Testing for bubbles in agriculture commodity markets

2016 ◽  
Vol 16 (1) ◽  
pp. 59 ◽  
Author(s):  
Francisco José Areal ◽  
Kevin Balcombe ◽  
George Rapsomanikis
Keyword(s):  
Time Series ◽  
Commodity Markets ◽  
Raw Material ◽  
Future Price ◽  
Agricultural Commodities ◽  
Food Markets ◽  
Price Bubbles ◽  
Monthly Time Series ◽  
Augmented Dickey Fuller ◽  
Food Commodity

<p class="Num-DocParagraph">We apply the recent generalized sup augmented Dickey-Fuller (GSADF) test for explosive bubbles (Phillips <em>et al.</em>, 2012) to monthly time-series for food, beverages, agricultural raw material, cereals, dairy, meat, oils and sugar indices and a total of 28 agricultural commodities between 1980-2012. We found price bubbles occurred for 6 out of the 10 indices studied and for 6 out of the 28 commodities within food markets. Results from the tests can help implementing policies aimed at mitigating effects of future price bubbles to targeted food commodity markets that may require special attention.</p>

scholarly journals Testing for bubbles in agriculture commodity markets

2016 ◽  
Vol 16 (1) ◽  
pp. 59 ◽  
Author(s):  
Francisco José Areal ◽  
Kevin Balcombe ◽  
George Rapsomanikis
Keyword(s):  
Time Series ◽  
Commodity Markets ◽  
Raw Material ◽  
Future Price ◽  
Agricultural Commodities ◽  
Food Markets ◽  
Price Bubbles ◽  
Monthly Time Series ◽  
Augmented Dickey Fuller ◽  
Food Commodity

<p class="Num-DocParagraph">We apply the recent generalized sup augmented Dickey-Fuller (GSADF) test for explosive bubbles (Phillips <em>et al.</em>, 2012) to monthly time-series for food, beverages, agricultural raw material, cereals, dairy, meat, oils and sugar indices and a total of 28 agricultural commodities between 1980-2012. We found price bubbles occurred for 6 out of the 10 indices studied and for 6 out of the 28 commodities within food markets. Results from the tests can help implementing policies aimed at mitigating effects of future price bubbles to targeted food commodity markets that may require special attention.</p>

scholarly journals Do natural rubber price bubbles occur?

2019 ◽  
Vol 65 (No. 2) ◽  
pp. 67-73 ◽  
Author(s):  
Chi-Wei Su ◽  
Lu Liu ◽  
Ran Tao ◽  
Oana-Ramona Lobonţ
Keyword(s):  
Time Series ◽  
Natural Rubber ◽  
Supply And Demand ◽  
Oil Prices ◽  
Climatic Changes ◽  
Price Bubbles ◽  
Augmented Dickey Fuller ◽  
Multiple Bubbles ◽  
Market Fundamentals ◽  
The Stability

In this paper, we employ the Generalized Supremum Augmented Dickey-Fuller test in order to identify the existence of multiple bubbles in natural rubber. This approach is practical for the using of time series and identifies the beginning and end points of multiple bubbles. The results reveal that there are five bubbles, where exist the divergences between natural rubber prices and their basic values on account of market fundamentals. The five bubbles are related to imbalance between supply and demand, inefficiencies of smallholders market, oil prices, exchange rate and climatic changes through analyses. Thus, the corresponding authorities are supposed to identify bubbles and consider their evolutions, which is beneficial to the stability of natural rubber price.

Forecasting based on spectral time series analysis: prediction of the Aurubis stock price

2020 ◽  
Vol 17 (4) ◽  
pp. 215-227
Author(s):  
Julia Babirath ◽  
Karel Malec ◽  
Rainer Schmitl ◽  
Kamil Maitah ◽  
Mansoor Maitah
Keyword(s):  
Time Series ◽  
Risk Management ◽  
Time Series Analysis ◽  
Wiener Process ◽  
Stock Price ◽  
Percentage Error ◽  
Portfolio Performance ◽  
Stock Price Prediction ◽  
Series Analysis ◽  
Augmented Dickey Fuller

The attempt to predict stock price movements has occupied investors ever since. Reliable forecasts are a basis for investment management, and improved forecasting results lead to enhanced portfolio performance and sound risk management. While forecasting using the Wiener process has received great attention in the literature, spectral time series analysis has been disregarded in this respect. The paper’s main objective is to evaluate whether spectral time series analysis can produce reliable forecasts of the Aurubis stock price. Aurubis poses a suitable candidate for an investor’s portfolio due to its sound economic and financial situation and the steady dividend policy. Additionally, reliable management contributes to making Aurubis an investment opportunity. To judge if the achieved forecast results can be considered satisfactory, they are compared against the simulation results of a Wiener process. After de-trending the time series using an Augmented Dickey-Fuller test, the residuals were compartmentalized into sine and cosine functions. The frequencies, amplitude, and phase were obtained using the Fast Fourier transform. The mean absolute percentage error measured the accuracy of the stock price prediction, and the results showed that the spectral analysis was able to deliver superior results when comparing the simulation using a Wiener process. Hence, spectral time series can enhance stock price forecasts and consequently improve risk management.

Prediction of Future Market Price for Agricultural Commodities

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Sagar Pathane ◽  
Uttam Patil ◽  
Nandini Sidnal
Keyword(s):  
Mobile Application ◽  
Market Price ◽  
Commodity Prices ◽  
Future Price ◽  
Future Market ◽  
Graphical Interface ◽  
Agricultural Commodities ◽  
Agricultural Commodity ◽  
Web Based ◽  
Agriculture Products

The agricultural commodity prices have a volatile nature which may increase or decrease inconsistently causing an adverse effect on the economy. The work carried out here for predicting prices of agricultural commodities is useful for the farmers because of which they can sow appropriate crop depending on its future price. Agriculture products have seasonal rates, these rates are spread over the entire year. If these rates are known/alerted to the farmers in advance, then it will be promising on ROI (Return on Investments). It requires that the rates of the agricultural products updated into the dataset of each state and each crop, in this application five crops are considered. The predictions are done based on neural networks Neuroph framework in java platform and also the previous years data. The results are produced on mobile application using android. Web based interface is also provided for displaying processed commodity rates in graphical interface. Agricultural experts can follow these graphs and predict market rates which can be informed to the farmers. The results will be provided based on the location of the users of this application.

scholarly journals Transmisión de precios en los mercados regionales de ovino en España

2011 ◽  
Vol 3 (1) ◽  
pp. 71 ◽  
Author(s):  
L. Boshnjaku ◽  
B. Ben-Kaabia ◽  
José M. Gil
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Commodity Markets ◽  
Time Series Models ◽  
The North ◽  
Long Run ◽  
Stochastic Properties ◽  
Price Relationships

The analysis of price relationships in commodity markets provides an approximate idea on markets performance as well as allows the researcher to analyze price responses to unanticipated shocks. The objective of this paper is to explore price relationships in geographical separated markets in the Spanish lamb sector. The methodology used is based on the specification of multivariate time series models which are flexible enough to take into account the stochastic properties of data, the multivariate nature of price relationships and to distinguish between short- and long-run horizons. Results indicate that lamb markets in Spain are strongly related being Zafra the leading market. The influence of Zafra is substantial in the southern markets while in the North, the Lonja del Ebro could be considered as the most representative market.

BALTIC: Monthly Time Series 1900–2005

2008 ◽  
pp. 311-336 ◽  
Author(s):  
Rainer Feistel ◽  
Sabine Feistel ◽  
Gnther Nausch ◽  
Jan Szaron ◽  
Elbieta ysiak-Pastuszak ◽  
...  
Keyword(s):  
Time Series ◽  
Monthly Time Series

scholarly journals Does Futures Speculation Destabilize Spot Prices? New Evidence for Commodity Markets

2013 ◽  
Vol 45 (4) ◽  
pp. 595-616 ◽  
Author(s):  
Martin T. Bohl ◽  
Patrick M. Stephan
Keyword(s):  
Commodity Markets ◽  
Raw Material ◽  
Conditional Volatility ◽  
Open Interest ◽  
Spot Prices ◽  
Market Shares ◽  
New Evidence ◽  
Robust Evidence ◽  
Price Spikes

Motivated by repeated price spikes and crashes over the last decade, we investigate whether the growing market shares of futures speculators destabilize commodity spot prices. We approximate conditional volatility and analyze how it is affected by speculative open interest. In this context, we split our sample into two equally long subperiods and document whether the speculative impact on conditional volatility increases. With respect to six heavily traded agricultural and energy commodities, we do not find robust evidence that this is the case. We thus conclude that the financialization of raw material markets does not make them more volatile.

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