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By
Dr. Paul Cottrell
March 14, 2016
The 2016 International Meeting of the Academy of
Behavioral Finance & Economics
Dynamic Hedging Oil and Currency Futures Using
Receding Horizontal Control and Stochastic
Programming
Title
Chapter 1
Introduction to the Study
Chapter 2
Literature Review
Chapter 3
Research Method
Chapter 4
Results
Chapter 5
Discussions, Conclusions, and Recommendations
Outline
Chapter 1 - Purpose
The purpose of this research is to fill the gaps in
the literature by providing a comprehensive study
on how to utilize and improve the performance of
the receding horizontal control and stochastic
programming (RHCSP) method pertaining to the
oil and currency markets.
Chapter 1 - Background
Many investors were negatively affected by the financial crisis of 2008.
Many asset types fall together in a financial crisis creating negative returns for investors.
During a financial crisis the energy and currency markets usually exhibit high volatility
As geo-political instability increases and energy supplies disrupted future prices also exhibit high volatility.
The real-world problem is how to offset falling asset prices in a dynamic way?
There is a lack of scholarly literature, research, and understanding in the area of hedging future contracts,
especially in illiquid or very volatile market conditions.
There is a lack of understanding for the reasons of
volatility in the oil or currency futures markets and how to
risk manage those volatility dynamics.
Chapter 1 - Problem Statement
Chapter 1 - Significance
To improve portfolio performance in the oil and currency markets and possible protection from black swan effects.
RHCSP could be a better risk management tool for investors and the banking industry.
This study lends itself to how to reduce volatility epochs.
In theory, governments might be able to use the RHCSP techniques to smooth out price swings.
Similar to how the Federal Reserve affects interest rates.
Question #1:
Can the RHCSP hedging method improve hedging error compared to
the BlackScholes, Leland, Whalley and Wilmott methods when
applied to a simulated market, oil futures market, and currency
futures market?
Chapter 1 - Research Questions
Question #2:
Can a modified RHCSP method significantly reduce hedging
error under extreme market illiquidity conditions when applied
to a simulated market, oil futures market, and currency futures
market?
Chapter 1 - Research Questions
Null Hypothesis:
There are no significant differences in hedging error among
RHCSP, modified RHCSP, BlackScholes, Leland, Whalley
and Wilmott methods when applied to a simulated market, oil
futures market, and currency futures market.
Alternative Hypothesis:
There are significant differences in hedging error among
RHCSP, modified RHCSP, BlackScholes, Leland, Whalley
and Wilmott methods when applied to a simulated market, oil
futures market, and currency futures market.
Chapter 1 - Null and Alternative Hypothesis
Literature Review
Kennedy (2007) used dynamic hedging utilizing a regime switching process, which leveraged a Levy process.
Kim, Han, and Lee (2004) used artificial intelligence to predict price by utilizing fuzzy logic and genetic algorithms.
Modovan, Moca, and Nitchi (2011) used technical indicators for making trading decisions.
Fleten, Brthen, and Nissen-Meyer (2010) used hedging strategies when studying the Nordic hydropower market.
Meindl (2006) used RHC&SP for hedging primarily in simulated environments.
Leland (1985) and Black and Scholes (1973) studied delta hedging at discrete time periods.
Whalley and Wilmott (1997) used threshold levels to activate a rebalancing for a hedged portfolio.
Chapter 2 - Literature Review
Literature Gap
Comprehensive study on how to utilize the performance of the RHCSP method pertaining to oil and currency markets.
Hedging performance in the full boom-bust-recovery cycle.
Dynamic hedging strategies in an illiquid market.
Hedging performance in the financial crisis of 2008.
The utilization of London interbank offered rate (LIBOR) and the Levy process to improve a dynamic hedging strategy.
Chapter 2 - Literature Gap
Chaos theory and emergence
Oil and currency markets are nonlinear systems that
exhibit chaotic attributes (Mastro, 2013, p. 295).
To reduce portfolio variance, due to possible price
swings in the futures market, it is common practice to
implement a hedging strategy (Taleb, 1997, p. 3).
Research used in this study pertains to risk management techniques in corporate finance, but applies the assumption
that markets are not efficient because investors are not utility
maximizing throughout the whole investment time horizon.
Chapter 2 - Theoretical Foundation
Chapter 3 - Research Methodology
Longitudinal quantitative method utilizing an experimental design with simulated and historical asset
prices.
Chapter 3 - Research Design
Two methods are utilized
Simulation
Historical backtesting
Simulation method
To determine, in a stochastic simulated environment,
which hedging method performs the best in terms of
hedging error.
Historical backtesting
To determine, in a real-world environment, which
hedging method performs the best in terms of hedging
error for the light sweet crude and EUR/USD future
contracts.
Bias
Large price swings can produce biased averages.
Point of this research is not to eliminate outliers and to use simple averages to
determine hedging error in real-world conditions.
Internal validity threat
Measuring instrument
o Simulation and historical backtesting should have similar hedging error
characteristics.
External validity threat
Particular market relevance and application to the whole boom-bust cycle of
asset markets.
o This study uses two different asset classes and evaluates the hedging
performance through a boom-bust cycle.
Ethics
Only using simulated and historical datasets of asset prices.
o No special ethical concern required.
Chapter 3 - Bias, Threats to Validity, and Ethics
Stochastic simulation (primary sampling)
One price curve produced using the De Grawue and
Grimaldi (2006) model.
506 4-day average returns calculated
o Equals 8 years of daily returns
Historical data (secondary sampling)
From datasets on light sweet crude and EUR/USD future
contracts.
506 4-day average returns calculated
o Equals 8 years of daily returns
January 1, 2005 to December 31, 2012
Chapter 3 - Sampling
Independent Variables:
Two variables
Three markets
o Simulated, oil, and currency
Five Hedging method
o BlackScholes
o Leland
o Whalley and Wilmott
o RHCSP
o Modified RHCSP
Dependent Variable:
Absolute hedging error
Chapter 3 - Statistical Analysis
Research Question #1 and #2
Two-way ANOVA
Post hoc Tukey testing
F-test and t-test
Using 4-day absolute hedging error
Chapter 3 - Statistical Analysis
Primary and secondary data
SPSS
95% confidence interval, alpha value of 0.05
Effect size 0.20
Power 0.95
With a sample size of 506 of hedging error calculations
Chapter 3 - Statistical Analysis
CL future contract
January 1, 2005 to December 31, 2012
Chapter 4 Data Collection
6E future contract
January 1, 2005 to December 31, 2012
Chapter 4 Data Collection
Simulated market
January 1, 2005 to December 31, 2012
Chapter 4 Data Collection
Hedging error was calculated For each hedging method
506 absolute hedging errors calculated
oA single sample was generated by
average of four daily absolute hedging
errors.
o Absolute hedging error
Value of position leg Value of
hedged leg.
Chapter 4 Data Collection
Chapter 4 Descriptive Statistics
Chapter 4 Results
CL contract
F (4, 2525) = 11.63, p = .000, partial 2 = .018, power = 1.0 t (505) = -9.884, p < .05 (two-tailed) Largest difference with modified RHCSP and Leland Modified RHCSP and BlackScholes , t (505) = -9.860, p < .05 (two-tailed)
Modified RHCSP and Whalley and Wilmott, t (505) = -5.511, p < .05 (two-
tailed)
Modified RHCSP and RHCSP, t (505) = -7.872, p < .05 (two-tailed)
post hoc Tukey test revealed that hedging error was significantly better for
modified RHCSP method versus all remaining methods (all p = .000)
Chapter 4 Results
6E contract
F (4, 2525) = 167.08, p = .000, partial 2 = .209, power = 1.0 t (505) = -21.266, p < .05 (two-tailed) Largest difference with RHCSP and Leland RHCSP and BlackScholes, t (505) = -21.265, p < .05 (two-tailed)
RHCSP and Whalley and Wilmott, t (505) = -12.576, p < .05 (two-tailed)
RHCSP and modified RHCSP, t (505) = -4.331, p <
