Economic Models and Their Use Case in Crypto Trading

Introduction

Systematic study of any field of study demands neat classification of the subject into meaningful branches. Economics is a challenging subject for anyone to understand. Similarly, the economy of a country is a tough thing but if it is split into models, its details become easier to grasp. Once we understand what economic models are, and why they are used by leading analysts around the world, we can get an idea how important they actually are for blockchain networks and cryptocurrencies.

An Economic Model

An economic model is like a map that shows how different components like jobs, inflation and prices are interconnected and what path the economy is expected to take in the near future. We can get information about what economic variables are. These models also serve as useful tools to forecast the future of an economy. They can help us evaluate the effects of different economic policies devised by the government.

Components of an Economic Model

Any economic model consists of a few variables, parameters, equations and assumptions. Variables are the things that can change, like prices, income, production levels, or interest rates. Parameters are fixed components that tell us how variables move. Equations connect variables and parameters mathematically. A famous case is the Phillips Curve, which links inflation with unemployment. The last component is assumptions, which are rules that simplify the model.

Economic models help policymakers assess the impact of fiscal and monetary changes. Analysts forecast economic indicators, and guide business planning for demand and production by using the models.

Economic Models and Blockchain Networks

Supply and Demand

In the blockchain world, economic models are devised on the basis of demand and supply. Supply tells us how many coins we can trade today and how quickly new coins enter the market. Crypto supply often changes in predefined ways. Some coins have a hard cap. Some issue new coins on a schedule. Some reduce issuance over time through events such as halvings . Some burn a portion of fees, which removes coins forever.

Staking and lockups reduce the number of coins that can be sold at once. Team and investor vesting increases the number of coins when new coins are unlocked after the cliff period is over. The cliff period is the time in which the project has promised to add no new coin. A model that tracks circulating supply rather than total supply gives a truer picture of sell pressure.

Demand explains why people want to hold or use a coin. In some cases, demand comes from practical use such as paying fees, using DeFi , joining governance, making payments, or playing games. In other cases, demand comes from speculation. Traders buy coins simply because they believe the price will rise. Growth in the number of users and apps can push demand up very quickly. A simple model can include a score for how useful the coin is and see how changes in utility affect demand.

Price comes from the point where supply and demand meet. In crypto this happens on exchanges and automated market makers. On exchanges with larger order books, a trade changes the price only a little. On smaller books, the same trade makes a big jump. In automated market makers, the size of the pool decides the price impact. Small pools move a lot when trades hit them, whereas large pools move less. A model that deals with liquidity explains why one trade may have very different effects on two venues. Arbitrage traders then step in to close gaps between prices.

Elastic and Inelastic Demand

Elasticity is another simple piece to add. It shows how strongly demand or supply reacts when the price changes. If demand is elastic, a small increase in price makes buyers capitulate a lot. If demand is inelastic, people keep buying even if price rises. Supply in crypto is usually inelastic in the short term. That means supply cannot adjust quickly, so when demand jumps, the price can swing sharply.

Transaction Cost

Transaction-cost model is also widely used. Every blockchain sells block space, and fees are the price. When activity is high, fees rise. When it is low, fees fall. Some blockchains adjust fees automatically with each block. Users can add tips to get faster confirmation. In general, low-cost networks are liked by users more than the high-cost blockchain networks. When fees rise, people mostly move to the cheaper networks. Small trades and micropayments often stop because the fee is bigger than the value. In DeFi, high fees reduce arbitrage because profits must be large enough to cover costs.

Simulation Models

Simulation models are useful for testing ideas without using real money. In a simulation you create simple agents. Some are normal users. Some are traders, and some are bots. There are also a number of validators. Each follows clear rules. Users send transactions when the value is higher than the fee. Traders buy based on trends or value signals. Bots close gaps when profits beat costs. Validators include the best paying transactions. The system then runs step by step and shows what happens. The main purpose of this kind of model is to see how different variables might affect the crypto market.

Limitations of Economic Models

Every model has limits. A model is only a simplified version of reality. Unexpected events can break its rules. If too many people rely on the same model, its predictions may stop working as traders act ahead of it. That is why it is important to test extreme situations and keep safety margins. The purpose is not to make perfect prediction. The purpose is to make better decisions with the information we have.

Conclusion

In conclusion, economic models are the guiding stars for analysts and policymakers to understand, evaluate and forecast about the economy of a country. An economic model has four components. These are variables, parameters, equations and assumptions. In the blockchain network, the economic models serve the same purpose, but the variety of variables is limited. Only the chain of supply and demand is focused.