VIX term structure as a trading signal

June 23, 2018

The VIX futures curve reflects expectations of future implied volatility of S&P500 index options. The slope of the curve is indicative of expected volatility and uncertainty relative to volatility and uncertainty priced in the market at present. Loosely speaking, a steeply upward sloped VIX futures curve should be indicative of present market complacency, while an inverted downward sloped curve should be indicative of present market panic and capitulation. In both cases the slope of the curve would serve as a contrarian indicator for market directional positions. An empirical analysis for 2010-2017 suggests that an inverted VIX curves has had a significant positive relation with subsequent S&P500 returns. Normal VIX curves, however, did not have significant predictive power, possibly because a market can stay complacent longer than it can panic.

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The danger of volatility feedback loops

February 17, 2018

There is evidence that the financial system has adapted to low fixed income yields through an expansion of explicit and implicit short volatility strategies. These strategies earn steady premia but bear large volatility, “gamma” and correlation risks and include popular devices such as leveraged risk parity and share buybacks. The total size of explicit and implicit short-volatility strategies may have reached USD2000 billion and probably created two dangerous feedback loops. The first is a positive reinforcement between interest rates and volatility that will overshadow central banks’ attempts to normalize policy rates. The second is a positive reinforcement between measured volatility and the effective scale of short-volatility positions that has increased the risk of escalatory market volatility spirals.

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Policy rates and equity volatility

January 13, 2018

Measures of monetary policy rate uncertainty significantly improve forecasting models for equity volatility and variance risk premia. Theoretically, there is a strong link between the variance of equity returns in present value models and the variance short-term rates. For example, there is natural connection between recent years’ near-zero forward-guided policy rates and low equity volatility. Empirically, the inclusion of derivatives-based measures of short-term rate volatility in regression forecast models for high-frequency realized equity volatility has added significant positive predictive power at weekly, monthly and quarterly horizons.

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The downside variance risk premium

December 9, 2017

The variance risk premium of an asset is the difference between options-implied and actual expected return variation. It can be viewed as a price for hedging against variation in volatility. However, attitudes towards volatility are asymmetric: large upside moves are fine while large downside moves are scary. A measure of aversion to negative volatility is the downside variance risk premium, the difference between options-implied and actual expected downside variation of returns. It is this downside volatility risk that investors want to protect against and whose hedging price is a valid and apparently robust indicator of future returns. Similarly, the skewness risk premium, the difference between upside and downside variance risk premia, is also a powerful predictor of markets.

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The 1×1 of risk perception measures

December 1, 2017

There are two reasons why macro traders watch risk perceptions. First, sudden spikes often trigger subsequent flows and macroeconomic change. Second, implausibly high or low values indicate risk premium opportunities or setback risks. Key types of risk and uncertainty measures include [1] keyword-based newspaper article counts that measure policy and geopolitical uncertainty, [2] survey-based economic forecast discrepancies, [3] asset price-based measures of fear and uncertainty and [4] derivatives-implied cost of hedging against directional risk and price volatility.

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What traders can learn from market price volatility

July 1, 2017

Equity and bond market volatility can be decomposed into persistent and transitory components by means of statistical methods. The distinction is relevant for macro trading because plausibility and empirical research suggest that the persistent component is associated with macroeconomic fundamentals. This means that persistent volatility is an important signal itself and that its sustainability depends on macroeconomic trends and events. Meanwhile, the transitory component, if correctly identified, is more closely associated with market sentiment and can indicate mean-reverting price dynamics.

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The “low risk effect” in financial markets

May 27, 2017

Low-beta and low-volatility securities can produce superior risk-adjusted returns. Thus, portfolios of calibrated low- versus high-vol stock positions have historically generated significant alpha. Other asset classes display similar ‘low risk effects’. Their plausible cause is many investors’ limited access to leverage and willingness to pay a premium for securities with greater exposure to market performance. Some investors may also pay a premium for lottery-like payoffs with large upside potential. For leveraged portfolio managers this creates relative value opportunities in form of ‘betting against market correlation’ or ‘betting against volatility’.

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Volatility risk premia and FX returns

February 18, 2017

Volatility risk premia – differences between implied and realized volatility – are plausible and empirically validated predictors of directional foreign exchange returns, particularly for EM currencies. The intuition is that excess implied volatility typically results from elevated risk aversion, which should be indicative of undershooting. When calculating the volatility risk premium it is important to compare short-term implied volatility with realized volatility of that same period. One would expect positive returns on currencies whose very recent volatility has been less than feared.

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The power and origin of uncertainty shocks

January 14, 2017

Uncertainty shocks are changes in beliefs about probabilities. They are perhaps the most powerful driver of financial markets. Uncertainty comes in various forms, such as macro uncertainty, firm-specific uncertainty and uncertainty about others’ beliefs. However, empirical and theoretical research suggests that different types of relevant uncertainty shocks have one common dominant origin: updated beliefs about disaster risk. Hence, when markets give greater probability of downside tail risks, all sorts of uncertainty would rise, with a profound impact on macro trading strategies, whether they are directional or based on relative value.

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Corporate bond market momentum: a model

September 3, 2016

An increase in expected default ratios naturally reduces prices for corporate bonds. However, it also triggers feedback loops. First, it reduces funds’ wealth and demand for corporate credit in terms of notional, resulting in selling for rebalancing purposes. Second, negative performance of funds typically triggers investor outflows, resulting in selling for redemption purposes. Flow-sensitive market-making and momentum trading can aggravate these price dynamics. A larger market share of passive funds can increase tail risks.

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