Suchergebnisse

This paper studies the forecasting properties of linear GARCH models for closing-day futures prices on crude oil, first position, traded in the New York Mercantile Exchange from January 1995 to November 2005. In order to account for fat tails in the empirical distribution of the series, we compare models based on the normal, Student's t and Generalized Exponential distribution. We focus on out-of-sample predictability by ranking the models according to a large array of statistical loss functions. The results from the tests for predictive ability show that the GARCH-G model fares best for short horizons from one to three days ahead. For horizons from one week ahead, no superior model can be identified. We also consider out-ofsample loss functions based on Value-at-Risk that mimic portfolio managers and regulators' preferences. EGARCH models display the best performance in this case.

Crude oil plays a big role in determining the world economy today. The increase in the oil price leads to an increase in inflation and hence reduces economic growth. More to that from crude oil, different products reduce. Therefore, a change in oil prices will directly affect these products. Because of this, it is very important to determine the future price of crude oil for better economy budgeting and future planning. Knowing the future price of oil is very challenging. Investors, business people, and the government need accurate prediction models for their decision-making. The main challenge of predicting the price of crude oil is the instability of the price of crude oil. In this paper, the study will use the deep learning techniques to capture the behavior of the crude oil price with a comparison with the other three techniques. The study will use Long Short Term Memory (LSTM) with a comparison with the Moving average (MA), linear regression (LR) and Autoregressive integrated moving average (ARIMA). Using the data from West Texas Index Intermediate (WTI), and measurement performance RMSE and R-Square, this research has proved that deep learning model (LSTM) is the best in capturing nonlinear data for the aim of predicting the future price of crude oil price.

Following record low interest rates and fast depreciating U.S. dollar, crude oil prices became under rising pressure and seemed boundless. Oil price process parameters changed drastically in 2003M5-2007M10 toward consistently rising prices. Short-term forecasting would imply persistence of observed trends, as market fundamentals and underlying monetary policies were supportive of these trends. Market expectations derived from option prices anticipated further surge in oil prices and allowed significant probability for right tail events. Given explosive trends in other commodities prices, depre

Cover -- Title -- Copyright -- Contents -- Notes on Language and Usage -- English-Language Quotations and Spelling -- Arabic Words and Names -- Cast of Characters -- The Saudis -- The Greeks -- The Americans -- Acknowledgments -- Introduction -- Chapter 1: The Reign of Big Oil -- The Original Aramco Concession -- The Truman "Denial Plan" -- The Neutral Zone and J. Paul Getty -- A Giant Tax Break -- Chapter 2: Signs of Trouble -- A Legendary Arab Dies -- Nasser, the New Voice of the Arabs -- Aramco's Workers Strike -- Inside the Elite -- "Minister of Everything" -- Big Deals, Unhappy Outcomes -- Chapter 3: Intrigue on the Riviera -- A Nautical Fraternity -- The Greek Network -- FBI Scrutiny -- Old Money, Saudi Style -- The Deal Is Done -- Chapter 4: Onassis in the Dock -- Facing the Music -- The First Court Appearance -- Duplicity and Disappearing Ink -- Chapter 5: The Shot Heard 'Round the World of Oil -- When Is a Bribe Not a Bribe? -- Welcome to Jeddah, Mr. Ambassador -- What Was in It for Saudi Arabia? -- Seeking Support in Washington -- Alarm at the Pentagon -- Chapter 6: Oil and the Cold War -- An Official "Statement of Policy" -- The "Eisenhower Doctrine" -- America's Great Red Scare -- The Arabs as Targets -- Chapter 7: The CIA Is on the Case -- The New Spy Agency -- "Project Twixt" -- Maheu's Other Client -- Chapter 8: A Two-Tier Strategy -- More Money for Alireza -- The King Wants a Yacht -- The Amended Article IV -- Chapter 9: The World vs. Onassis -- Opposition from London -- Good Guys or Bad Guys? -- Rebuffing the French -- Other Countries Weigh In -- Splitting Hairs -- Chapter 10: Too Many Moving Parts -- Going Public -- Alireza Wants More -- A Film Mogul Enters the Picture -- The Teams Take the Field -- The FBI Sounds an Alarm -- Chapter 11: Onassis "in the Doghouse" -- Mood Swings, Real or Fake -- The Lawyers Weigh In.