|Stand-Alone Volatility||Stand-Alone Volatility is estimated using the backtest method. It is defined as being equal to the annualized standard deviation of the backtested returns. KeyQuant’s Risk Management Team uses 254 (number of trading days per annum) backtested daily returns exponentially weighted (α = 99%).||KRisk - Exponential Volatility.pdf|
|Ex-Ante Volatility||Ex-Ante Volatility is estimated using the current portfolio positions. We backtest the returns of the current portfolio and calculate the annualized volatility of this portfolio. KeyQuant’s Risk Management Team uses 254 (number of trading days per annum) backtested daily returns exponentially weighted (α = 99%).||KRisk - Exponential Volatility.pdf|
|Contribution to Portfolio Volatility||The Contribution to portfolio volatility is a measure of risk contribution of a position (expressed in volatility), accounting for correlation between positions held at the portfolio level. It represents the level of volatility of a specific position vis-a-vis the global portfolio.
Contribution to portfolio volatility of a position is equal to ωi x βi with β = Ω x ω / volatility and Ω a covariance matrix with an exponential smoothing (α = 99%).
The volatility of the portfolio is equal to the sum of the contributive volatility of each position.
|Margin to Equity||Margin to Equity figures are estimates. They are calculated using exchange margin requirements. Margin to equity is calculated using the sum of estimated initial margin divided by NAV.
Actual margin requirements may vary and margin analysis provided is not intended to be an accurate representation of actual initial margin requirement or margin to equity ratio.
|Global Economic Factor ("GEF")||The Global Economic Factor ("GEF") is a proprietary indicator which measures the strength of global economic trends to optimize portfolio exposure. The GEF can vary anywhere from 0.5 to 1.5.|
|Value at Risk||Value at risk (VaR) is a measure of the risk of loss for investments. It estimates how much a set of investments might lose over a target horizon within a given level of confidence. KeyQuant’s Risk Management Team estimates VaR using the historical simulation approach.|
|Conditional Value at Risk||The CVaR is derived by taking a weighted average of the “extreme” losses in the tail of the distribution of possible returns, beyond the value at risk (VaR) cutoff point. It is estimated using the historical simulation approach and is equal to the average of the backtested returns which are worse than the VaR threshold for the same confidence level and given time horizon.|
|Historical Stress Tests||Historical scenarios have been selected by identifying events from 1990 when markets have displayed large price movements.|
|Series of future contracts||A futures contract has a set expiration date. In order to analyze price trends and estimate risk, one needs to create a continuous futures price series by concatenating the different maturities of this contract.|
|SG Trend Index||The SG Trend Index is designed to track the 10 largest (by AUM) trend following CTAs and be representative of the trendfollowers in the managed futures space. The SG Trend Index is equally weighted, and rebalanced and reconstituted annually.
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|Sharpe Ratio||Sharpe ratio is a measure of excess portfolio return over the risk-free rate relative to its standard deviation. It measures the risk-adjusted performance of an asset. KeyQuant’s Risk Management Team calculates it by dividing the Annualized Return of the asset by its Realized Volatility and assume a zero risk free rate.|
|Downside Volatility||Downside Volatility is calculated like Realized Volatility but only negative returns are taken into account. It has been designed to address downside risk.|
|Sortino Ratio||Sortino Ratio is a modification of Sharpe Ratio where Realized Volatility has been replaced with Downside Volatility. Thus, it presents a more realistic picture of the downside risk ingrained in a financial asset.|
|Skewness||Skewness is a measure of asymmetry of a distribution function. The Skewness for a normal distribution is zero. Negative values for the skewness indicate data that are skewed left (i.e. left tail is long relative to the right tail).|
|Kurtosis||Kurtosis is a statistical measure that is used to describe the tails of a distribution. Distributions with high kurtosis (>3) exhibit heavier tail than the normal distribution. For investors, high kurtosis of the return distribution implies that the investor is more likely to experience extreme returns (either positive or negative) than it would be under the normal distribution.|
|Ulcer Index||The Ulcer Index (“UI,” or “Ulcer”) measures both the depth and duration of drawdowns and is one of the rare risk indicators that is path-dependent. Its return-adjusted version is a better indicator than the Sharpe Ratio for investors who are more concerned by drawdowns (vs. volatility). The name of the index comes from the supposition that drawdowns cause stress and ulcers to investors. Ulcer is calculated by taking the quadratic mean of the drawdowns.|
|Adjusted Leverage||Total notional amount for futures contracts (duration adjusted for bonds and interest rates).|