Risk Management System
Arbor Risk Attribution System is now seamlessly integrated to Arbor Portfolio Management System. Our newest feature enables you to automate your risk reporting needs, covering substantial areas such as VaR , risk ratios and volatility indicators for multiple asset classes.
Risk Management System
Data from our Risk Management System can also be sent on a daily basis to Arbor Reporting Portal. This allows us (or you), to create interactive, stylish and transparent reports with drilldown capability, covering multiple asset classes within the past 12 months.
You can pool these reports together and create slideshows and dashboards on any risk parameters that you want, which will look great for your pitch-decks and management level reporting. These can also easily be viewed on your phone, tablets or PC.
Value at Risk (VaR)
In Arbor’s standard risk reporting package, clients will gain a comprehensive overview of their short term and long term VaR (with 95% confidence level). We will also provide details on each product’s volatility based on its prices within the past year.
STRESS TESTING & STRESSED VAR
Beside regular VaR, our Risk Attributor also enables the users to identify particular periods where prices or return on asset classes, portfolio or individual investments are fluctuating more than the calculated VaR.
We can also include statistical analysis containing price, standard deviation and volatility shocks. In the graph below, you can see that Stressed VaR is generally higher than its regular counterpart in the respective timeframe.
Market Risk Reporting
SHARPE, TREYNOR AND SORTINO RATIO
In our risk reporting system, you can opt in to add the following ratios to your risk reports and dashboard. Sharpe and Treynor ratio provide you with insights on risk-adjusted performance in terms of market risk.
Our report, as shown above, will automate each risk ratio for each reporting month. This makes it easy for you to recognize the fund period when your fund is more volatile.
The green graph above represents Sortino Ratio, which assists you in evaluating your fund return based on the volatility level of your losing positions. A higher Sortino shows the fund period where your fund is more volatile to positions with negative return.
Information Ratio (or IR for short), is useful to measure not only a fund manager’s ability to generate excess return, but also the consistency of the performance of his investment.
A high information ratio shows the period where a fund outperforms its benchmark in terms of return and diversifiable risk, making this ratio especially important for hedge funds with multiple fund managers.
Known as one of the most common types of Alpha used in performance attribution, Jensen’s Alpha can be calculated as your Alpha), adjusted with current risk free rate, market beta and expected market return.
With Arbor Reporting Portal’s flexibility in automating reports fully integrated to our Risk Management System, you can compare the performance of multiple funds versus a single benchmark of your choice, or the other way around.
The graph above shows the Jensen Alpha of a fund, compared to three different indexes (S&P 500, Nikkei 225 and Dow Jones).
Our interactive graphing capability allows you to see which portion each part of this parameter’s breakdown, such as risk free rate, expected market return, or how much each sector that you have invested in contribute to your overall Alpha.
Statistical & Volatility Indicators
SKEWNESS & KURTOSIS
Our risk management & attribution software enables you to view the kurtosis and skewness of your P&L distribution, assisting you in evaluating your fund strategy to achieve the ideal balance between risk, return and diversification.
Skewness measures the sensitivity of your fund return to extreme outcomes. A positively skewed return (skewness > 0) shows the period where your fund performance is less prone to extremely negative outcomes, as shown below.
negative skew (skewness < 0), on the other hand, displays a higher probability for extreme outcomes, which is not preferable for investors with high risk aversion.
Similarly, kurtosis measures the extremity of your fund’s return. A leptokurtic return
(kurtosis > 3), informs you the periods when your fund are less prone to extreme outcomes, whereas a platykurtic return (kurtosis < 3) signals higher probability of extreme outcomes.
The graph above shows the month where your fund kurtosis and skewness are unusually high. We can also group these parameters based on metrics you find important, such as product ID, asset class or investment sector.
Performance Statistics Report
Arbor Performance Attribution Report allows you to see all the risk metrics that have been comprehensively discussed in several paragraphs above.
Not only on risk, you can also gauge some metrics on investor consistency (on standard error and tracking error) and on movement or variability between your fund and its benchmark.
Tracking error measures the consistency of your fund against a benchmark over a period of time. A high tracking error underlines the volatility in your portfolio, for example, a tracking error of 1% assumes that your fund will return within 1%, plus or minus, around its benchmark return.
Therefore, a fund that realized low returns and has a high tracking error shows something wrong with the investments within. This means that the fund is performing worse than the benchmark and is also more volatile at the same time.
Standard error measures the how widely dispersed a range of parameter is from its average. In trading, standard error can be considered as an accurate guesstimate of difference between the opening and closing price of a product.
The higher the standard error of a product, the more likely its closing price deviate from its opening price – which is great for risk-seeking investor, but not ideal for risk-averse traders.
Correlation coefficient shows how likely the value of your fund follows the benchmark you choose.
the correlation between a fund and its benchmark is close to 1 (like the graph above), their movements are more likely to be similar (when index value goes up, fund also goes up).
correlation close to 0 (like the graph above) shows almost a total independence between fund and its benchmark, which means that an increase of your fund return doesn’t have anything to do with the increase of the benchmark return.
On the other hand, if the correlation between your fund and its benchmark is close to -1, they are likely to move in opposite direction (e.g: if your fund value goes up, benchmark value goes down, and vice versa).
COEFFICIENT OF DETERMINATION
Coefficient of determination, also known as ‘R2’ or Goodness of Fit, measures whether a fund / benchmark can be utilized to measure the variability of another fund. Coefficient of determination ranges between 0 and 1.
The closer it is to one, the more likely each factor (e.g: a fund and its benchmark) is dependent one with another. If it’s closer to 0, there’s higher possibility that these two factors are independent from each other.
Reports on risk can be pooled to a dashboard, allowing you to view only relevant data based on your preferred filters, for example: sector, product ID, product type and even risk ratios.
For management level reporting, we also enable Portfolio Management System users to pool their risk report to a slideshow, which allows them to drilldown and view the underlying data within.
Feel free to play around with the dashboard and slideshow displayed above.
To demo Arbor Risk Management & Attribution System, please contact us at email@example.com