Chainlink price feeds have been a fundamental building block of the DeFi ecosystem, providing accurate, tamper-proof, and aggregated price reference data for a growing number of cryptocurrencies, commodities, and fiat currencies. The availability of high-quality price data has played a major role in the growth of DeFi, bringing its total locked asset value to $170 billion at its peak, and protecting users from attacks related to data manipulation.
However, for DeFi applications, secure external data sources are not limited to price data, but also include various other key indicators that can serve as important inputs for creating more complex financial products and automated risk management strategies. In order to meet user market demand for new and unique data points, we are constantly conducting research and development to make these data points available to developers.
For example, the Chainlink Liquidity Indicator is now in beta, supporting the use of Chainlink's low-latency oracles for risk management of DeFi derivatives. These data sources could enable derivatives markets to mitigate the risk of excessive exposure to illiquid assets, and lending markets to adjust parameters such as loan-to-value (LTV) ratios based on current liquidity conditions.
To further support developers in the DeFi ecosystem, the Chainlink network will support implementing volatility and implied volatility oracles. This is yet another powerful tool in the DeFi developer’s toolbox to build a new wave of on-chain risk management strategies and unique derivative markets.
In this article, we'll explore the role of realized volatility and implied volatility data in financial markets, and how developers can start immediately implementing a volatility data feed with Chainlink and using implied volatility with Chainlink Functions.
Asset Volatility Data in Financial Markets and DeFi
In the financial field, volatility refers to the frequency and magnitude of asset price fluctuations within a certain period of time. It is important to note that volatility does not measure the direction of price movement, but the degree of movement. Although two different assets may have the same rate of return, the more volatile asset fluctuates more in price and is generally considered riskier.
Volatility can be further broken down into two different types of asset volatility measures: realized volatility and implied volatility.
Realized Volatility (RV)
Realized volatility (also known as historical volatility) measures how much an asset's price has moved over a specified time interval in the past. Prices are measured at regular time intervals, so the larger the price movement, the higher the realized volatility. Realized volatility is used in various financial derivative instruments, such as volatility futures and volatility options, to allow market participants to speculate on or hedge against the volatility of a particular market.
In addition, realized volatility helps determine the "normal" ranges for an asset's price movement, so when prices fall outside of these ranges, parameters used in financial products can be adjusted to reduce risk exposure. In addition, because realized volatility data can be used to measure the degree of market risk of an asset, it can also be used to adjust leverage, borrowing utilization and collateral coverage, and rebalance portfolio asset allocation to meet risk targets.
An example of annualized BTC volatility over the past 30 days ( source ).
Implied Volatility (IV)
While realized volatility measures volatility that has occurred in the past, implied volatility is a forecast of future market expectations. It is important to note that implied volatility is not a prediction of the direction in which an asset's price will move. Conversely, a high implied volatility indicates that the asset price is likely to fluctuate significantly, either up or down, while a low implied volatility indicates that the asset price is unlikely to experience large increases or decreases, but remains relatively stable.
Implied volatility is a metric used by market participants to estimate future volatility and is often used to price options contracts, with high implied volatility leading to higher option premiums.
Chart of the CBOE Volatility Index (VIX), a popular measure of stock market volatility expectations, derived from options on the S&P 500, from 2004 to 2020 (source ) .
Data on an asset's volatility, whether realized or implied, is a key component of sound financial markets. It not only enables appropriate pricing and risk management strategies, but also facilitates the creation of options and markets for institutions to hedge their exposures. Making the volatility data available on the chain through the oracle machine and used in the DeFi ecosystem will enable dApps to launch new products and incorporate advanced risk management strategies, promoting the practicality and maturity of the entire DeFi ecosystem.
Chainlink's method for calculating asset volatility data
As an industry-standard Web3 service platform, Chainlink is very flexible in terms of the types and delivery methods of on-chain datasets. As demand for realized and implied volatility datasets continues to grow, we have worked closely with the developer community to deliver two new product solutions for each type of asset volatility measurement, currently on testnet use.
Chainlink Implements Volatility Data Feed
To enable DeFi developers to use realized volatility data in on-chain applications, we have introduced a new data source category (Chainlink realized volatility data source ) with the same decentralization and Minimize the trust attribute. Additionally, these new data sources leverage the same advanced data providers used in existing Chainlink price oracles, ensuring consistency between an asset’s observed price and volatility.
These data sources are enabled by enabling volatility to be based on historical data, allowing data providers in the Chainlink ecosystem to adopt a consistent, collectively agreed upon calculation methodology. Specifically, the data providers opted for the " close-to-close " methodology, an established standard in traditional finance, to reflect the 24/7 nature of the cryptocurrency market, and refreshed the data at ten-minute intervals. Sample price data. The data provider then uses off-chain calculations to calculate realized volatility over three rolling time windows: 24 hours, 7 days, and 30 days. Finally, multiple Chainlink node operators ingest this data across the oracle network and combine it into an aggregated oracle report, which is then published on-chain for consumption by DeFi applications.
Chainlink implements the process of the volatility data source.
The Chainlink implementation volatility data feed covers 24-hour, 7-day, and 30-day lookback periods and has been tested live and verified for usability on four blockchain testnets : Arbitrum Goerli, Avalanche Fuji, Ethereum Sepolia, and Polygon Mumbai . Initially supported data sources include realized volatility of BTC/USD, ETH/USD, LINK/USD, etc.
Get Implied Volatility Using Chainlink Functions
To obtain implied volatility datasets, DeFi developers can use Chainlink Functions - a Web3 serverless developer platform that can fetch data from any API and run custom calculations. Direct connection to data provider's implied volatility API via dApp. Users can have the final say on spending and calculating implied volatility according to their protocol-specific requirements.
An example flow of how implied volatility data obtained through Chainlink Functions can be used in a DeFi application.
Supporting implied volatility data through Chainlink Functions is a deliberate design decision compared to using aggregated data sources, because implied volatility is a prediction of future asset price fluctuations. Unlike realized volatility or reference price data, implied volatility has no observable real data, only forecasts. Additionally, given the current structure of the crypto options market, there are differences in expiration dates and strike prices across exchanges, as well as differences in the approaches taken by data providers. As such, there is no single standard method of calculating implied volatility.
As such, Chainlink Functions provides the ideal infrastructure solution where dApps can source implied volatility data from as many data providers as they need and have final say over their internal calculations without having to deal with their own base of oracle nodes facility management.
Implied volatility data can be brought to the testnets of Avalanche Fuji, Ethereum Sepolia, and Polygon Mumbai through Chainlink Functions before Functions is officially released to the mainnet. DeFi developers can apply for beta access through the Typeform below.
Integrating asset volatility data in DeFi
Asset volatility indicators are important inputs to financial risk models. By providing realized volatility and implied volatility data to the chain through Chainlink's decentralized oracle network, DeFi applications can dynamically adjust their risk parameters to reflect changes in market conditions. By leveraging Chainlink's other services, such as Chainlink Automation , the risk management process can be carried out efficiently and reliably without human intervention.
With realized volatility and implied volatility data provided on-chain, the following DeFi application scenarios can be enhanced by adding functionality or security measures:
- Lending protocol: Improve the capital efficiency of mortgage loans by dynamically and automatically adjusting risk parameters (such as loan size, liquidation incentives and liquidation factors) to match the volatility and liquidity of collateral.
- Leveraged trading: When volatility exceeds expectations, leverage ratios and/or maintenance margins can be adjusted to limit the risk of the agreement until market conditions return to the benchmark's "normal" level.
- Option Pricing: Using realized volatility to construct volatility surfaces when illiquid options markets cannot reliably measure implied volatility.
- Money Management: Manage portfolio allocation and risk exposure more effectively by taking into account historical and forecasted future volatility.
Chainlink realizes the introduction of volatility data sources and obtains implied volatility through Chainlink Functions, providing additional high-quality data points for the DeFi ecosystem, further enhancing the ability of the protocol to manage risks and create more complex financial products.
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