In this article, I intend to discuss the importance of market data, decentralized financial econometrics (DeFi), and applied DeFi research on crypto (and digital) assets as a consequence of financial econometrics and applied research. I will also seek to build on the views and lessons learned from Eugene Fama’s articles about his interest in measuring the statistical properties of stock prices and solving the debate between technical analysis and stock prices in order to predict the future price performance of a security and fundamental analysis (the use of of accounting and economic data to determine the value) fair share of a security). Nobel laureate Fama used the efficient market hypothesis – briefly summarized in the symbolism that “prices fully reflect all available information”. in efficient markets.

So let’s focus on this information about crypto and digital assets, on crypto and decentralized financial data sources, market data analysis and everything to do with the new DeFi industry giant float, which is essential to attract institutional investors to the DeFi cryptocurrency and broader “tokens” win “market.

In most markets, market data is the price of an instrument (assets, stocks, commodities, etc.) This data reflects the volatility of the market and asset class, volume and specific trading data such as open, high, low, close, volume (OHLCV) and value data other increases such as order book data (bid-ask spread, aggregated) versus market depth etc.) and pricing and valuation (reference data, traditional financial data such as rates) first exchange rate etc.) These market data were instrumental in financial econometric studies, applied finance and now DeFi research how:

• Risk management framework and risk model
• Quantitative trading
• Prices and prices
• Build and manage portfolios
• Total cryptocurrency financing

While applying a traditional methodology to assessing risk and identifying different opportunities spread across different and emerging crypto asset classes may be limited, this is a first step. New pricing models have emerged aimed at understanding that these digital assets have risen to dominate the truly global digital markets, and even these models require market data. Some of these models include, but are not limited to:

• VWAPor volume-weighted average price, a method that typically determines the fair value of a digital asset by calculating a volume-weighted average price from available post-trade data from structured exchanges.
• TWAPor a time-weighted average price, which can be an oracle or a smart contract that derives token prices from pools of liquidity, using a time period to determine the collateral rate.
• Growth rate determine the element of collateral.
• TVL, or blocked total, for liquidity pools and automated market makers (AMMs).
• Total number of users reflect network effects, usage and potential growth.
• Main market methodology applies to the mainstream market, generally defined as the market with the greatest volume and activity for digital assets. Fair value would be the price you would get for a digital asset in that market.
• Trading volume of CEX and DEX is the total trading volume on central exchanges (CEX) and decentralized exchanges (DEX).
• CVI, or Crypto Volatility Index, is created by calculating a decentralized volatility index from crypto option prices, along with analyzing market expectations for future volatility.

As a result, market data becomes the core of all modeling and analysis tools in order to understand the market and also to carry out correlation analyzes between different crypto areas such as layer one, class one etc. two, Web 3.0 and DeFi. The main source of this crypto market data comes from the ever growing and fragmented mix of crypto exchanges. The data from these exchanges cannot be generally trusted as we have seen cases where increased volume from activities such as wash trading and closed pools can skew prices by misrepresenting the modeling of demand and volume. Therefore, it can be difficult to model a hypothesis based on empirical data and then test the hypothesis to form an investment theory (findings from empirical summaries). This leads to masterpieces in solving problems with reliable data getting into a blockchain transaction system or a mediation layer between traditional financial layers and cryptocurrencies.

Connected: Oracle aims to make blockchain accessible to the masses by providing secure data in cryptocurrency

Blockchain, the underlying technology that governs all crypto assets and networks, establishes its basic principles of trade, trust, and ownership based on transparency that is augmented by a system of trust (or consensus). So why is market data such a big deal? Isn’t it part of the ethos of the blockchain and the crypto industry to rely on marketable and easily accessible data for analysis?

The answer is yes! But! “It gets interesting when we cross crypto markets with fiat-based liquidity – US dollar, euro, yen and pound-pound transactions are the route to traditional financing. The system is made easier for the exchange of cryptocurrencies .

Understand crypto macros and distinguish between global macros

As Peter Tchir, Head of Global Macro at New York Academy Securities, explains in an article by Simon Constable: “Global Macro is the term for fundamental trends that are big enough to boost the economy or a large part of the economy can lower the stock market. “Constable added:

“They are different from microfactors that can affect the performance of a company or a sub-sector of the market.”

I want to differentiate between global macros and crypto macros. While global macro trends – such as inflation, money supply and other macro events – influence the global supply and demand curves, crypto macros regulate the correlation between different domains (such as Web 3.0, layer one, layer two, DeFi and inedible tokens) token domains and Events that affect the respective movement of these types of assets.

Connected: How NFTs, DeFi and Web 3.0 come together

Cryptocurrency (and digital) asset classes define a whole new area of ​​investing, trading, and moving assets in which there is limited interchangeability between asset classes and institutions for exchanging mechanisms such as loans, collateral, and exchanges. This creates a macro environment that is underpinned by crypto-economic principles and theories. When we try to combine these two major macroeconomic environments to inject or move liquidity from one economic system to another, we are essentially complicating metrics and market data due to the collision of value systems.

Let me demonstrate the complexity with an example of the importance of market data and other factors in building investment theories based on evidence from empirical summaries.

Although layer one provides important benefits to the many ecosystems found in layer one networks, not all layer one networks are created equal or offer the same value and insights. For example, Bitcoin (BTC) has the first benefit and is the face of the cryptocurrency ecosystem. It started out as a utility but has evolved into a store of value and an inflation hedge asset class trying to displace gold.

On the other hand, Ether (ETH) introduced the concept of programmability (ability to apply conditions and rules) to price movement, creating rich ecosystems like DeFi and NFT. The ETH is thus becoming a utility token that drives these ecosystems and facilitates co-creation. The increase in transaction activity has increased the demand for ether as it is needed to process transactions.

Bitcoin as a store of value and protection against inflation differs significantly from an ever-growing and emerging business in a Layer 1 network. Hence, it is important to understand what gives these tokens value. It is the usefulness of the token as a fee in the network that makes it valuable, or the ability to store and transfer (large) values ​​in a short period of time, that gives it an advantage over payment systems or shifts an existing value.

In both cases, the utility, trading volume, circulating supply, and associated transaction metrics provide insight into token pricing. If we analyze and consider in more detail the macroeconomic impact on valuation (such as interest rates, money supply, inflation, etc.), the ratio of other crypto assets and cryptocurrencies directly or indirectly affects class one, the resulting theory would include the evolution of the underlying asset include technology, the role of the underlying asset classes and fee insurance at maturity. It would be a sign …