Connecting Silos : On linking macroeconomics and finance, and the role of econometrics therein

The crises of this century have stressed how intertwined macroeconomics and finance are in practice. This intertwinement was absent in most economic models. This led to calls for economists to step out of their specialized silos. Since then, the literature of macro-finance, which studies the relationship between asset prices and economic fluctuations, has been developed. In this inaugural address, I argue for a prominent role of econometrics to study the macro-finance interaction. Key elements such as mixed frequencies and the selection of factors can be incorporated using recent econometric advances. I discuss some of the results, such as estimation of continuous-time equilibrium models for macroeconomic and financial series, as well as characteristics of trading on financial markets after macroeconomic news releases. Finally, I discuss the outstanding challenges, which include developing a yield curve model based on macroeconomic foundations, modeling how financial markets anticipate news releases, and developing a macro-finance model for European bond markets taking into account the large heterogeneity across the continent

Why do Pit-Hours outlive the Pit?

__Abstract__ We study why a majority of trades still happen during the pit hours, i.e. when the trading pit is open, even after the pit ceased to be a liquid and informative venue. We investigate the case of 30-year U.S. Treasury futures using a ten-years-long intraday data set which contains the introduction of the CME Globex platform as an example of sophistication in electronic trading. We use a structural model to estimate the time-variation in potential factors of the clustering of trading activity around the pit hours, namely price informativeness, information asymmetry and price impact of trades. We find evidence for a feedback mechanism between trading activity and these factors. Across the sample period, price informativeness during the afterhours is a consistently significant factor attracting trade activity. Information asymmetry has a negative effect on afterhours act ivity, particularly during the crisis years. The negative effect of price impact on afterhours activity ceases to be a significant factor from 2007 on, possibly due to improvements in order execution algorithms and electronic trading facilities

Intraday Price Discovery in Fragmented Markets

For many assets, trading is fragmented across multiple exchanges. Price discovery measures summarize the informativeness of trading on each venue for discovering the asset’s true underlying value. We explore intraday variation in price discovery using a structural model with time-varying parameters that can be estimated with state space techniques. An application to the Expedia stock demonstrates intraday variation, to the extent that the overall dominant trading venue (NASDAQ) does not lead the entire day. Spreads, the number of trades and volatility can explain almost half of the intraday variation in information shares

Improving Density Forecasts and Value-at- Risk Estimates by Combining Densities

__Abstract__ We investigate the added value of combining density forecasts for asset return prediction in a specific region of support. We develop a new technique that takes into account model uncertainty by assigning weights to individual predictive densities using a scoring rule based on the censored likelihood. We apply this approach in the context of recently developed univariate volatility models (including HEAVY and Realized GARCH models), using daily returns from the S&P 500, DJIA, FTSE and Nikkei stock market indexes from 2000 until 2013. The results show that combined density forecasts based on the censored likelihood scoring rule significantly outperform pooling based on the log scoring rule and individual density forecasts. The same result, albeit less strong, holds when compared to combined density forecasts based on equal weights. In addition, VaR estimates improve a t the short horizon, in particular when compared to estimates based on equal weights or to the VaR estimates of the individual models

Predicting Covariance Matrices with Financial Conditions Indexes

We model the impact of financial conditions on asset market volatility and correlation. We propose extensions of (factor-)GARCH models for volatility and DCC models for correlation that allow for including indexes that measure financial conditions. In our empirical application we consider daily stock returns of US deposit banks during the period 1994-2011, and proxy financial conditions by the Bloomberg Financial Conditions Index (FCI) which comprises the money, bond, and equity markets. We find that worse financial conditions are associated with both higher volatility and higher average correlations between stock returns. Especially during crises the additional impact of the FCI indicator is considerable, with an increase in correlations by 0.15. Moreover, including the FCI in volatility and correlation modeling improves Value-at-Risk forecasts, particularly at short horizons

Economic Valuation of Liquidity Timing

__Abstract__ This paper conducts a horse-race of different liquidity proxies using dynamic asset allocation strategies to evaluate the short-horizon predictive ability of liquidity on monthly stock returns. We assess the economic value of the out-of-sample power of empirical models based on different liquidity measures and find three key results: liquidity timing leads to tangible economic gains; a risk-averse investor will pay a high performance fee to switch from a dynamic portfolio strategy based on various liquidity measures to one that conditions on the Zeros measure (Lesmond, Ogden, and Trzcinka, 1999); the Zeros measure outperforms other liquidity measures because of its robustness in extreme market conditions. These findings are stable over time and robust to controlling for existing market return predictors or considering risk-adjusted returns

Intraday price discovery in fragmented markets

We explore intraday variation in the contribution to price discovery across different exchanges. We estimate a structural model with time-varying parameters in state space form using maximum likelihood. We analyze data for 50 S&P 500 stocks in 2013 and find that the constancy of shares in price discovery is rejected. Tighter quoted spreads attract informed trading from other exchanges. Exchange listing and industrial sector of a stock significantly affect the dominant venues of price discovery in different parts of the day and following macroeconomic news announcements

Taking two to tango:fMRI analysis of improvised joint action with physical contact

<div><p>Many forms of joint action involve physical coupling between the participants, such as when moving a sofa together or dancing a tango. We report the results of a novel two-person functional MRI study in which trained couple dancers engaged in bimanual contact with an experimenter standing next to the bore of the magnet, and in which the two alternated between being the leader and the follower of joint improvised movements. Leading showed a general pattern of self-orientation, being associated with brain areas involved in motor planning, navigation, sequencing, action monitoring, and error correction. In contrast, following showed a far more sensory, externally-oriented pattern, revealing areas involved in somatosensation, proprioception, motion tracking, social cognition, and outcome monitoring. We also had participants perform a “mutual” condition in which the movement patterns were pre-learned and the roles were symmetric, thereby minimizing any tendency toward either leading or following. The mutual condition showed greater activity in brain areas involved in mentalizing and social reward than did leading or following. Finally, the analysis of improvisation revealed the dual importance of motor-planning and working-memory areas. We discuss these results in terms of theories of both joint action and improvisation.</p></div

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants