| Course | IBF-5070 Financial Modeling |
|---|---|
| Module | Module 7 |
| Paper type | Discussion forum: initial response and two comments |
| Length | About 453 words |
| Format | Discussion post with APA 7 citations |
| School | Thomas Edison State University |
| Program | MBA in Finance |
| Updated | October 2026 |
Free sample paper for IBF 5070 Module 7
Module 7 Discussion: History, Forwards or Fundamentals?
History, Forwards or Fundamentals? Setting Long-Run Power Prices for a Solar Model
My Cumberland solar model needs a market power price for years 21 to 35, from about 2047 to 2061. Last week I calculated a volatility of 77 percent from PJM annual prices for 2019 to 2023, and it was clearly unusable for that horizon. This week's question is where long-run price assumptions should come from. The horizon matters here: the contract covers years 1 to 20, so only the last fifteen years depend on the market at all, and their value is discounted heavily.
Historical data has two problems. The recent history is short and dominated by one event: prices roughly doubled from 2021 to 2022, to about $80 per MWh, when natural gas prices spiked, then fell to about $31 in 2023 (Monitoring Analytics, 2024). An average of those years tells me little about 2050. And the period's volatility reflects a shock that reversed quickly, which a random walk model treats as permanent.
Market data has a different problem. Forward contracts for PJM power trade actively only a few years out, so they say nothing direct about prices two decades away, and their prices include risk premiums and reflect today's gas prices.
The pattern in the data, a spike followed by a quick return, is mean reversion. Schwartz (1997) showed that commodity prices are better described by models in which prices are pulled back toward a long-run level than by random walks, and Pindyck (1999) found that energy prices revert to trends that themselves shift slowly over time. For electricity, the long-run level is tied to the cost of building new generation, since prices well above that cost attract new plants. I plan to model the post-contract price as reverting toward an estimate of that cost, with a slowly moving long-run level and much lower long-horizon uncertainty than the 77 percent figure implied.
Comment on Jamal's Post
Jamal, using the Treasury forward curve for your bank's interest rate model makes sense for the next five years, where the market is deep. Beyond that, have you considered blending the curve with a long-run rate based on expected inflation and growth? Rates have also shown mean reversion over long periods, so the curve and history together might give a steadier anchor than either alone.
Comment on Elena's Post
Elena, your copper price history from the last ten years includes the 2021 commodity boom, much like my 2022 power spike. Pindyck (1999) found mean reversion in copper and other resource prices, as well as in energy, with long-run levels tied to production costs. Anchoring your model's long-run copper price to mining costs might avoid building one unusual period into fifteen years of forecasts.
References
Monitoring Analytics. (2024). 2023 state of the market report for PJM. Monitoring Analytics, LLC.
Pindyck, R. S. (1999). The long-run evolution of energy prices. The Energy Journal, 20(2), 1-27. https://doi.org/10.5547/ISSN0195-6574-EJ-Vol20-No2-1
Schwartz, E. S. (1997). The stochastic behavior of commodity prices: Implications for valuation and hedging. Journal of Finance, 52(3), 923-973. https://doi.org/10.1111/j.1540-6261.1997.tb02721.x
IBF 5070 Module 7 instructions, in plain terms
The Module 7 forum asks about the problems of using historical and market data in time series analysis. Expect to discuss whether past prices or current market prices should drive a model's assumptions, how to recognize mean reversion, and how the choice affects long-run forecasts and volatility. Use the data from your own model, explain what is wrong or limited about each source and propose an approach. Reply to two classmates with comments that suggest a specific alternative or a piece of research, and keep your replies tied to the data each classmate described. Name the source of any data you cite, such as a market monitor or a government agency, and give the period it covers. If your model uses interest rates, exchange rates or a commodity other than power, the same questions apply.
How this IBF 5070 Module 7 example is built
The post begins with the specific forecasting need, prices two decades away, and the volatility problem found the week before. One paragraph explains the limits of the short historical record, with figures from the regional market monitor. Another explains the limits of forward prices. A paragraph identifies mean reversion in the data, cites research on commodity price behavior and proposes an approach anchored to the cost of new generation. The replies suggest blending market and long-run data for interest rates and anchoring a copper price to production costs. The post stays short and specific, using only the figures that make its point, and every claim about price behavior is tied to research a reader can find. Each reply gives the classmate a method to try and a reason to expect it to work.
Reading the IBF 5070 Module 7 grading rubric
Credit here follows a strong first post, correct time series ideas backed by research and replies that move classmates forward. Posts that use the student's own data, and that explain the specific weakness of each source, show understanding. Identifying mean reversion and connecting it to a fundamental long-run level demonstrates the module's main idea. Replies that suggest a concrete method with research support earn more than general encouragement, and accurate figures from a named source strengthen the post. Mentors also look for posts that state the horizon of the forecast at the start, since the right data source depends on how far ahead the model looks. Replies that connect a classmate's series to the same idea, mean reversion toward a cost-based level, show integration across cases.
Common IBF 5070 Module 7 mistakes, and how to avoid them
Students lose points by declaring historical or market data simply better, by ignoring the horizon of their model or by mentioning mean reversion without saying what prices revert toward. Others reply with praise and no suggestion. Explain the limitation of each data source for your horizon. Identify the long-run level and why prices would return to it. Cite research. In replies, propose a specific approach your classmate could test and say why it fits their data. Keep the post focused on one series and one horizon. A free first sample on your own IBF-5070 Module 7 discussion is available on request.
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IBF 5070 Module 7 questions, answered
Where can I find a free IBF 5070 Module 7 sample paper?
History, forward prices and mean reversion in long-run power prices are debated in the post above, with two replies.
What is mean reversion?
The tendency of a price to move back toward a long-run level after rising or falling away from it.
Why are forward prices limited for long-term models?
They trade actively only a few years out and include risk premiums, so they say little about prices decades away.
What sets the long-run level of electricity prices?
Largely the cost of building new generation, since prices well above it attract new plants.
Why can historical volatility mislead a long-term model?
A short history may include unusual shocks that reversed, and a random walk treats them as permanent.