scholarly journals Conditional Probability of Jumps in Oil Prices

2021 ◽  
Vol 16 (4) ◽  
pp. 1-14
Author(s):  
Arturo Lorenzo-Valdés
Keyword(s):  
Conditional Probability ◽  
Stochastic Dynamics ◽  
Multivariate Time Series ◽  
Oil Prices ◽  
Time Series Modeling ◽  
Volatility Clustering ◽  
Crude Oil Prices ◽  
Double Exponential ◽  
Jump Intensity ◽  
Negative Effect

The objective of this research is to model the behavior of oil returns. The volatility of oil returns is described through a TGARCH process. Conditional probability jumps are incorporated through uniform, double exponential and normal jump intensity distributions. We found that the volatility of oil returns follows the stylized facts of leptokurtosis, leverage effect and volatility clustering. The abnormal information that causes the jumps, can cause another type of unexpected changes in the following period and the intensity of the jumps has a negative effect on the probability of jumps in the next period. The dynamic model proposed can be extended to other markets and to multivariate time series modeling considering the dependence among the markets’ returns. The main contribution of this work is the estimation of the conditional probability of jumps depending on the previous behavior leading to a better description of the stochastic dynamics of crude oil prices. This will be useful for making better decisions regarding oil as an underlying asset in derivatives or in the formulation of better public policies.

scholarly journals COVID-19 Deaths Cases Impact on Oil Prices: Probable Scenarios on Saudi Arabia Economy

2021 ◽  
Vol 9 ◽  
Author(s):  
Abdelmageed Algamdi ◽  
Said Khalfa Mokhtar Brika ◽  
Adam Musa ◽  
Khalil Chergui
Keyword(s):  
Saudi Arabia ◽  
Crude Oil ◽  
Oil Prices ◽  
The United States ◽  
Real Economy ◽  
Sharp Drop ◽  
Distributed Lag ◽  
Crude Oil Prices ◽  
Negative Effect ◽  
The Usa

The purpose of this paper is to discuss death cases on the World, exacerbated investor fears, uncertainties, and increased volatility of crude oil prices in financial markets. The reaction absorbed the epidemic gradually until January 22. Still, the market situation changed soon with a sharp drop in prices, and prices slowly recovered after that until June 14. The data of this research using an econometric model, the ARDL (Autoregressive Distributed Lag), according to the Gets methodology, using daily data, January 22 –June 14, 2020. Our ARDL shows, the death ratio has a significant negative effect on oil price dynamics. However, the death ratio has an indirect impact on volatility in Crude Oil prices. The findings show that the death toll of COVID-19 has a significant impact on oil prices in Saudi Arabia (KSA). However, the preliminary results mainly influence by the situation reported in the USA. When we assess the case outside the USA, and we see the positive effect of the COVID-19 death figures on oil prices, therefore, stress the amplification of death-related risks to the financial market and the real economy, caused by increased, policy-induced economic uncertainty in the United States.

Modelling Crude Oil Prices Volatility in India

2019 ◽  
Vol 4 (2) ◽  
Author(s):  
Khyati Kathuria ◽  
Shikha Gupta ◽  
Nand Kumar
Keyword(s):  
Crude Oil ◽  
Linear Time ◽  
Price Volatility ◽  
Oil Prices ◽  
Oil Price ◽  
Crude Oil Price ◽  
Volatility Clustering ◽  
Price Series ◽  
Crude Oil Prices ◽  
Increasing Demand

Crude oil is a crucial component of India’s energy basket after coal. The increasing demand for crude oil in India is met through imports. Crude oil price changes affect the social stability, economic development, and national security of the country. Therefore, it is crucial to devise suitable methods to forecast crude oil price movements accurately.Thus, the purpose of this study is to evaluate the forecasting performance of linear and non-linear time series models. In the study Box Jenkins methodology is used to obtain a best fit ARIMA and GARCH type models and further use it to forecast the crude oil (Brent) prices. The study shows that the crude oil price series is volatile over the time trend and therefore uses the GARCH class models as well which are capable of capturing volatility clustering typical of oil price series. Performance of ARIMA & GARCH class modes is then compared to find out which model better forecasts the crude oil prices. Indian economy being vulnerable to volatility in the international crude oil market requires a methodology to accurately forecast the price volatility and therefore to fill this gap this study for forecasting and studying the behavior of crude oil price series was conducted.

scholarly journals Nonlinear Causality between Crude Oil Prices and Exchange Rates: Evidence and Forecasting

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6043
Author(s):  
Witold Orzeszko
Keyword(s):  
Exchange Rates ◽  
Crude Oil ◽  
Oil Prices ◽  
Support Vector ◽  
Time Series Modeling ◽  
Crude Oil Prices ◽  
Linear Framework ◽  
Modeling And Forecasting ◽  
Causality Tests ◽  
Policy Analysts

The relationships between crude oil prices and exchange rates have always been of interest to academics and policy analysts. There are theoretical transmission channels that justify such links; however, the empirical evidence is not clear. Most of the studies on causal relationships in this area have been restricted to a linear framework, which can omit important properties of the investigated dependencies that could be exploited for forecasting purposes. Based on the nonlinear Granger causality tests, we found strong bidirectional causal relations between crude oil prices and two currency pairs: EUR/USD, GBP/USD, and weaker between crude oil prices and JPY/USD. We showed that the significance of these relations has changed in recent years. We also made an attempt to find an effective strategy to forecast crude oil prices using the investigated exchange rates as regressors and vice versa. To this aim, we applied Support Vector Regression (SVR)—the machine learning method of time series modeling and forecasting.

scholarly journals Investigation of Global Crude Oil Price Shocks verses Exchange Rates Nexus: Evidenced from Tanzanian Shillings

2020 ◽  
pp. 16-29
Author(s):  
Suma Mwankemwa ◽  
Isack Kibona ◽  
Aziza M. Said
Keyword(s):  
Exchange Rates ◽  
Crude Oil ◽  
Oil Prices ◽  
Oil Price ◽  
Series Data ◽  
Oil Price Shocks ◽  
Crude Oil Price ◽  
Price Shocks ◽  
Crude Oil Prices ◽  
Negative Effect

This study investigated the nexus of crude oil price shocks and exchange rates of Tanzanian shillings (TSh) as an oil importing country. Using weekly series data for the period 01/01/2005 to 31/12/2015, Vector Autoregressive (VAR) model was employed to test the relationship of crude oil prices and Tanzanian exchange rates. In addition, Granger Causality was tested to check the causality of these two variables. The findings of this study show that oil prices granger causes the exchange rate of TSh while exchange rates of TSh cannot Granger cause the oil prices. Also, the impulse response functions revealed that crude oil price shocks initially had a significant negative effect on TSh, however, there was a slightly negative effect on crude oil starting from TSh as a granger causer. VAR results showed that all the coefficients of TSh do not significantly influence crude oil prices. Crude oil price coefficients had a negative significance towards explaining the variability of Tanzanian shillings’ exchange rates (TZS).This revealed that a change in oil prices would precede changes in TSh movements.

An Empirical Investigation of Factors Affecting Stock Prices in Vietnam

2014 ◽  
pp. 74-89 ◽  
Author(s):  
Vinh Vo Xuan
Keyword(s):  
Exchange Rates ◽  
Crude Oil ◽  
Foreign Exchange ◽  
Stock Prices ◽  
Global Financial Crisis ◽  
Oil Prices ◽  
Foreign Exchange Rates ◽  
Factors Affecting ◽  
Crude Oil Prices ◽  
Before And After

This paper investigates factors affecting Vietnam’s stock prices including US stock prices, foreign exchange rates, gold prices and crude oil prices. Using the daily data from 2005 to 2012, the results indicate that Vietnam’s stock prices are influenced by crude oil prices. In addition, Vietnam’s stock prices are also affected significantly by US stock prices, and foreign exchange rates over the period before the 2008 Global Financial Crisis. There is evidence that Vietnam’s stock prices are highly correlated with US stock prices, foreign exchange rates and gold prices for the same period. Furthermore, Vietnam’s stock prices were cointegrated with US stock prices both before and after the crisis, and with foreign exchange rates, gold prices and crude oil prices only during and after the crisis.

Determinants of Inflation in Vietnam: A VECM Approach

2015 ◽  
Vol 22 (04) ◽  
pp. 26-50
Author(s):  
Ngoc Tran Thi Bich ◽  
Huong Pham Hoang Cam
Keyword(s):  
Interest Rate ◽  
Crude Oil ◽  
Error Correction ◽  
Budget Deficit ◽  
Oil Prices ◽  
Crude Oil Prices ◽  
Domestic Credit ◽  
Short Run ◽  
The Impact

This paper aims to examine the main determinants of inflation in Vietnam during the period from 2002Q1 to 2013Q2. The cointegration theory and the Vector Error Correction Model (VECM) approach are used to examine the impact of domestic credit, interest rate, budget deficit, and crude oil prices on inflation in both long and short terms. The results show that while there are long-term relations among inflation and the others, such factors as oil prices, domestic credit, and interest rate, in the short run, have no impact on fluctuations of inflation. Particularly, the budget deficit itself actually has a short-run impact, but its level is fundamentally weak. The cause of the current inflation is mainly due to public's expectations of the inflation in the last period. Although the error correction, from the long-run relationship, has affected inflation in the short run, the coefficient is small and insignificant. In other words, it means that the speed of the adjustment is very low or near zero. This also implies that once the relationship among inflation, domestic credit, interest rate, budget deficit, and crude oil prices deviate from the long-term trend, it will take the economy a lot of time to return to the equilibrium state.

Dynamic Relationship between Crude Oil and Stock Prices in India: Before And After the Subprime Financial Crisis 2008

GIS Business ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. 96-104
Author(s):  
P. Sakthivel ◽  
S. Rajaswaminathan ◽  
R. Renuka ◽  
N. R.Vembu
Keyword(s):  
Financial Crisis ◽  
Crude Oil ◽  
Stock Prices ◽  
Oil Prices ◽  
Energy Index ◽  
Data Set ◽  
Dynamic Relationship ◽  
Crude Oil Prices ◽  
Long Run ◽  
Before And After

This paper empirically discovered the inter-linkages between stock and crude oil prices before and after the subprime financial crisis 2008 by using Johansan co-integration and Granger causality techniques to explore both long and short- run relationships.  The whole data set of Nifty index, Nifty energy index, BSE Sensex, BSE energy index and oil prices are divided into two periods; before crisis (from February 15, 2005 to December31, 2007) and after crisis (from January 1, 2008 to December 31, 2018) are collected and analyzed. The results discovered that there is one-way causal relationship from crude oil prices to Nifty index, Nifty energy index, BSE Sensex and BSE energy index but not other way around in both periods. However, a bidirectional causality relationship between BSE Energy index and crude oil prices during post subprime financial crisis 2008. The co-integration results suggested that the absence of long run relationship between crude oil prices and market indices of BSE Sensex, BSE energy index, Nifty index and Nifty energy index before and after subprime financial crisis 2008.

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