# Research¶

## Working Papers¶

**“Are U.S. Treasury Auctions Twice Underpriced?”**

*Abstract*: I empirically show that underpricing in U.S. Treasury auctions is
explained by a risk premium for exogenously determined holding period risk.
Investors who place winning auction bids must wait day(s) to settle the
transaction, in which the settlement period is exogenously determined by the
Treasury. Post-auction prices rise because auction prices are discounted for
holding period risk. I model the returns from underpricing with a GARCH-M
process and show that the bulk of underpricing is compensation for risk. I
also show that auction demand is determined by the expected risk-adjusted
returns that result from underpricing. These results suggest that U.S.
Treasury pre- and post-auction markets may be efficient. This finding is a
dramatic departure from convention because underpricing is viewed almost
exclusively as an aberration from market efficiency.

**“Underpricing and Risk in Municipal Bond Offerings”**

`Download`

(w/Daniel Bergstresser)Using a cross-section of 1.762 million bond offering, we estimate the effect that
risk has on underpricing in municipal bond offerings. Using U.S. Treasury auction
volatility as a common risk factor, we find that a risk premium exists, suggesting
that dealers are risk-averse. We estimate that dealers require a risk premium of
roughly 0.453 (*t=5.63*) basis points for every 1 basis point of expected volatility
when reselling securities intra-day to retail investors, and 0.418 (*t=10.7*) basis
points for next-day trades. We also find that the risk premium for trades between
dealers is less than trades from dealers to retail investors. Our findings are the
first to explain underpricing in municipal bond offerings as compensation for risk.

**“Empirical Evidence for Carry Trade Liquidity Spirals”**

*Abstract*: This paper provides an empirical test in support of carry trade liquidity
spirals that result from the mutually reinforcing characteristics of market liquidity
and funding liquidity. Volatility
increases with market illiquidity, and when markets are illiquid, reductions in
funding liquidity can result in liquidity spirals, which increase the negative
skewness of speculative asset returns. Conversely, reductions in funding liquidity
can reduce market liquidity and induce liquidity spirals, which increase volatility
and negative skewness. It is well known that many financial asset return processes
exhibit volatility clustering, which can imply an important dependence in the return
time path. Since market liquidity and funding liquidity can be mutually reinforcing,
liquidity spirals may be related to the dependence in the asset return time path. If
liquidity spirals are related to the time dependence, then skewness should also be
related. This paper tests this conjecture with a bootstrap or resampling method and
rejects in favor of liquidity spirals and time dependence in carry trade returns.

**“A Demonstration of Time Varying Volatility and Invalid t-Statistcs”**

*Abstract*: This paper demonstrates that using ordinary least squares (OLS) to
estimate a model that exhibits time varying volatility can result in invalid
t-statistics and a rejection of the true data generating process. Artificial data
is simulated for ten separate models that all have the same linear relationship
but different volatility structures. Parameters are estimated for each model using
both OLS with Huber-White heteroskedasticity consistent standard errors (robust
standard errors) and an alternative model that compensates for the time varying
volatility. In each case, OLS with robust standard errors rejects the true data
generating process by more than the allowed p-value. This result indicates that
the efficiency gains from modeling volatility may reverse contemporary findings
in economics and finance.

## Published Book Chapters¶

Herb, Patrick, Shishir Paudel, and Mark Wu. (*forthcoming*). **Bond Auctions**.
In: Baker, H. Kent, Greg Filbeck, and Andrew C. Spieler, (Eds.),
Debt Markets and Investments. Oxford University Press, New York, NY.

## Work in Progress¶

**“Why Pension Funds Underperform in U.S. Treasury Auctions”**

**“Overpricing in Portuguese Treasury Auctions”**

**“Discriminatory vs. Uniform-Price Portuguese Treasury Auctions”**

**“Underpricing and Bidder Allocations in U.S. Treasury Auctions”**

## Agent-Based Computational Finance¶

In cooperation with Blake LeBaron and Axel Szmulewiez, we set forth to develop a suite of agent-based software to assist in both research and teaching. We have written code to replicate some of the classic papers in agent-based computational finance. The code is available upon request. Below is a list of the papers and corresponding code. Programs are written in Python, MATLAB and NetLogo.

Gaunersdorfer, A., & Hommes, C. (2007). *A nonlinear structural model for
volatility clustering* (pp. 265-288). Springer Berlin Heidelberg. Chicago

Nonlinear Structural Volatility Model

Franke, R., & Westerhoff, F. (2012). Structural stochastic volatility in
asset pricing dynamics: Estimation and model contest. *Journal of Economic
Dynamics and Control*, 36(8), 1193-1211.

Discrete Choice Approach: Wealth

Discrete Choice Approach: Wealth & Predisposition

Discrete Choice Approach: Wealth, Herding & Predisposition

Discrete Choice Approach: Herding, Predisposition & Misalignment

Transition Probability Approach: Wealth

Transition Probability Approach: Herding & Predisposition

Transition Probability Approach: Herding, Predisposition & Misalignment

Farmer, J. D., & Joshi, S. (2002). The price dynamics of common trading
strategies. *Journal of Economic Behavior & Organization*, 49(2), 149-171.

Homogeneous Trend Followers

Heterogeneous Value Investors

Order Based Value Investors

Position Based Value Investors

State-Dependent Threshold Value Strategies

Value Investors and Trend Followers

Dividend Data

Chiarella, C., Iori, G., & Perello’, J. (2009). The impact of heterogeneous
trading rules on the limit order book and order flows. *Journal of Economic
Dynamics and Control*, 33(3), 525-537.

Limit Order Book

Gai, P., & S. Kapadia. (2010). *Contagion in Financial Networks* (pp. 2401-423)
*Proceedings of the Royal Society A: Mathematical, Physical and Engineering
Sciences* 466.2120

One Contagion Round, Equal Size Banks

One Contagion Round, Different Size Banks

Many Contagion Rounds, Equal Size Banks, Default Extension Histogram

Many Contagion Rounds, Descriptive Statistics, Frequency and Extent of Contagion Plots

Many Contagion Rounds, average and top 20% average contagion plots

Many Contagion Rounds, Different Size Banks, Default Extension Histogram and Descriptive Statistics

Duffy, J., & Utku Unver, M. (2006). *Asset Price Bubbles and Crashes with
Near-Zero-Intelligence Traders*. Economics Theory, 27(3), 537-563.

Asset Price Bubble Model with Optional Dividends, Constant or Decreasing Fundamental Price