Time to Manage Your Risk

Articles tagged with: Risk

25 August 2015

A Look at the Recent US Equity Market Drop

To say that recent activity in the US Equity market has been unprecedented would certainly be an understatement. On Friday, August 21st, and Monday, August 24th the S&P 500 index fell 3.24% and 4.02% respectively. To put these declines in perspective: based on a year's worth of data, Monday's market drop was equivalent to an over 4 standard deviation event, making these declines extreme by any statistical measure:

Date   Return   Standard Deviations
August 20   -2.13%   2.86
August 21   -3.24%   4.17
August 24   -4.02%   4.83

For some perspective, to correctly estimate last Friday's decline using a year of daily data through August 20th, a parametric VaR would require a confidence interval of 99.9984%. This equates to the probability of such an event occurring to be less than once in 10,000 observations.

On the subject of VaR, one would require using data going back to 2008 to anticipate the returns just seen on Friday, August 21st:

For Monday, August 24th's decline of 77.68 points, only a conditional VaR using 7-years of data (again, including 2008) correctly estimated the S&P 500's decline that day:

Which brings us to why stress-testing is exceedingly important as a complimentary set of exposure analysis. Here we shock an at-the-money option on the SPY's starting with data as of August 20th using a -5% move in the S&P 500 Index:

The benefit of stress-testing is its lack of reliance on statistical (historical) data. Regardless of the presence of extreme events in a given data set (or detrimentally, in this case, the lack thereof), stress-testing allows for simulation of market shocks in all environments, volatile, extreme, or not.


The results above were calculated using The RiskAPI Add-In, our unique software client which allows fund managers to access a whole spectrum of on-demand portfolio risk analysis calculations.

23 April 2015

Highest (and Lowest) S&P 500 Components by Beta

Current top 10 highest beta components of the S&P 500 index:

First Solar IncFSLR1.9034
TripAdvisor Inc.TRIP1.8698
United Rentals IncURI1.8450
Newfield Exploration CoNFX1.8196
Micron Technology IncMU1.8081
Harman Intl Industries IncHAR1.7811
Skyworks Solutions IncSWKS1.7116
Allegheny Technologies IncATI1.6592
LyondellBasell Industries N.V.LYB1.6427
Freeport-McMoRan IncFCX1.6155

Current top 10 lowest beta components of the S&P 500 index:

Sigma-Aldrich CorpSIAL0.0244
Pepco Holdings IncPOM0.2402
HCP IncHCP0.2414
Family Dollar Stores IncFDO0.2892
Health Care REIT IncHCN0.3474
Newmont Mining CorpNEM0.3502
Ventas IncVTR0.3719
Southern CoSO0.4073
Duke Energy CorpDUK0.4118
AvalonBay Communities IncAVB0.4400

All calculations are as of 4/22/2015, executed on 1-year of daily data, adjusted for corporate actions.

The results above were calculated using The RiskAPI Add-In, our unique software client which allows fund managers to access a whole spectrum of on-demand portfolio risk analysis calculations.

27 October 2014

The Return of Volatility: Which VaR Model Measured Up?

Earlier this month saw volatility return to nearly every tradable market with a serious vengeance. The long-running low volatility (or no volatility, to some) state of affairs had apparently come to an end. For those of us that construct and test risk systems, this month has provided a unique in situ series of observations with which to critically examine the performance of certain VaR models.

Defining the low-vol environment

With regards to equity markets, at least, it will certainly come as no surprise that volatility has been low and getting lower for quite some time. Below is a chart of rolling 90-day S&P500 realized volatility starting on Jan 1 of this year through September 24th (one day prior to the first large down moves seen in the US Equity markets starting on 9/25/2014):

Even taking the period's peak volatility of ~ 13%, these are historically very low volatility levels for the US Equity markets. For the purposes of this evaluation we will consider the "pre-volatility" period to be from Jan 1-September 24th of 2014.

Defining the volatility-inducing events

Starting on September 25th, the S&P500 experienced a series of several outsized, negative returns:

DateReturn %Return Points

These were certainly enough observations to make even the most ardent believer in the "endless-low-volatility-bull-market" theory question his or her convictions.

Which VaR model did best?

Using data from January 1 to September 24th, we ran the S&P500 through the Parametric, Historical Simulation, and Monte Carlo VaR models offered in RiskAPI, both under 95% and 99% confidence levels. Here are the results (in index points):

ModelConfidence VaR CVaR
Parametric95% -20.69 -31.19
Parametric99% -29.27 -39.34
Full Historical95% -18.56 -28.84
Full Historical99% -39.13 -40.05
Monte Carlo95% -22.21 -27.00
Monte Carlo99% -31.29 -38.00

The last two columns in the results table above show the output of Value at Risk (VaR) and Conditional Value at Risk (CVaR) across each of the three models and two confidence levels. While VaR shows the predicted return event based on a statistical model, CVaR shows how large the actual historical period losses that exceed this prediction were, on average.

By far, the worst day for the S&P this month was on October 9th, with a loss over 40 points, or more than 2%. As a risk manager, being able to forecast such a loss well-prior to experiencing it would clearly be advantageous. At the 95% confidence level, no model using either VaR or CVaR came close to predicting the loss on October 9th. However, this is more of a function of how rare this event was than the efficacy of 95% VaR. Remember that we fully expect a 95% VaR result to fall short 5% of the time.

Using a 99% confidence level, the Full Historical model came quite close to forecasting this event using the "low volatility" data alone, while CVaR did very well across all models. The performance of CVaR in this situation speaks very highly of this metric, showing that even with event-limited data, one is able to predict large losses by explicitly examining behavior at the "tail" loss region of a distribution.

For completeness, here is what realized volatility looks like taking events since September 24th into account:

The results above were calculated using The RiskAPI Add-In, our unique software client which allows risk practitioners, portfolio managers, and traders to access a whole spectrum of on-demand portfolio risk analysis calculations.

<<  1 2 3 4 [56 7 8 9  >>