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Biography

Dr. Cliff Parsons is President of WTM Energy Software, LLC. He has worked in the energy industry since 2000 developing valuation, trading, and risk models for mainly natural gas trading, but also for power, oil/products, and coal trading. His main focus has been on developing an accurate and practical trading model for natural gas storage, which has been highly elusive to do through time. This development started in 2001 and proceeded for seven more years with the toughest part of the problem being the model’s computer program, not its theoretical foundation.

Dr. Parsons has also worked as a consultant on implementation of risk management systems, mainly Endur. His expertise in this area focused more on VaR methodologies, option valuations and greeks, and assets possessing real optionality. In addition, Dr. Parsons has nine years experience in trading and modeling equity options and mortgage-backed securities, including CMO tranches.

He earned his Ph.D. in finance from the Tepper School of Business, Carnegie Mellon University, in 2001, his MBA with distinction from that same institution in 1994, and he earned his BA in economics from the University of California, Irvine, in 1984. He also passed several actuary exams along the way.


PUBLICATIONS
“Quantifying Natural Gas Storage Optionality: A Two-Factor Tree Model.” Journal of Energy Markets – forthcoming. The paper illustrates a two-factor tree approach to valuing and optimally injecting into, withdrawing from, and hedging natural gas storage. The model was back-tested against prior Henry Hub data and did well at monetizing and hedging the value that the model forecasted for the lease. These results are given in the paper.


“Explaining Bias in Mean-Reversion Speed Estimates for Energy Prices.” Energy Risk, July 2008, pp 92-97. The paper shows that standard OLS estimates of mean-reversion speeds can be biased greatly towards zero when the long-run mean in the sample varies through time, either deterministically or stochastically. The amount of bias is approximated, and a stark example is given for illustration of this bias.

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