TAIL RISK MONITORING

Pascal vander Straeten; Conference North Dallas Chamber of Commerce; Tail risk management; March 15th, 2016

How tail risk monitoring enables proactivity

Tail risk reporting

• Tail risk has gained a considerable profile as a result of the major ‘shock’ events that characterised the financial crisis as well as the euro debt crisis, and the subsequent market volatility.

• Our research seeks to deepen understanding of the response of institutional investors to these market shocks and their remaining concerns."

Early warning system

• With global credit and capital markets raging, we propose a set of indicators. Some are long term, some short term. Some are investor-sentiment driven, others meticulously follow systemic risk. By themselves they mean very little, but collectively they provide a 20,000 foot view of global market risks.

• The Holy Grail of measuring systemic risk is by monitoring how much banks trust each other around the world. There were so many warning signs before Lehman; before the flash crash in 2010; before the 20% drop in the summer of 2011, and before the 10% May-June 2012 market dive in the S&P 500.

• The closer you are to a “Lehman Moment,” the more systemic risk is in the markets, which could take ten years to normalize.

• The further we sail from Lehman, the longer the runs of complacency will be .

• Realized Vol on Euribor, Libor• Say what you like about Libor and Euribor being fixed in

2005-8, but they have been a solid systemic risk indicator since. Better yet, realized volatility (how much they move day to day) on Euribor and Libor has been amazingly predictive, it was spiking just before the flash crash in 2010, in May 2011 and April 2012.

• Today it’s as calm as it’s been in years. On a scale of 1-10, ten being red alert, the risk indicator here is a 3. This doesn’t mean we’re not going to have a 3-5% pullback soon, which we may. It just means a true systemic sell off 10% or more is not in the cards in the next 1-3 months. We take a look at this every day. When they they start singing, I’ll be right there with ‘em

3 month Euribor rate

Libor 3 month rate

• Two Year Swap Spread • If banks in Europe start pulling away from each other in

overnight repurchase agreements, and short term loans, this is sure to spike. Once again, looking at realized volatility on the 2 year swap spread gives you a two to three week lead time on serious market sell offs. In 2010, 2011 and 2012 before each serious market sell off, realized vol on 2 year swaps was spiking. There was a clear breakout to the upside. Today? It’s in a multi month base, coming off a serious downtrend. On a scale of 1-10, it’s a 3 in terms of any warning whatsoever.

• Repo rates, EONIA• Banks have lots of assets on their balance sheets. They use

these holdings to raise cash on a daily basis. If you know who to call, tracking the cost of this funding is a valuable risk measurement in the market. In the 9 month period before Lehman’s failure, the cost of short term loans like repos were sharply spiking. The same could be said about many French banks in the spring of 2011 as the world was concerned about their exposure to Greece. Today, banks in Italy and Spain are experiencing rising costs in this area. Our systemic risk score here is a 5 and rising.

• TED Spread• Everyone knows what the TED spread is, but it’s still a classic

systemic risk indicator. Our systemic risk score here is a 2.• The TED spread is an indicator of perceived credit risk in the

general economy,[2] since T-bills are considered risk-free while LIBOR reflects the credit risk of lending to commercial banks. An increase in the TED spread is a sign that lenders believe the risk of default on interbank loans (also known as counterparty risk) is increasing. Interbank lenders, therefore, demand a higher rate of interest, or accept lower returns on safe investments such as T-bills. When the risk of bank defaults is considered to be decreasing, the TED spread decreases.

• A Breakout Short Interest on the Largest Banks• In 2007, we were watching this like a hawk in the USA. In

2012, once again it’s Spain and Italy. Let’s face it, the smartest investors play on the short side. The Chanos’s and Einhorns of the world need to borrow large amounts of stock to take a position. Therefore a breakout in short interest in the major bank equities is a bearish sign. In the summer of 2008, early spring 2011 & 2012 there was a classic breakout in this indicator. Today, our systemic risk score here is a 5 and rising.

• Here’s a chart of equity Santander:

• Credit Spread between Big Banks and Industrials• Last April – May 2012, as well as May 2011, the credit spread

between the iTRAXX Main (corporate bonds in Europe) and iTraxx Sub Fin (subordinated corporate bonds in the financial sector in Europe) was blowing out. Financials had a credit spread 120bps wide of industrials… today they trade 5-15bps wide. Since Lehman failed, when 5 year CDS on the big banks is in an uptrend, then the financials are weekly or monthly underperformers in credit (in comparison to other industries). Our systemic risk score here is a 3 and rising. The lessons of 2010, ’11 and ’12 tell us that when these spreads start to really widen, lighten up on stocks in general.

• Tertiary Capital Structure Activity• By and large CFO’s are not stupid. They access the capital markets to borrow money

when the credit markets are healthy. But exactly how much access to capital do they have? This can be measured by the type of debt issuance in the league tables the banks proudly present to the street. Banks are always thrilled with lively capital market activity, and this information is easy to find. In 2007, we saw an explosion of subprime MBS issuance, but another telling area to watch is the PIK Toggle and Hybrid Bond issuance levels. How much power a CFO has over investors can be measured here. Something truly bizarre is going on in Europe, in particular France. I hate both France’s sovereign credit as well as most corporate credits across the country. Credit spreads are just way too tight for a country heading into a clear and obvious recession. There was a new all time record Hybrid bond sale yesterday. A colossal $5.3 billion Hybrid bond was sold to investors by French utility EDF. The previous record was $GE ‘s $3.2 bln in 2007, yes GE in 07! You can’t make this stuff up. I can’t emphasize enough what a classic risk warning sign this is. The pace of Hybid bond sales over the last 52 weeks in DOUBLE the previous 52 week mark. Our systemic risk score here is a 7 and rising.

• Spain and Italy Credit Spread Over Germany & Curve Steepness• Over the last 4 years the steepness of the Spanish sovereign bond

curve has been a fantastic risk indicator. As the curve has flattened in 2011 and 2012 it made sense to lighten exposure to equities. In a flat curve, short term bonds yield similar amounts to long term bonds. In April May 2012, the Spanish 2s – 10s spread got inside of 50bps, the 10 year bond yield was near 7.4% and the 2 year was 7%. In the months before the Lehman bankruptcy, as in all Chapter 11 filings, the curve was flattening month by month. Spain’s bond yield spread over German Bunds is something we watch every day. Mario Draghi’s OMT program has changed this game somewhat, but the 5-year bond is something to watch closely as it is outside of the ECB’s bond buying mandate. Our systemic risk score here is a 4 and rising.

• Correlation• The 50-Day correlation of S&P 500 stocks to gains or losses in the full

index increased to a near record 0.86 in October 2012. It was 0.93 in the days around Lehman’s failure, the granddaddy of all correlation moments in the markets. This is a measure of how stocks in the S&P are acting like each other. During normal, healthy markets correlation is low. IBM shouldn’t move with MGM Casino’s in a normal market, but should move independently depending on true company specific news flow. A level of 1 would mean all 500 stocks moved together. August-September 2008, May-Sept 2011, April-May 2012, rising correlation existed across all asset classes. A breakout in the ICJ, the CBOE S&P 500 Implied Correlation Index, which occurred on September 2nd 2008, July 11th 2011, and April 17th 2012, is very bearish for equities and commodity prices. Our systemic risk score here is a 4 and rising

• Two Month VIX Future vs. 8 Month VIX Future• Money flow from the 8 Month VIX Future into the 2 Month VIX

Future has been a solid short term “risk off” indicator. As money moves from the 2 Month VIX Future to the 8 Month, this has always been solid short term “risk on” indicator. Why? Just about every hedge fund on the street has a risk manager in a trading capacity. At Lehman, we were long billions of dollars of high yield bonds and every day we could hedge our exposure. If the market crashed, we’d make money short equities, and lose money on our bonds. One of the watering holes where risk mangers reside is trading the VIX – volatility. By tracking the 2s vs 8 month money flow you can see elephant footprints, see where the smart players are making their bets. Our systemic risk score here is an 8 and rising. The smartest money is stepping into short-term protection.

• Investor Sentiment• If you look at Hulbert newsletter writers, this tracks bullishness

and bearishness among market pundits. Right now they recommend an 81% net long exposure, the highest since July 2000, the next highest December 2004, and May 2010. No matter how many people on CNBC tell you “everyone is bearish,” track the numbers. Another good one here, only 24.5% of advisors in Investors Intelligence survey looking for a “correction.” That is the fewest since 14-Sep when their number fell to 21.3%. That was the last market high when optimism was abundant. The September 14th reading was a classic short term top in the market, preceded a cool 7% drop. Our systemic risk score here is a 9 and rising.

• Percentage of Stocks Above their 50 Day Moving Average*• This week a remarkable 79.9% of the stocks in the S&P 500

hit the overbought level across our model. The chart below is a classic breadth measure. The market is extended here to say the least. As shown, 90% of the stocks in the S&P 500 are currently trading above their 50-day moving averages. This is the highest reading we’ve seen over the last year, and it’s only the sixth time we’ve crossed the 90% barrier since the bull market began in 2009. Bespoke research confirms these findings. Our systemic risk score here is a 9 and rising.

• CBOE Put Call Ratio• When nobody’s buying puts, lookout on the long

side, and when everyone is buying puts GET LONG STOCKS. The ratio hit a recent low this week at 0.73. When stocks made that recent bottom on November 16th, the ratio hit 0.86, a lot of investors were looking for downside protection. THE CROWD IS ALWAYS WRONG. The only way to beat them is to go the other way. Our systemic risk score here is 8 and rising.

• NYSE New Highs vs. New Lows• This indicator is much better at predicting market

bottoms vs. market tops. Peaks in NYSE New 52 week lows are a classic buy signal. The near 2000 all time record after Lehman, hopefully will never be broken. More telling is the MACD on New Highs / New Lows, when the 50, 100, and 200 day turn negative, it’s a solid sell signal. Our systemic risk score here is a 8 and rising.

• High Yield vs. Equities• By definition, the high yield market is not as liquid

as the stock market. In the months before the 2011 sell off, as well as during the summer of 2008, high yield bonds were dramatically underperforming equities. Today both high yield and stocks are on fire to the upside. Typically, before substantial pull backs in equities, high yield will start to underperform. Our systemic risk score here is a 6 and rising

• High Yield Fund Inflows• If you look back over the last 10 years, most market tops take

place around the same time of record inflows into high yield bonds, coming from the retail investor base. J.P. Morgan’s weekly analysis of European high-yield funds shows a €413 million inflow for the week ending Jan. 16, which is the largest weekly inflow on J.P. Morgan’s records (which date back to 2005). Of this, €56 million is attributable to ETFs. The reading for the week ending Jan. 9 is a €180 million inflow. The provisional reading for December is an inflow of €1.67 billion, and the 2012 total currently stands at a €7.97 billion inflow. Our systemic risk score here is an 8 and rising.

• Corporate Default Rate• Moody’s global speculative-grade default rate ended 2007 at 0.91% – approximately 48%

lower than 2006’s year-end level of 1.74%. This was a classic warning sign for stocks. The speculative-grade default rate finished the fateful year at its lowest level since 1981 when it came in at 0.70%. The default rate for all Moody’s-rated corporate issuers fell to 0.31% in 2007 from 0.61% in 2006, also over a two-decade low going back to 1981.

• US corporate family defaults remained steady in the final quarter of 2012, Moody’s Investors Service says in the latest edition of its quarterly “US Corporate Default Monitor.” Just seven non-financial corporate families defaulted during the quarter, for a total of nearly $9 billion, compared with 16 defaults representing approximately $10 billion in the same period a year earlier. “Continuing low interest rates and accessible credit markets have kept the default count light among speculative-grade corporates,” says Moody’, “and we expect it to edge even lower this year in 2013.” Moody’s forecasts the US speculative grade default rate to end 2013 at 3.0%, below the average of 4.5% since 1993 and well below the cyclical peak above 14% in late 2009.BOTTOM LINE: Raise cash. Your exposure to the markets now should only be your core positions. If you are feeling brave, go short. Our goal is to point your ship into fairer winds. Good luck, and stay poised. We think a storm is brewing.

Tail risk & Big Data

• Unfortunately, Black Swans, by their very definition, cannot be predicted by analyzing the data. Yes, Black Swan events always look eminently predictable in hindsight, yet no one ever predicts them. Equally unfortunate, the more data we throw at the problem, the more impossible it becomes to predict such events. Indeed, the bigger the data set, the harder it becomes to sift through the noise to find the signal, because we are more prone to fixate on incorrect correlations between disparate data sets.

• In business and economic decision-making, data causes severe side effects - data is now plentiful thanks to connectivity; and the share of spuriousness in the data increases as one gets more immersed into it. A not well-discussed property of data: it is toxic in large quantities - even in moderate quantities. The more frequently you look at data, the more noise you are disproportionally likely to get (rather than the valuable part called the signal); hence the higher the noise to signal ratio.

• Key is that firms should look beyond bigger data to better models, holding that historically, it's been about relative balance and flow between unmodelled to modelled. The value is not the data but the models. Yet, before the Wall Street meltdown even low-level IT peons knew the models were a joke but our tribal mindset blinded us to the consequences. Which may well be the problem: we are human - all too human.

• Whether in our models, our collection of certain kinds of data, or our interpretation of that data, we bring personal biases to the analysis. We cannot avoid this bias, and the attempt to look for correlation rather than causation in our data solves nothing. In fact, it arguably makes the problem worse, because it gives us too much confidence in Big Data.

• The trick - again - is to approach Big Data Analytics with caution. It is not that data cannot help us anticipate the future. It can. Just ask the City of Chicago, which has a very successful predictive analytics platform used to anticipate crime and health trends, among other things. But there is a reason most enterprises still use Big Data technologies like Hadoop to solve old problems like ETL, rather than analytics.

• We are still early in Big Data, and enterprises rightly suspect that Big Data is not some magic pixie dust that immediately yields insights into how much to charge, where to market, etc. Big Data can help, but it's not The Answer. And it's certainly not the answer to predicting Black Swan events; to do that, you don not need data - you need hindsight.

• As I have already explained in several previous articles, such events as Black Swans are impossible to predict per se, so the only approach is to build robust models to mitigate the effects of the negative ones and to use the positive ones to their fullest potential.

• Yes, Big Data Analytics is an interesting and useful tool for a researcher dealing with a large amount of data, provided that the said researcher also has a keen ornithological eye that knows how to properly see findings coming from Tea Leaf readings.

Sources of information for the risk monitoring process

• The purpose of risk management is to identify potential problems before they occur so that risk-handling activities may be planned and invoked as needed across the life of the product or project to mitigate adverse impacts on achieving objectives.

• Risk management is a continuous, forward-looking process that is an important part of business and technical management processes. Risk management should address issues that could endanger achievement of critical objectives. A continuous risk management approach is applied to effectively anticipate and mitigate the risks that have critical impact on the project.

• Effective risk management includes early and aggressive risk identification through the collaboration and involvement of relevant stakeholders. Strong leadership across all relevant stakeholders is needed to establish an environment for the free and open disclosure and discussion of risk.

• Demonstrate that you have a process to determine risk sources and categories. Identification of risk sources provides a basis for systematically examining changing situations over time to uncover circumstances that impact the ability of the project to meet its objectives. Risk sources are both internal and external to the project. As the project progresses, additional sources of risk may be identified. Establishing categories for risks provides a mechanism for collecting and organizing risks as well as ensuring appropriate scrutiny and management attention for those risks that can have more serious consequences on meeting project objectives.

• • Typical work products would include: (1) risk source lists (external and

internal) and (2) risk categories lists.

• Demonstrate that you have a process to define the parameters used to analyze and categorize risks, and the parameters used to control the risk management effort. Parameters for evaluating, categorizing, and prioritizing risks typically include risk likelihood (i.e., the probability of risk occurrence), risk consequence (i.e., the impact and severity of risk occurrence), and thresholds to trigger management activities.

• • Risk parameters are used to provide common and consistent criteria for

comparing the various risks to be managed. Without these parameters, it would be very difficult to gauge the severity of the unwanted change caused by the risk and to prioritize the necessary actions required for risk mitigation planning.

• • Typical work products would include: (1) risk evaluation, categorization, and

prioritization criteria and (2) risk management requirements (control and approval levels, reassessment intervals, etc.).

• In most organisations, the existing approach has involved dividing risk into three main categories: financial, operational and strategic. Companies have generally done a good job of focusing on the first two of these risks – financial and operational. But they have often been less successful at linking these risk categories together, or understanding the interdependencies between them. And many businesses have focused much less attention on strategic risk, largely because they regarded risk and strategy as separate from each other, rather than seeing risk-taking as a key part of value creation in any business.

• And, that's were early warning, red flags or grey swans come into play.

• A common mistake in risk management is to refuse to face the facts and be unwilling to prepare ourselves for upcoming events. Senior executives are falling too easily into the sunk cost fallacy trap alleging that since the stakes are so high (ie. because firms are emotionally invested in whatever money, time, or any other resource they have committed in the past), retreating and preparing for the worst would be unacceptable. Indeed, many senior risk management executives opt to put their heads into the sand instead of trying to maximize recovery values in case of loss scenarios.

• One of the most compelling examples illustrating the failure to react on red flags is the recent GFC. Indeed, notwithstanding a hard to quell enduring myth alleging that everybody missed the early warning signals, instead there were a lot of signs of an upcoming crisis - but no one paid real attention to it.

• Therefore, when going through the financial statements and other related firm's reports, risk management should ensure that the firm in question provides adequate and timely financial and performance information that can answer questions, such as for instance:

• Is the bank’s strategic plan realistic for the bank’s circumstances?• Is management meeting the goals established in the planning process? If no, why?• Is the level of earnings consistent or erratic?• Do earnings result from the implementation of planned bank strategies, or from transactions that, while

increasing short- term earnings, raise longer term risk?• Do audit programs test internal controls to identify inaccurate, incomplete, or unauthorized transactions;

deficiencies in the safeguarding of assets; unreliable financial and regulatory reporting; violations of law and regulations; and deviations from the institution’s policies and procedures?

• Are policies and procedures in place that safeguard against conflicts of interest, insider fraud and abuses, and affiliate abuse?

• Is the bank giving due consideration to changes in external conditions?• Is the bank being compensated adequately for the risks it is taking in its various product lines and activities?• Does the bank have sufficient capital to support its risk profile and business strategies?• Are financial reports and statements accurate, or do they reflect an incomplete evaluation of the bank’s

financial condition?• Are the bank’s goals and plans consistent with the directors’ tolerance for risk?

Mutualizing resources with Finance

• Risk management needs to work more closely with Finance.

• Key to have the same understanding about risk metrics, such as risk exposures, provisions, and other reporting data

• Build common database to facilitate and mutualize exchange of information