Financial Econometrics and Empirical Market Microstructure

Origins of Market Shocks

It is still not clear what the main source of price jumps is. Price jumps, understood as an abrupt price change over a very short time, are also related to a broad range of market phenomena that cannot be connected to the noisy Gaussian distribution. Researchers agree on the presence of price jumps, but they disagree about the origins. All the explanations are very different in nature. One branch of the literature considers new information as a primary source of price jumps (Lee and Mykland 2008; Lahaye et al. 2009; Cutler et al. 1989). They also show a connection between macroeconomic announcements and price jumps on developed markets. A possible explanation of the source of these jumps says that they originate in the herd behavior of market participants (Cont and Bouchaud 2000; Hirshleifer and Teoh 2003). An illustration of such behavior is a situation when a news announcement is released, and every market participant has to accommodate the impact of that announcement. However, this herding behavior can provide an arbitrage opportunity and can be thus easily questioned. Bajgrowicz and Scaillet (2011) found that majority of news do not cause jumps. One exception is share buybacks announcements, Fed rate news have an important impact but rarely cause jumps. Another finding is that 60 % of jumps occur without any news event. Also authors admit that liquidity pressures are probably another important factor of jumps—for one third of the jumps with no news they found there is unusual behavior in the volume of transactions.

Joulin et al. (2010) and Bouchaud et al. (2004) conclude that price jumps are usually caused by a local lack of liquidity on the market and news announcements have a negligible effect on the origin of price jumps. A hidden liquidity problem is when either the supply or the demand side faces a lack of credit and thus is not able to prevent massive price changes. Madhavan (2000) also claims that the inefficient provision of liquidity caused by an imbalanced market microstructure can cause extreme price movements. Easley et al. (2010) introduced a new metric Volume-

Synchronized Probability ofInformed trading (the VPIN) as a real-time indicator of order flow toxicity. Order flow is toxic when it adversely selects market makers, who may be unaware they are providing liquidity at a loss. They find the measure useful in monitoring order flow imbalances and conclude it may help signal impending market turmoil, exemplified by historical high readings of the metric prior to the Flash crash. More generally, they show that VPIN is significantly correlated with future short-term return volatility. In contrast, empirical investigation of VPIN performed by Andersen and Bondarenko (2011) documents that it is a poor predictor of short run volatility, that it did not reach an all-time high prior, but rather after, the Flash crash, and that its predictive content is due primarily to a mechanical relation with the underlying trading intensity.

Filimonov and Sornette (2012) suggests that price dynamics are mostly endoge­nous and driven by positive feedback mechanisms involving investors’ anticipations that lead to self-fulfilling prophecies, as described qualitatively by Soros’ concept of “market reflexivity”. Filimonov and Sornette introduce a new measure of activity of financial markets that provides a direct access to their level of endogeneity. This measure quantifies how much of price changes are due to endogenous feedback pro­cesses, as opposed to exogenous news. They calibrate the self-excited conditional Poisson Hawkes model, which combines exogenous influences with self-excited dynamics, to the E-mini S&P 500 futures contracts traded in the Chicago Mercantile Exchange from 1998 to 2010. They find that the level of endogeneity has increased significantly from 1998 to 2010, with only 70 % in 1998 to less than 30 % since 2007 of the price changes resulting from some revealed exogenous information. Filimonov and Sornette claim that this measure provides a direct quantification of the distance of the financial market to a critical state defined precisely as the limit of diverging trading activity in absence of any external driving. But Hardiman et al. (2013) challenge this study and say that markets are and have always been close to criticality and it is not the result of increased automation of trading. They also note that the scale over which market events are correlated has decreased steadily over time with the emergence of higher frequency trading.

The behavioral finance literature provides other explanations for price jumps. Shiller (2005) claims that price jumps are caused by market participants who themselves create an environment that tends to cause extreme reactions and thus price jumps. Finally, price jumps can be viewed as a manifestation of Black Swans, as discussed by Taleb (2007), where the jumps are rather caused by complex systemic interactions that cannot be easily tracked down. In this view, the best way to understand jumps is to be well aware of them and be ready to react to them properly, instead of trying to forecast them.

Price jumps can also reflect moments when some signal hits the market or a part of the market. Therefore, they can serve as a proxy for these moments and be utilized as tools to study market efficiency (Fama 1970) or phenomena like information - driven trading, see e. g., Cornell and Sirri (1992) or Kennedy et al. (2006). An accurate knowledge of price jumps is necessary for financial regulators to implement the most optimal policies, see Becketti and Roberts (1990) or Tinic (1995).

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