIONS

2001 ◽  
Vol 5 (4) ◽  
pp. 577-597 ◽  
Author(s):  
Antti Ripatti ◽  
Pentti
Keyword(s):  
Structural Changes ◽  
Asymptotic Theory ◽  
Var Model ◽  
Level Shift ◽  
Autoregressive Processes ◽  
Short Term ◽  
Vector Autoregressive ◽  
Market Rate ◽  
Nonlinear Trends ◽  
Vector Autoregressive Processes

We extend the conventional cointegrated VAR model to allow for general nonlinear deterministic trends. These nonlinear trends can be used to model gradual structural changes in the intercept term of the cointegrating relations. A general asymptotic theory of estimation and statistical inference is reviewed and a diagnostic test for the correct specification of an employed nonlinear trend is developed. The methods are applied to Finnish interest-rate data. A smooth level shift of the logistic form between the own-yield of broad money and the short-term money market rate is found appropriate for these data. The level shift is motivated by the deregulation of issuing certificates of deposit and its inclusion in the model solves the puzzle of the “missing cointegration vector” found in a previous study.

scholarly journals The Trivariate Causality among Education, Health, and Economic Growth in Zimbabwe

2020 ◽  
Vol 12 (4) ◽  
pp. 1357
Author(s):  
Michael Takudzwa Pasara ◽  
Tapiwa Kelvin Mutambirwa ◽  
Nolutho Diko
Keyword(s):  
Economic Growth ◽  
Granger Causality ◽  
Transmission Mechanism ◽  
Feedback Effect ◽  
Health Policies ◽  
Var Model ◽  
Vector Autoregressive ◽  
Education And Health ◽  
Causality Tests ◽  
Granger Causality Tests

This study investigated the causality among education, health, and economic growth in Zimbabwe. Causality effects are a thinly explored area in literature, with most studies focusing on bidirectional relationships. Granger causality tests were employed in a Vector autoregressive (VAR) model. Results showed that education Granger causes health improvements, with health improvements in turn fairly associating to Granger cause economic growth in Zimbabwe. Thus, the effect of education on economic growth is not direct, but works through improved health, pointing to the conclusion that health is a transmission mechanism through which education drives economic growth. No feedback effect was established from health to education and from economic growth to education and health. Thus, results suggest the need for a holistic policy approach which integrates education and health policies in a bid to drive economic growth, since education has no effect on economic growth in its own domain, but through health.

HOW DOES BLOCKCHAIN PARTICIPATION AFFECT THE CRYPTOCURRENCY MARKET?

2020 ◽  
pp. 1-24
Author(s):  
YI LI ◽  
WEI ZHANG ◽  
PENGFEI WANG
Keyword(s):  
Trading Volume ◽  
Positive Impact ◽  
Granger Causality Test ◽  
Realized Volatility ◽  
Var Model ◽  
Alternative Measure ◽  
Theoretical Studies ◽  
Causality Test ◽  
Vector Autoregressive ◽  
The Impact

Taking the unique advantage of the cryptocurrency market setting, this paper examines the relationships between blockchain participation and returns, trading volume and realized volatility of main cryptocurrencies (i.e., Bitcoin, Ethereum and Litecoin). Dissimilar to previous theoretical studies that model the influencing factors on participation, we employ the number of unique from addresses 1 as the proxy for cryptocurrency investors’ blockchain participation and further explore the impact of such participation. By using vector autoregressive (VAR) model, we find that the blockchain participation has a significant and positive impact on the next day’s trading volume and realized volatility for the main cryptocurrencies. Our results are robust to the Granger causality test and alternative measure for blockchain participation.

The impact of macroeconomic uncertainty on international commodity prices

2017 ◽  
Vol 7 (2) ◽  
pp. 163-184 ◽  
Author(s):  
Xiaofen Tan ◽  
Yongjiao Ma
Keyword(s):  
Raw Materials ◽  
Commodity Prices ◽  
Precious Metals ◽  
Commodity Price ◽  
Content Type ◽  
Vector Autoregressive ◽  
Macroeconomic Uncertainty ◽  
Wide Range ◽  
The Impact ◽  
Low Uncertainty

Purpose The purpose of this paper is to empirically analyze the impact of macroeconomic uncertainty on a large sample of 19 commodity markets. Design/methodology/approach The authors rely on Jurado et al.’s (2015) measure of macroeconomic uncertainty based on a wide range of monthly macroeconomic and financial indicators and estimate a threshold VAR model to assess whether the impact of macroeconomic uncertainty on commodity prices differs under the high- or low-uncertainty state. Findings The findings show that positive macroeconomic uncertainty shocks affect commodity prices returns negatively on average and the impact of macroeconomic uncertainty is generally higher in high-uncertainty states compared with low-uncertainty states. Besides, although the safe-haven role of precious metals is confirmed, energy and industrial markets are more sensitive to short-run and long-run macroeconomic uncertainty, respectively. Research limitations/implications The findings in this study suggest that commodity prices reflect not only the level of economic fundamental but also the volatility of economic fundamental. Originality/value This study empirically analyzes and verifies the influence of macroeconomic uncertainty not only on oil prices but also on four groups of 19 raw materials. As for the methodological issues, the authors rely on a structural threshold vector autoregressive specification for modeling commodity price returns to account for potentially different effects depending on the macroeconomic uncertainty states.

Yet more on a stochastic economic model: Part 5: a vector autoregressive (VAR) Model for retail prices and wages

2018 ◽  
Vol 13 (1) ◽  
pp. 92-108 ◽  
Author(s):  
A. D. Wilkie ◽  
Şule Şahin
Keyword(s):  
Economic Model ◽  
Autoregressive Model ◽  
Original Model ◽  
Var Model ◽  
Vector Autoregressive Model ◽  
Slight Improvement ◽  
Vector Autoregressive ◽  
Retail Prices

AbstractIn this paper we develop a vector autoregressive model for retail prices and wages within the Wilkie model. The results turn out to be a slight improvement over the original model, but the simulated results are not very different.

scholarly journals Observed Interaction between Pacific Sea Ice and the Western Pacific Pattern on Intraseasonal Time Scales

2011 ◽  
Vol 24 (19) ◽  
pp. 5031-5042 ◽  
Author(s):  
N. Joss Matthewman ◽  
Gudrun Magnusdottir
Keyword(s):  
Sea Ice ◽  
Bering Sea ◽  
Time Scale ◽  
Western Pacific ◽  
Var Model ◽  
Short Time Scale ◽  
Vector Autoregressive ◽  
Induced Changes ◽  
Short Time ◽  
The Western Pacific

The relationship between North Pacific sea ice and the Western Pacific (WP) pattern is examined using wintertime observational data between 1978 and 2008. Weekly averaged data are chosen to capture the characteristically short time scale of the WP. A clear relationship is found between the WP and sea ice concentrations in the Bering Sea, where the positive polarity of the WP is accompanied by increasing sea ice concentrations and the negative WP by decreasing sea ice concentrations. Sea ice concentrations in the Sea of Okhotsk, however, are shown to be largely insensitive to the strength of the WP. Feedback of Bering Sea sea ice concentrations onto the WP is tested by fitting weekly averaged observations to a vector autoregressive (VAR) model. Results from the VAR model indicate that feedback of Bering Sea sea ice onto the WP plays a significant role in the dynamics of the WP and that this feedback is positive; that is, WP-induced changes in Bering Sea sea ice concentrations help sustain existing WP conditions, thereby lengthening the time scale of variability of the WP.

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