Journal Of Indexes: Tails, You Lose

May 14, 2014

 

Algorithmic (Signals-Based) Tail Hedging

The primary challenge during the current low-volatility environment, however, is that the cost of static, “always on,” tail insurance is often expensive to hold. Accordingly, if a tail event fails to materialize, the buyer of a systematic tail strategy risks significantly underperforming his unhedged peers. To moderate the cost of carry, hedgers often shift toward dynamic tail-risk strategies during times of market stability.

Over the last few years, a vast number of dynamic strategies in the form of algorithmic indexes1 have been designed to profit from the realization of tail events and offered as a hedging product to end investors. Algorithmic indexes (aka “algos”) are liquid, transparent and easily investable through delta-one wrappers such as swaps, notes or more advanced products involving the use of derivatives and/or leverage in order to produce a highly asymmetrical payoff.

Algorithmic Tail-Risk Construction

As of the time of writing, the marketplace currently has more than 200 active tail-risk algorithmic products spanning five asset classes. However, due to the leverage to downside shocks and the greater liquidity offered by equity volatility products in times of market distress, the majority of algo products invest in equity volatility. Figure 3 provides a cross section of Credit Suisse’s more popular tail-hedging algos (by notional invested), their asset class exposure and a short description of the trading rules.

Algorithmic tail-risk construction generally follows a five-step process:

  1. Tail definition
  2. Benchmark selection
  3. Trigger design
  4. Simulation
  5. Test of efficacy
Figure 1
For a larger view, please click on the image above.

 

In the following pages, we will use the development of our Equity Dynamic Tail Hedge Index (Ticker: DYTL) as a case study to illustrate the process of constructing a tail-risk algorithm.

Step 1: Tail Definition
The obvious first step to developing a tail-risk algo is to first define what is meant by “tail.” Given the breadth of investment styles, the definition of the term “tail-risk” itself (and therefore the solution) may vary greatly among investment professionals. Take for example, the flash crash, in which the market plummeted 10 percent during the course of one hour and then recovered 8 percent during the next hour to finish down 2 percent for the day. For an investor such as a high-frequency trader or an active delta-hedger who was actively trading during that period and therefore realized profit and loss (“P&L”) during those volatile two hours of the day, such an event may in fact qualify as a tail event. However, if one were a “low-frequency,” long-term investor such as a pension fund that did not trade during that day, then a tail event may refer to a protracted deterioration in one’s portfolio caused by a breakdown of the core investment strategy. For the purposes of this case study, we will define a tail risk as a sizable abrupt market decline that triggers a persistent volatility regime shift from a low- to high-volatility environment.

 

 

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