Risk Databases

Intraday estmates of price volatility, risk and perfomance
PE Database
Product Datasheet (.pdf)
  • 01/2013 to today
  • 87,786 individual datasets
  • 8,000+ symbols (US stocks, ETFs & indices)
  • Available via Quandl

Volatility and Risk Factors Database

Updated daily, this database offers data on volatility and other risk factors for 8,000+ financial instruments, including stocks, stock indices and ETFs. Subscription is availble via Quandl.
Metrics Coverage

Code Description
RET Average return (expected return)
VAR Variance (volatility, squared standard deviation) of price returns
SKEW Skewness of price returns
KURT Kurtosis of price returns
MOM3 3rd moment of price returns
MOM4 4th moment of price returns
ALPHA Alpha (Jensen's alpha, excess return, ex-post alpha) of price returns
BETA Beta (market sensitivity) of price returns
HURST Hurst exponent (long memory measure) of price returns
FDIM Fractal dimension of price returns
Window Length

Each metric is available in multiple flavors, depending on the rolling window length used for its calculation. Metrics at longer windows (e.g. 1 week) are less sensitive to recent price changes, while capturing important aspects of long-term price behavior. Same metrics at shorter time windows (e.g. 1 day) use fewer price points, but are much more responsive to the latest market dynamics (e.g. new volatility regime after a news release).
Length Description Available via Quandl
1_WEEK 1 week (5 trading days) window Yes
1_DAY 1 day window Yes
1_HOUR 1 hour window No
1_MIN 1 minute window No
1_SEC 1 second window No
Methodology

Metrics are computed using a time series of high frequency price returns of an instrument in a rolling window of given length and then rescaled to a 1 day horizon. PortfolioEffect features a next-generation “smart” model pipeline for high frequency data. Returns are processed with a series of auto-calibrating models for high frequency market microstructure noise, price jumps/outliers, fat distribution tails (extreme events), long memory (price fractality) and intaday risk factors (single index model).
PM Database
Product Datasheet (.pdf)
  • 01/2013 to today
  • 148,057 individual datasets
  • 8,000+ symbols (US stocks, ETFs & indices)
  • Available via Quandl

Risk and Performance Metrics Database

Updated daily, this database offers data on risk and performance metrics for 8,000+ financial instruments, including stocks, stock indices and ETFs. Subscription is availble via Quandl.
Metrics Coverage

Code Description
SHARPE_RATIO Sharpe ratio
VALUE_AT_RISK Value-at-Risk (at 95% and 99% confidence intervals)
EXPECTED_SHORTFALL Expected shortfall, expected tail loss (at 95% and 99% confidence intervals)
MOD_SHARPE_RATIO Modified Sharpe ratio (at 95% & 99% confidence intervals)
TREYNOR_RATIO Treynor ratio
SORTINO_RATIO Sortino ratio at 0% return threshold
INFORMATION_RATIO Information ratio
STARR_RATIO STARR ratio (at 95% and 99% confidence intervals)
RACHEV_RATIO Rachev ratio (at 95% and 99% confidence intervals)
GAIN_VARIANCE Gain variance
LOSS_VARIANCE Loss variance
GAIN_LOSS_VARIANCE_RATIO Gain to loss variance ratio
Window Length

Each metric is available in multiple flavors, depending on the rolling window length used for its calculation. Metrics at longer windows (e.g. 1 week) are less sensitive to recent price changes, while capturing important aspects of long-term price behavior. Same metrics at shorter time windows (e.g. 1 day) use fewer price points, but are much more responsive to the latest market dynamics (e.g. new volatility regime after a news release).
Length Description Available via Quandl
1_WEEK 1 week (5 trading days) window Yes
1_DAY 1 day window Yes
1_HOUR 1 hour window No
1_MIN 1 minute window No
1_SEC 1 second window No
Methodology

Metrics are computed using a time series of high frequency price returns of an instrument in a rolling window of given length and then rescaled to a 1 day horizon. PortfolioEffect features a next-generation “smart” model pipeline for high frequency data. Returns are processed with a series of auto-calibrating models for high frequency market microstructure noise, price jumps/outliers, fat distribution tails (extreme events), long memory (price fractality) and intaday risk factors (single index model).