Risk Modelling Part 1: Foundations

risk

series

Published

December 12, 2025

Modified

December 16, 2025

What is Risk Modelling?

Risk modelling is the practice of using mathematical frameworks to quantify uncertainty and potential losses. In climate contexts, this typically involves:

Hazard identification - What can go wrong?
Exposure assessment - What assets are at risk?
Vulnerability analysis - How susceptible are those assets?
Loss estimation - What are the potential consequences?

Core Concepts

The foundation of risk modelling rests on probability theory. We often use Monte Carlo methods to simulate thousands of scenarios and understand the distribution of possible outcomes.

Key terms: - Expected loss: The average loss across all scenarios - Value at Risk (VaR): The maximum loss at a given confidence level - Tail risk: The probability of extreme events

Why This Matters for Climate

Climate change introduces non-stationarity into our models. Historical data becomes less reliable as a predictor of future risk. This is explored further in Part 2: Data.

---
title: "Risk Modelling Part 1: Foundations"
date: 2025-12-12
categories: [risk, series]
---

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Series: Introduction to Risk Modelling (Part 1 of 3)
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[Next: Data →](risk-series-2.html)
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## What is Risk Modelling?

Risk modelling is the practice of using mathematical frameworks to quantify uncertainty and potential losses. In climate contexts, this typically involves:

1. **Hazard identification** - What can go wrong?
2. **Exposure assessment** - What assets are at risk?
3. **Vulnerability analysis** - How susceptible are those assets?
4. **Loss estimation** - What are the potential consequences?

## Core Concepts

The foundation of risk modelling rests on probability theory. We often use Monte Carlo methods to simulate thousands of scenarios and understand the distribution of possible outcomes.

Key terms:
- **Expected loss**: The average loss across all scenarios
- **Value at Risk (VaR)**: The maximum loss at a given confidence level
- **Tail risk**: The probability of extreme events

## Why This Matters for Climate

Climate change introduces non-stationarity into our models. Historical data becomes less reliable as a predictor of future risk. This is explored further in [Part 2: Data](risk-series-2.html).

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