As with other big IPO's, the Alibaba deal has drawn a lot of attention from Wall Street - it is the single largest U.S. initial public offering ever - and with that, a likely large amount of institutional investors. As such, chances are high that there are now more than several chief risk officers and portfolio managers grappling with how, exactly, to measure the exposure of this new issue.

Ownership Structure

To begin with, it's worth spending a moment talking about what specifically shares of the NYSE-listed BABA represent. Unlike shares of common stock in a typical public corporation, which resolve to one unit of direct equity in a corporation, shares of BABA are actually units in an offshore, Cayman Islands-based trust, which has a contract to share in the profits of the local Chinese Alibaba corporate entity. This is due to legal restrictions in place by the Chinese government which prohibit direct foreign ownership in Chinese Internet service companies. To get around this restriction, something called a Variable Interest Entity (VIE) was created to allow a foreign-owned investment vehicle to experience correlated returns vs. the onshore Chinese stock. In short, everybody involved agrees that one share in the VIE will track the performance of the onshore-Chinese equity. To be clear, this agreement is only as good as all of the players decide it will be, of these most notably are Alibaba's CEO Jack Ma and the Chinese government. If the on-shore owners or the authorities decide to change or invalidate elements of this agreement, it's not clear what legal recourse foreign investors could have.

Needless to say, there are a great deal of structural risk factors in the very nature of the shares of BABA that need to be carefully considered as part of the systemic risk inherent in owning shares of BABA.

Market Risk of an IPO

Analyzing the market risk of an IPO has always been difficult. By definition, shares in an IPO do not have any historical pricing data, something ex post facto risk calculations rely on heavily to generate exposure analytics. If a stock only has one day's worth of pricing history (as is currently the case with BABA) how does one calculate return observations? How can predictions based on historical data even be made if no historical data exists? So what's a risk manager to do?

Enter data proxying. Data proxying is a mechanism whereby the original market-driven historical pricing used to analyze a financial instrument is replaced with a single or combination of adjusted or derived historical time series. There are several reasons to proxy market data. Chief among them is data scarcity- there simply isn't enough market data available. Another is a belief that the market is mis-pricing a security and that proxied data better reflects the "real" historical value and risk.

The source of proxied data can be a geographically relevant index, a sector-relevant stock, or a basket of financial instruments chosen for macro-economic or fundamental reasons. In the case of a proxy for an IPO, the idea is to come up with a replacement for the non-existent historical pricing data and provide instead a suitable time series that approximates the return behavior of the stock, pre-IPO. With the proxied data in-hand, useful exposure analytics can be derived and utilized as is the case with instruments that have abundant pricing information.

Proxying in RiskAPI

The RiskAPI system provides a built-in proxying mechanism that enables the return history of an existing data set to be mathematically joined to any available IPO data. Proxying is done via a dedicated symbol format which contains the information necessary to construct the proxy. In the case of BABA, this can be done as follows:

SPX;BABA;09-18-2014.PRX

The result of the proxy symbol above is a data set that combines the return history of the S&P 500 Index prior to 9/19/2014 (the IPO date) and any available data for BABA since the IPO. In contrast to a wholesale replacement of BABA with a position in the S&P 500, no quantity adjustment needs to be made in order to match the position size of the proxy. The market value of 10,000 shares of BABA and 10,000 shares of SPX;BABA;09-18-2014.PRX will be equivalent.

The proxy above represents a crude approximation of pre-IPO behavior using the returns of a US equity index. This proxy would only be appropriate if one holds the view that the S&P 500 is a reasonable substitute for the behavior of BABA pre-IPO. In order to more closely approximate the economic risk of BABA shares, a more fitting proxy could use the China-based Shanghai Composite Index:

180167;BABA;09-18-2014;USD.PRX

Note that even though the Shangahi Index is a China-based equity index and as such would represent a CNY-denominated asset, the RiskAPI system allows users to denominate the proxy in US Dollars (for example) allowing the proxy to sample the market returns only of the index, eliminating any currency exposure, which would not be present for holders of BABA, a stock listed in the US and denominated in dollars. Below are sample results run using the RiskAPI Add-In with both forms of proxying:

The output above shows the results of three different forms of proxying: outright substitution (note the different quantity change made to match the market value), simple US-based index proxying, and local-market index proxying. Note the significant difference in VaR as a result of the application of the Shanghai Composite index vs. the S&P 500.

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.

A look at the 10 (YTD) highest beta constituents of the S&P 500:

Constituent	Symbol	Beta
TripAdvisor Inc.	TRIP	2.542171
Alexion Pharmaceuticals	ALXN	2.432534
Facebook	FB	2.245835
E*TRADE Financial	ETFC	2.204229
Harman Intl Industries	HAR	2.038566
Amazon.com	AMZN	1.959263
Under Armour A	UA	1.907848
Schwab Charles Corp	SCHW	1.907240
Biogen Idec Inc	BIIB	1.901673
Micron Technology Inc	MU	1.873427

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.

Examining Downside Market Exposure: Filtered (Downside) Beta

Beta (aka simple linear regression) has long been a widely used measure of single name and portfolio exposure to a benchmark. One of the drawbacks of beta, however, is that it lumps in all returns, regardless of direction, into the same analytical basket. Those wishing to measure index exposure isolating negative portfolio return observations are mostly out of luck. The best they can do is pick a historical time period where the asset or portfolio of interest lost money most of the time.

What is needed, therefore, is a version of beta that only includes observations where an asset exclusively experiences negative returns. The objective is to understand what the asset's exposure to a benchmark is purely on days where it loses value. In portfolio manager-speak, this translates into: "On days when we lost money, what was our exposure to the market?". This is an interesting data point for many alpha-seeking stock pickers: their objective is to find single name stocks that enjoy gains when the market is bid and yet do not wholly participate in market crashes.

The Mechanics of Filtered Beta:

Although we have been talking about a "downside" measurement, in truth, the generalized case involved here is the filtering of return observations according to arbitrary criteria. For example, we may choose to look at every return in a stock above 0.5%. Therefore, downside beta is merely a special case where the returns are selected according to the following rule:

Xi < 0

Were each Xi is a return observation in the target asset's return history. For example, if an index and a portfolio have the following returns:

Index	Portfolio
+0.004	-0.006
+0.010	+0.007
+0.018	+0.007
+0.004	+0.007
+0.002	-0.010
+0.001	-0.003
-0.005	+0.028

For downside beta, only the returns highlighted in bold will be used in the resulting beta calculation. These are the set of returns that correspond to days when the portfolio lost money. Let's take a look at a real-world example using downside beta:

Citigroup (NYSE:C) Vs. S&P500 Index

Taking daily data for both Citi and the S&P since Jan 1, 2013 results in a standard beta of 1.60. Assuming this beta is sound, this tells us that on a given day, we can expect Citi's returns to be about 60% more volatile than the index's returns. If the index has 1% loss, Citi's stock value should decrease by 1.6%. Since we are looking at ALL returns (both positive and negative), we expect this statement to hold true for up days in the market: i.e. if the market rallies 1%, Citi should rally even more: 1.6%.

Now let's look at downside beta for the same period. Applying the filtering mechanism above, we find that the downside beta is down to 1.15. This is good news for a portfolio manager who is long Citigroup. What we've learned here is that on days where the stock loses value, its returns were very nearly in line with those of the market. In other words, when we lost money in our Citi position, in percentage terms, our losses did not exceed the market's losses.

The filtered beta feature is currently only available in RiskAPI Enterprise and will soon be released in the RiskAPI Add-In

The results above were calculated using The RiskAPI Enterprise service, the flexible, high-performance software client connecting funds and fund service providers to PortfolioScience's proprietary risk engine calculations.