Catching Up With Volatility

The last few years have seen significant volatility in the financial markets.  This has highlighted a basic issue with the popular simulation models used in financial institutions: models have a hard time catching up with volatility.

In other words, these models react slowly to changing volatility conditions, causing the risk metrics to be out of sync with the actual world, and potentially failing to predict significant losses with both business and regulatory repercussions.

It is not that the simulation methodologies are necessarily wrong.  Instead, it’s that they have limitations and work best under “normal” conditions.

Take, for example, historical simulation. Most banks for regulatory purposes use historical simulation to calculate VaR.  During this process a considerable amount of historical data is included to ensure a high confidence level for predicted values. Historical simulation, however, rests upon the assumption that there will be nothing new under the sun – an assumption that has been proving wrong since 2008.  Financial markets have – unfortunately – over the past three years set new records in volatility and free falling, at least in the context of the historical window typically included in such calculations.

Another popular simulation methodology is covariance-based Monte Carlo simulation where the covariance matrix is based on recent historical data. This is, again, limited by the events captured in the historical window and can also reduce the effects of extreme events. The covariance matrix can furthermore suffer from Simpson’s paradox: as correlations between risk factors during tumultuous times get reversed a covariance matrix based on those times can look like little or no correlation.

But there is help to be had:

If the issue is that the historical window is not including enough representative events, then the historical changes can be augmented with specific events or hypothetical scenarios. This might, however, require a more free-style method of simulation.

If issues arise because too much “irrelevant” history is included in the data used, thus drowning out the important events, then a shorter or more selective set of data can be used.

Choosing a shorter window can cause the confidence level of the results to decrease. However, if possible, switching to covariance-based Monte Carlo simulation can alleviate this effect and will not require more data.

If extreme events are either dominating or drowning in the covariance matrix, a solution might be to have multiple covariance matrices at hand and choose among them based on signal values in the data. This can also remedy issues with correlation reversal. Again, this should not require any new data.

A more costly, but also more accurate, method is to formulate statistical models for the risk factors. This allows for explicit modeling of volatility and how fast it should be incorporated in risk metrics.

Finally, choosing the methodology that is most appropriate for each risk factor is obviously an optimal approach if the resources are available.

We’re Not Perfect

Imagine the Masters has finished in a tie: Tiger Woods and Phil Mickelson are heading to a playoff. All Phil needs to win is a putt. You rush over to the next hole to find the perfect spot in case he misses. Then, suddenly, you hear the roar of thousands of people. Phil has just earned another green jacket! "What was I thinking?!" you mumble to yourself as you head home.

What explains peoples' inevitable deviation from rational thought? According to the author of Against the Gods, Peter Bernstein, these deviations can be explained by decision regret, endowment effect, and myopia In fact all are applicable, in some form, to the most rational investors.

David Bell explains that decision regret is "the result of focusing on the assets you might have had if you had made the right decision." From an investor's standpoint, for example, decision regret comes from selling stock and watching it sky rocket soon after. This, then, promotes the irrational behavior of selling low and buying high.

The endowment effect is another human flaw that leads to irrational behavior. Richard Thaler defines this phenomenon as "our tendency to set a higher selling price on what we own than what we would pay for the identical item if we did not own it." This makes sense as irrational because an investor’s price to sell is different than his price to buy – whether you own it or not should not matter.

The final human flaw is myopic sight: not being able to see far enough into the future to make rational decisions. This concept is of particular importance to investors in volatile stock markets, mainly because stocks do not have a maturity date. A volatile stock market is an environment that, Bernstein states, is "nothing more than bets on the future, which is full of surprises."

Ever since Daniel Bernoulli's thoughts on utility and risk aversion in the 18th century, behavioral economics has been studying irrational behavior. It is understood that not all investors will follow the same rational model. If they did, then everyone’s investment portfolios would look exactly the same. There has to be winners and losers in investing. However, if irrational thought can be deterred then one just might catch the game winning putt.

Coming Down the Assembly Line: Automated Form PF Reporting

Regulate Your Risk…GET A CAR!
Commuters who use public transportation encounter the risk of being tardy on a daily basis. There are so many factors unaccounted for that the probability of arriving on time is not in their favor. What to do? How can they eliminate the chance of, say, a subway breakdown or late bus?

An answer: they can buy a car.

A similar parallel can be drawn to the 2008 financial crisis. In response to the crisis, the Dodd-Frank Act established the Financial Stability Oversight Council (FSOC). This council’s mission is to monitor and respond to systemic risks affecting financial markets in the United States. How can the FSOC hedge their risk of being too late to a financial crisis that would crush the economy?

An answer: Form PF.

The process of automating Form PF reporting is a daunting task that will soon come to fruition thanks to the collaborative efforts of The Financial Risk Group and ConceptOne. It starts with the building blocks of data validation and ends with a final product ready for analysis by the FSOC. Within this progression, certain data serves as a direct input to the report while other data is used to calculate output data that will be implemented into the report. An added feature includes tracking the data used to answer the questions on Form PF. This feature is especially important for auditing purposes. The process has already begun rolling down the assembly line. While most private fund advisers will need to begin filing for their fiscal year or fiscal quarter ending on or after December 15, 2012, some funds will need to begin as soon as June 15, 2012.

Those that qualify for the June 15, 2012 date are advisers with at least $5 billion in assets under management (AUM) attributable to hedge funds or private equity funds and liquidity fund advisers with $5 billion in AUM, attributable to liquidity funds and registered money market funds.

Much like car manufactures who build specific models to cater to customers’ needs sections of Form PF are designated to qualifying advisers. All SEC-registered advisers with at least $150 million in private fund AUM must file. Those with less than $1.5 billion in hedge funds, $1 billion in liquidity funds and registered money market funds, or $2 billion in private equity funds are considered small. These advisers must file only once a year and within 120 days of the end of their fiscal year. Information provided by “small” private fund advisers is significantly less in comparison to “large” private fund advisers. Large hedge fund advisers must file within 60 days of the end of each fiscal quarter, large liquidity fund advisers within 15 days, and large private equity fund advisers within 120 days.

Safety First
The key to safety is having a structurally sound foundation. For a car, that would be a solid chassis. For the automated Form PF design, that would be staging tables. Much like the chassis of a car, properly thought out staging tables provide the framework for the rest of the process. These staging tables show us where to put data in order for the process to be as streamlined as possible. Luckily for us, the structure of these tables was provided by ConceptONE.

To illustrate the value of staging tables, let’s assume that a staging table missing a column is the same as a chassis missing the proper door mounts. In the case of the car, the assembly would stop there. The chassis would be noted as an exception and the proper course of action would be taken to fix the problem. Staging tables provide a similar function. If the next step in the process is to pull the data from the staging table but the column doesn’t exist, the code creates an exception report and stops execution.

“Take me home, country roads”
Cars don’t always have the luxury of smooth roads, just as we in the financial risk industry don’t always have the luxury of valid data. Until that glorious day when we do have valid data, something needs to be created to smooth out those “potholes” of data. Just as employees on the assembly line bolt on the suspension, the Form PF process bolts on validation.

Form PF’s validation is relatively complex: it requires validation on multiple inputs that may vary depending on the desired report. The backbone of the Form PF validation is metadata (provided by ConceptONE ). This information defines the required data and provides a list of valid inputs. Utilizing this information allows the process to accurately validate data and create exception reports in case “potholes” of data are encountered. Suspension helps keep a car running smooth on bumpy roads; validation helps keep the process running smooth when invalid data is encountered.

Vroom… Vroom…
The critical element in a car is the engine; for the Form PF process it is calculations. Just as an engine cannot function without gas, calculations cannot function without data. Based on specific questions, data is pulled from the staging tables into working tables where the calculations are performed. Like a computer chip in a car that regulates fuel consumption (to comply with regulations), a collection of calculation rules for Form PF serves the same purpose. The rules further subset the data to specify exactly which variables should be calculated and how they should be calculated for optimal Form PF reporting performance.

That New Car Smell
A car on the assembly line would probably be hard to recognize for any average Joe until it gets its body panels put on. The same can be said about the Form PF automation. If one were to look at the code and calculations, one would have a tough time guessing what the final product would look like. That’s where a nice and shiny output report comes in to play. Different sections of the Form PF report have to be filled out based on the type and size of the private fund adviser, just like different body panels are used for different models of a car.

While body panels are generally large and basic, the real details occur within the interior of the car. The output reports are comprised of the same idea. For example, direct input data (e.g., the seats of a car) is something that is pulled from a specific field multiple times that was never run through calculations and will probably never change. This can be information such as identification numbers or addresses. What about options like a sleek CD player or fancy navigation system? Just like these options, the output reports show information from specific calculations based on the desired result. There are plenty of ways to get a result, but only one way to get to the result that you need. And let’s be honest, you want that fancy navigation system.

“But why do they put the guarantee on the box?”
Source tracking plays an important role in the automation of Form PF reporting. Fortunately, source tracking is why customers keep coming back to the dealership (The Financial Risk Group / ConceptOne). It’s the “warranty” that ensures accuracy of the data in the staging tables loaded through the standardized data loader. Source tracking provides multiple reports that display input tables while also highlighting the pertinent columns associated with particular questions on Form PF.

The necessity of source tracking is especially evident when auditing data. Much like unknown malfunctions in a car, unknown errors can arise in data while updating, storing, and using it to complete Form PF. Source tracking provides accessibility to input data to help avoid fines and other potential punishments. Internal auditing is also made easier through source tracking. The “warranty” provides bumper to bumper coverage for as long as Form PF reports park in the Investment Adviser Registration Depository garage.

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