Lecture 15

.title[
# Lecture 15
]
.subtitle[
## Climate Risk and Financial Instruments
]
.author[
### Ivan Rudik
]
.date[
### AEM 4510
]

---

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if (!require("pacman")) install.packages("pacman")
pacman::p_load(
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knitr::opts_hooks$set(fig.callout = function(options) {
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knitr::opts_chunk$set(echo = TRUE, fig.align="center")
  options
})
```

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## Warning: 'xaringanExtra::style_panelset' is deprecated.
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---

# Roadmap

1. .hi[Weather markets (Schlenker and Taylor, 2021):] do traders forecast weather and climate?
2. .hi[Municipal bond markets (Painter, 2020):] is sea level rise capitalized into municipal financing costs?
  - Marginal damages
3. .hi[Prediction markets (Meng, 2017):] what is the probability of environmental regulation?
4. .hi[Equity markets (Meng, 2017):] what is the financial impact of expected environmental regulation?
  - Marginal abatement costs

---

# Weather markets

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# Betting on the weather

The purpose of this lecture is to see whether financial markets price climate risk

Our first step: prove to ourselves that traders even recognize climate change

How?

Studying the market for weather derivatives
    
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# Betting on the weather

Weather derivatives are a way for weather-exposed firms to manage climate risk
- Which kind of firms?

CME offers contracts based on weather indices in 13 cities (mostly US)

---

# Betting on the weather

Winter contracts are based on .hi[heating degree days (HDD)]
- HDD = max(0, `$65^\circ$`F - daily average temperature): how much colder than 65

Summer contracts are based on .hi[cooling degree days (CDD)]
- CDD = max(0, daily average temperature - `$65^\circ$`F): how much warmer than 65
  
--

Idea is `$65^\circ$`F is about where you would heat or cool a building to during the day

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# Betting on the weather

How do the contracts work?

The contracts are bought and sold like regular assets, but the settlement price is explicitly based on the realized HDD/CDD

CME contracts work where the settlement price is: 20 `$\times$` CDD/HDD

Example:
 - If the July CDD contract is trading at 300 CDDs, the contract costs `$20 \times 300 = 6000$`
 - If actual July CDDs are 330, a buy-side trader profits: `$20 \times (330 - 300) = 600$`

---

# Betting on the weather

Who might buy/sell weather contracts?

Natural gas suppliers in the Northeast

- Natural gas suppliers profit more when winter is cold (why?)

Do these firms want to buy or sell winter HDD contracts to manage risk?

---

# Betting on the weather

Selling a winter HDD contract is a bet that HDDs will be low `$\rightarrow$` winter will be warm

This bet pays off when natural gas revenues are low and helps manage risk

Example:

- The winter HDD contract is trading at 1250
- At 1250 HDDs, NYSEG expects to generate $80 million in profit
- An increase in HDDs of 1 increases profits by $80,000

---

# Betting on the weather

Example:

- The winter HDD contract is trading at 1250
- At 1250 HDDs, NYSEG expects to generate $80 million in profit
- An increase in HDDs of 1 increases profits by $80,000

If NYSEG sells `$Y$` winter HDD contracts, and the realized winter HDD is `$HDD_{actual}$`:
- Its futures market profits are: `$Y \times 20 \times (1250 - HDD_{actual})$`
- Its natural gas profits are: `$80,000,000 + 80,000 \times (HDD_{actual} - 1250)$`

Futures profits down in `$HDD_{actual}$`, natural gas profits go up

---

# Betting on the weather

Example:
- The winter HDD contract is trading at 1250
- At 1250 HDDs, NYSEG expects to generate $80 million in profit
- An increase in HDDs of 1 increases profits by $80,000

How many contracts should NYSEG sell if it wants to eliminate all risk?

Eliminate risk by setting sum of futures market profit and HDD-related natural gas profit to zero

`$$Y \times 20 \times (1250 - HDD_{actual}) + 80,000 \times (HDD_{actual} - 1250) = 0$$`

`$$Y = 4,000 \,\, \text{contracts}$$`

---

# Betting on the weather

Who else might participate in these markets (summer or winter)?

- Farmers
- Amusement parks
- Electricity utilities
- Snow plow services
- People who think they have better private information

The market price should aggregate everyone's beliefs about weather

If traders actually internalize climate information, we should see weather derivative prices respond to weather and climate forecasts

---

# Betting on the weather

Schlenker and Taylor (2021) study whether the contract prices capitalize expected short-run weather, and long-run climate change

The first step is to see whether short-run weather is capitalized into the price

How do they do it?

1. Compute the .hi[weather anomaly:] how much warmer or cooler a day is relative to its average (accounting for overall warming over time)
2. Compute whether the change in the price from open to close on a given day is associated with weather anomalies

---

# Betting on the weather

1-3 day forecasts are essentially perfect now

10 day forecasts have 40% "skill": 40% smaller error than if you just assumed temperature would be its long-run average

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# Betting on the weather

Forecasts >10 days out have little skill `$\rightarrow$` little information value

Forecasts 1-3 days out have near-perfect skill, their information is probably already capitalized by prior forecasts 5-7 days ago

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# Betting on the weather

We should expect forecasts 3-10 days out to matter the most for contract prices

We should expect little to zero effect of 1-3 day forecasts, and 10+ day forecasts

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# July CDD prices for Laguardia airport

July in NY averages about 400 CDDs

Each year from 2001-2020 (different color lines) differs in terms of actual CDDs (price at 0), and expected CDDs (prices to the left of 0)

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# July CDD prices for Laguardia airport

In general, prices don't move much further than 10 days before the start of July `$\rightarrow$` consistent with short-run 10+ day forecasts not being skillful

Differences across years can be from long-run trends, El Nino, etc

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# July CDD prices for Laguardia airport

Once we near the actual month (about -40), forecasts are skillful

They start trending toward their realized values (at 0)

From -30 to 0 we are .hi[in] the actual month and observe some of the realized CDDs

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# Betting on the weather

So far we just eyeballed data, but now we want to actually compute whether the change in the price from open to close on a given day is associated with weather anomalies

Schlenker and Taylor estimate a regression model of how weather anomalies up to 1 week before some day `$t$`, and up to 3 weeks after day `$t$` affect the change in the contract price during day `$t$`

Let's think through the intuition before seeing the results

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# Betting on the weather

Should weather anomalies 3 days ago (i.e. in the past) affect the change in the contract price today?

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# Betting on the weather

Should weather anomalies in the future affect the change in the contract price today?

.hi[Yes!] Skillful forecasts should predict future weather anomalies, if traders use these forecasts then future weather anomalies should affect the current price change
- Suggests forecasts 10+ days ahead might not affect the price

---