Marcela Valenzuela

Implied Correlation and Expected Market Returns

This paper provides evidence that the implied correlation is a significant indicator of market-wide risk. From a time-series approach, I analyze whether aggregate implied correlation contains information on future market returns. I document that it explains an important fraction of the variation in aggregate market excess returns, with high implied correlation followed by an increase in subsequent market returns. The predictive power is stronger at a forecast of bimonthly, quarterly and semi-annually return horizons and robust to the inclusion of standard predictors. Moreover, I show that the forecasting power of the correlation risk premium is mainly driven by the implied correlation. From a cross-sectional perspective, I examine whether aggregate implied correlation affects the risk-return trade-off of stock returns. I show that assets with positive sensitivities to the level of implied correlation have lower average returns even after controling for standard risk factors. Consistent with other studies documenting a negative price of correlation risk, my results suggest that investors require a higher risk premium to hold assets that perform bad in states of high market-wide correlation.

Global Depth and Future Volatility (with I. Zer)

We provide new empirical evidence that the distribution of liquidity has a strong in-sample and out-of-sample predictive power on intraday market volatility. To this end, we introduce a novel way of summarizing relative depth provision in the whole limit order book. Our measure, global depth, considers the entire quoted depth and assigns weights decreasing with distance from the best quotes. Hence, it takes into account that a trader gives higher weights to the information around the best quotes compared to the rest of the book due to decreasing execution probabilities. We document that global depth outperforms alternative predictors of volatility, such as the bid-ask spread, standard depth variables, and measures of trading activity, in explaining the variations in market volatility.

Competition, signaling and non-walking through the book: Effects on order choice (with I. Zer), Forth. in Journal of Banking and Finance

We investigate the effects of competition and signaling in a pure order driven market and examine the trading patterns of agents when walking through the book is not allowed. We show that the price information does not matter for an impatient trader in her decision of fitting the order size under this market mechanism. Also, our results suggest that the competition effect is persistent beyond the best quotes and dominates the signaling effect at every level, being strongest for the volume at the second best bid and ask for both sides of the market. Finally, we find that institutional traders' order submission strategies are less sensitive to the state of the limit order book compared to individual ones

Dealing with systemic risk when we measure it badly: A minority report (with J. Danielsson, K. James and I. Zer)

While an omniscient regulator would base a bank's capital requirement upon its contribution to systemic risk, we show that a regulator who measures a bank's contribution to systemic risk badly will find it optimal to use a simple leverage ratio instead. We empirically analyze the performance of leading risk measurement methods and find that they are incapable of providing either precise estimates of an individual bank's contribution to systemic risk or reliable rankings of banks by the amount of systemic risk they create. We conclude that a simple leverage ratio dominates a policy of systemic risk based capital requirements

Model risk of systemic risk models (with J. Danielsson, K. James and I. Zer)

Statistical systemic risk measures (SRMs) have been proposed by several authors. Those generally depend on established methods from market risk forecasting. The two most common SRMs, MES and CoVaR, along with VaR, are compared theoretically and then critically empirically analyzed. They are found to contain a high degree of model risk so that the signal they produce is highly unreliable. Finally, the paper discusses the main problems in systemic risk forecasting and proposes evaluation criteria for such models.