Overview

The Price Discrepancy Metric measures the deviation between the highest and lowest prices of a cryptocurrency pair relative to its Volume-Weighted Average Price (VWAP). It provides insights into the price stability and potential volatility pressures on the pair.

Mathematical Background

The Price Discrepancy Metric consists of two key components:

Metric in the API Response

[
  {
    "timestamp": "2024-01-20T00:00:00.000Z",
    "pairid": "usdt-usd",
    "open": 1,
    "high": 1.0017,
    "low": 0.9971,
    "close": 0.9991,
    "volume": 324089.0553,
    "vwap": 0.9999,
    "tradecount": 261,
    "discrepancy": {
      "low": {
        "value": 0.2796,
        "marketvenueid": "ascendex"
      },
      "high": {
        "value": 0.1805,
        "marketvenueid": "ascendex"
      }
    }
  }
]

Usage Example

Based on a deeper analysis of the exchanges with the most significant price discrepancies for the USDT-USD pair over the 24-hour period, we can:

• Identify the exchanges with the highest average high price discrepancies and the highest average low price discrepancies.
• Determine the times of day when these discrepancies were the most pronounced.

By doing this, we can pinpoint specific exchanges and time periods where the USDT-USD pair showed significant deviations from the expected price, which may indicate issues such as liquidity problems or external market forces at play during those hours.

Exchanges with Significant Discrepancies:

• Binance US (High) had the highest average high discrepancy of approximately 0.27% with a maximum high discrepancy reaching 0.4124%.
• AscendEX (Low) showed the highest average low discrepancy of around 0.719% and a maximum low discrepancy of 2.5987%.

High Activity Periods:

• The hour starting at 15:00 UTC had the highest trade count with 34,298 trades.
• The hour starting at 14:00 UTC had the highest volume traded, which was over 74 million USDT.

Hourly VWAP Observations:

• Hourly VWAPs ranged from $0.9969 to $1.0001, indicating that the majority of trading activity kept the price close to the $1 mark.
• The hourly VWAP data provides a more granular view of the price stability throughout the day, showing minor fluctuations.

Additional Insights Without Much Aggregation:

• Hourly VWAPs, when examined individually, can provide insights into short-term price stability and may reveal patterns or trends not visible in the daily aggregated VWAP.
• Trade count and volume data per hour can indicate periods of high liquidity and activity which could affect price stability and discrepancies.
• Observing hourly trade counts and volumes may also reveal specific hours when the market is more susceptible to volatility or price manipulation.

Implications for Stablecoins

• Stability Analysis:

  • The relatively small discrepancy from the high price suggests good stability. However, the more substantial difference from the low price indicates potential volatility or external market pressures. This is unusual for stablecoins, which are expected to maintain close parity with their pegged value.

• Market Confidence and Monitoring:

  • While the coin mostly hovers around its intended value, the larger fluctuation from the low point could impact market confidence. It’s crucial to monitor such deviations, especially in stablecoins, as they are typically expected to exhibit minimal volatility.

• Alert for Anomalies and Further Investigation:

  • The notable difference from the low price point warrants attention. Such anomalies in stablecoins could signal underlying market dynamics or external factors impacting the coin’s stability. Continuous monitoring and analysis are recommended to understand these fluctuations better.

Visuals

Here is the chart visualizing the high and low price discrepancies for the USDT-USD pair over time. Each point on the chart represents the discrepancy value at a specific hour:

• The green line with circles represents the high price discrepancies.
• The red line with circles represents the low price discrepancies.

The x-axis shows the timestamp (date and hour), and the y-axis indicates the discrepancy values. This visualization helps in understanding how these discrepancies fluctuated over the course of the day.

Integrating with Other Pricing Metrics

While the Price Discrepancy Metric is valuable on its own, integrating it with other pricing metrics (OHLC and VWAP) provides a multi-dimensional view of market behavior:

• OHLC Data: Offers a snapshot of market activity within the specified timeframe, helping to contextualize the discrepancies observed.

• VWAP: Acts as a benchmark for assessing the relative significance of price discrepancies. Consistently high or low discrepancies in relation to VWAP can indicate underlying market trends or anomalies.

• Comprehensive Analysis: Using these metrics together allows for a more nuanced understanding. For example, a day with a high closing price and a high positive discrepancy might suggest bullish market sentiment, while a low closing price with a high negative discrepancy could indicate bearish sentiment.

Applications in Market Surveillance

• Detecting Potential Mispricing: Significant discrepancies in stablecoin prices across exchanges may indicate mispricing issues.

• Identifying Stability Pressures: Large discrepancies can signal risks to a stablecoin’s peg stability due to market pressures.

• Uncovering Coordinated Behaviors: If unusual price discrepancies appear concurrently across exchanges, it may suggest coordinated manipulation.

• Combining with Order Flows: Inspecting pricing gaps alongside order book dynamics can help uncover tactics like spoofing and manipulation.

Considerations for Cryptocurrencies

• Trading often happens on fragmented exchanges versus centralized venues
• Increased potential for price manipulation across disparate exchanges
• Stablecoin volatility due to shifting market sentiments and stability perception

Key Takeaways

• Price Discrepancy measures trading range deviations from the average price across exchanges.
• Significant discrepancies, especially for stablecoins, may indicate risks.
• Concurrent anomalies across exchanges can signal manipulation.
• Multidimensional analysis combining pricing and orders is beneficial.