Presentation - Dynamically Hedging Oil and Currency by Paul Cottrell

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Text of Presentation - Dynamically Hedging Oil and Currency by Paul Cottrell

  • 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 <