The causal relationship between futures price volatility and the cash price volatility of GNMA securities

1986 ◽  
Vol 6 (1) ◽  
pp. 29-39 ◽  
Author(s):  
Anand K. Bhattacharya ◽  
Anju Ramjee ◽  
Balasubramani Ramjee
Keyword(s):  
Causal Relationship ◽  
Price Volatility ◽  
Futures Price

The Reaction of Coffee Futures Price Volatility to Crop Reports

2016 ◽  
Vol 53 (10) ◽  
pp. 2361-2376 ◽  
Author(s):  
Rodrigo Lanna F. da Silveira ◽  
Fabio L. Mattos ◽  
Maria Sylvia M. Saes
Keyword(s):  
Price Volatility ◽  
Futures Price

scholarly journals Nowcasting of Lumber Futures Price with Google Trends Index Using Machine Learning and Deep Learning Models

2021 ◽  
Vol 72 (1) ◽  
pp. 11-20
Author(s):  
Mingtao He ◽  
Wenying Li ◽  
Brian K. Via ◽  
Yaoqi Zhang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Price Volatility ◽  
Google Trends ◽  
Learning Models ◽  
Fresh Perspective ◽  
Futures Price

Abstract Firms engaged in producing, processing, marketing, or using lumber and lumber products always invest in futures markets to reduce the risk of lumber price volatility. The accurate prediction of real-time prices can help companies and investors hedge risks and make correct market decisions. This paper explores whether Internet browsing habits can accurately nowcast the lumber futures price. The predictors are Google Trends index data related to lumber prices. This study offers a fresh perspective on nowcasting the lumber price accurately. The novel outlook of employing both machine learning and deep learning methods shows that despite the high predictive power of both the methods, on average, deep learning models can better capture trends and provide more accurate predictions than machine learning models. The artificial neural network model is the most competitive, followed by the recurrent neural network model.

Forecasting the oil futures price volatility: Large jumps and small jumps

2018 ◽  
Vol 72 ◽  
pp. 321-330 ◽  
Author(s):  
Jing Liu ◽  
Feng Ma ◽  
Ke Yang ◽  
Yaojie Zhang
Keyword(s):  
Price Volatility ◽  
Oil Futures ◽  
Futures Price

scholarly journals The Impact of Futures Price Volatility to Spot Market : Case of Coffee in Indonesia

2019 ◽  
Vol 15 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Anis Erma Wulandari ◽  
Harianto Harianto ◽  
Bustanul Arifin ◽  
Heny K Suwarsinah
Keyword(s):  
Exchange Rate ◽  
Price Volatility ◽  
Spot Market ◽  
Futures Market ◽  
Spot Price ◽  
Price Risk ◽  
Price Inflation ◽  
Spot Prices ◽  
Futures Price ◽  
The Impact

Indonesia is the world 4th largest coffee producer after Brazil, Vietnam and Colombia with export potential and higher national consumption concluded in 2017 while the coffee production was relatively stagnant. This was led the producer to not only the production risk but also the price risk which then emphasize the importance of futures markets existence as price risk management. This study is performed to examine the impact of futures price volatility to spot market using ARCH-GARCH toward primary data of coffee futures and spot prices of 1172 trading days starting from January 2014 to June 2018. The ARCH-GARCH analysis result indicates that futures price volatility and monetary variables are impacting the volatility of spot price. Arabica spot price volatility is impacted by volatility of Arabica futures price, inflation and exchange rate while Robusta spot price is impacted by Robusta futures price volatility and exchange rate. This is confirming that futures market plays dominant role in spot price discovery. Local futures and spot prices are also found to be significantly influenced by volatility of offshore futures prices which indicates that emerging country futures market is actually influenced by offshore futures market which the price itself used as price reference.

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