20171130-法国巴黎银行-Introducing_ENERGY_Factor_Model_Monthly_11页_377kb

Summary of COMMODITY QUANT STRATEGY - ENERGY Factor Model Monthly

Core Content

This document outlines the ENERGY Factor Model Monthly report from BNP Paribas Brasil S.A., part of the Commodity Quant Strategy. It presents the current state of energy commodity prices, particularly WTI crude oil, Brent crude oil, and gasoline RBOB, in relation to a factor model that incorporates macroeconomic and financial data to project future prices.

Main Points

1. Factor Model Overview

• Factor Model and MarFA™ are quantitative models used to analyze commodity prices.
• The ENERGY Factor Model Monthly is introduced to provide monthly updates on factor model values for WTI, Brent, and gasoline RBOB.
• A Metals Factor Model Monthly was introduced previously.

2. Current Price Analysis

• WTI Crude Oil (1st maturity):

  • Model value: USD 56.40/bbl
  • Current market level: 2% above model value
  • Error bands: USD 43.17 to USD 69.93
  • Conditional projection for Q1 2018: USD 56.00/bbl

• Brent Crude Oil (1st maturity):

  • Model value: USD 58.30/bbl
  • Current market level: ~10% above model value
  • Error bands: USD 49.90 to USD 67.10
  • Conditional projection for Q1 2018: USD 58.00/bbl

• Gasoline RBOB (1st maturity):

  • Model value: USD 160/gal
  • Current market level: 9% above model value
  • Error bands: USD 127.4 to USD 192.6
  • Conditional projection for Q1 2018: USD 157.6/gal

3. Strategy Position

• Factor model is neutral on crude oil; however, MarFA™ is short a basket of Brent and WTI.
• The model is used to identify substantial dislocations in the market, which can be arbitraged.

4. Methodology and Tools

• VAR (Vector Autoregression) model is used to analyze the dynamic relationship between macroeconomic variables and commodity prices.
• The model looks at the impact of US real interest rates and supply-demand dynamics on oil prices.
• Non-parametric distant measures are used to determine robust drivers of WTI crude oil from 2007 to the present.

5. Impulse-Response Analysis

• A 16% drop in WTI oil price (equivalent to 2 standard deviations) would result in a 13% drop in gasoline RBOB in the first month and a 11% permanent drop thereafter.
• A 30bp change in the US IR curve slope (equivalent to 2 standard deviations) would lead to a 9.1% immediate drop and a 16% medium-term drop in WTI oil price.
• A supply increase equivalent to 2 standard deviations would result in a 4.2% drop in the first month and an 8% drop in the medium term.

6. Macroeconomic and Financial Linkages

• The US real interest rate and the US dollar are strong drivers of WTI oil prices.
• The US dollar is also a key factor for commodity prices, affecting monetary policy and global inflation.
• China's pseudo-peg with the US dollar means that US monetary policy impacts Chinese PPI, which in turn affects global CPI.

7. Performance Update

• A table of recent trades shows the performance of the strategy, including PnL percentages and USD values.
• The Factor model has shown a positive overall performance, while MarFA™ has had mixed results.

8. Contacts

• The document provides contact details for the Commodity Quant Strategy team in Brazil and London.
• Key contacts include Gabriel Gersztein, Michael Sneyd, Samuel Castro, Gustavo Mendonca, and Robert McAdie.

Key Information

• The ENERGY Factor Model Monthly is a monthly publication that updates factor model values for WTI, Brent, and gasoline RBOB.
• The factor model is used to quantify market dislocations and project future price movements.
• The VAR model is employed to assess the dynamic impact of macroeconomic and financial variables on commodity prices.
• The strategy is short on certain commodities, based on the model's neutral stance on crude oil.
• The document highlights the interconnectedness between financial markets, macroeconomic variables, and commodity prices.

Legal and Risk Notice

• The document is non-independent research and marketing communication.
• It is intended for Professional Clients and Eligible Counterparties.
• No guarantees are made regarding the accuracy or completeness of the information.
• Performance data is based on back-testing and is not indicative of future results.
• Transactions involving the products discussed may involve high risk and volatility.
• Confidentiality is emphasized, and distribution without consent is prohibited.
• Options and ETFs mentioned carry significant risks and are not suitable for all investors.

Conclusion

The ENERGY Factor Model Monthly provides a quantitative analysis of energy commodity prices, focusing on WTI, Brent, and gasoline RBOB. It highlights the role of macroeconomic and financial variables in shaping these prices and presents a strategy based on these insights. The document also includes performance data, impulse-response analysis, and legal disclaimers to ensure transparency and compliance with financial regulations.