- Asset pricing, and its connections with corporate finance; financial constraints; credit risk; liquidity risk.
- Robustness; financial econometrics; financial machine learning.

**Hui Chen**

MIT Sloan School of Management77 Massachusetts Avenue, E62-616

Cambridge, MA 02139

Tel: (617) 324 3896

Fax: (617) 258 6855

huichen@mit.edu

**Research Interest**

**Working Papers**

We propose a novel structural estimation framework in which we train a surrogate of an economic model with deep neural networks. Our methodology alleviates the curse of dimensionality and speeds up the evaluation and parameter estimation by orders of magnitudes, which significantly enhances one's ability to conduct analyses that require frequent parameter re-estimation. As an empirical application, we compare two popular option pricing models (the Heston and the Bates model with double-exponential jumps) against a non-parametric random forest model. We document that: a) the Bates model produces better out-of-sample pricing on average, but both structural models underperform random forest for large areas of the volatility surface; b) random forest is more competitive at short horizons (e.g., 1-day), for short-dated options (with less than 7 days to maturity), and on days with poor liquidity; c) both structural models outperform random forest in out-of-sample delta hedging; d) the Heston model's relative performance has deteriorated significantly after the 2008 financial crisis.

Firms tend to compete more aggressively in financial distress; the intensified compe- tition in turn reduces profit margins for everyone, pushing some further into distress. To study such feedback and contagion effects, we incorporate dynamic strategic competition into an industry equilibrium with long-term defaultable debt, which generates various peer interactions: predation, self-defense, and collaboration. Such interactions make cash flows, stock returns, and credit spreads interdependent across firms. Moreover, indus- tries with higher idiosyncratic-jump risks are more distressed, yet also endogenously less exposed to aggregate shocks. Finally, we exploit exogenous variations in market structure – large tariff cuts – to test the core competition mechanism.

We provide causal evidence for the value of asset pledgeability. Our empirical strategy is based on a unique feature of the Chinese corporate bond markets, where bonds with identical fundamentals are simultaneously traded on two segmented markets that feature different rules for repo transactions. We utilize a policy shock on December 8, 2014, which rendered a class of AA+ and AA bonds ineligible for repo on one of the two markets. By comparing how bond prices changed across markets and rating classes around this event, we estimate that an increase in the haircut from 0 to 100\% would result in an increase in bond yields in the range of 40 to 83 bps. These estimates help us infer the magnitude of the shadow cost of capital in China.

As part of overall market architecture, market-wide trading halts, also called circuit breakers, have been proposed and widely adopted as a measure to stabilize the stock market when experiencing large price movements. We develop an intertemporal equilibrium model to examine how circuit breakers impact the market when investors trade to share risk. We show that a downside circuit breaker tends to lower the stock price and increase its volatility, both conditional and realized. Due to this increase in volatility, the circuit breaker's own presence actually raises the likelihood of reaching the triggering price. In addition, the circuit breaker also increases the probability of hitting the triggering price as the stock price approaches it -- the so-called ``magnet effect.'' Surprisingly, the volatility amplification effect becomes stronger when the wealth share of the relatively pessimistic agent is small.

We formalize the concept of ``dark matter'' in asset pricing models by quantifying additional information the econometrician can obtain about the fundamental dynamics from asset pricing cross-equation restrictions. The dark matter measure captures the degree of fragility for models that are potentially misspecified and unstable: a large dark matter measure signifies a model's lack of internal refutability (weak power of specification tests) and external validity (high overfitting tendency and poor out-of-sample fit). The measure can be computed at low cost even for complex dynamic structural models. We illustrate its applications via (time-varying) rare-disaster risk and long-run risk models.

We analyze a model of optimal capital structure and liquidity choice based on a dynamic tradeoff theory for financially constrained firms. In addition to the classical tradeoff between the expected tax advantages of debt financing and bankruptcy costs, we introduce a cost of external financing for the firm, which generates a precautionary demand for cash and an optimal retained earnings policy for the firm. An important new cost of debt financing in this context is a \emph{debt servicing cost}: debt payments drain the firm's valuable precautionary cash holdings and thus impose higher expected external financing costs on the firm. Another change introduced by external financing costs is that realized earnings are separated in time from payouts to shareholders, implying that the classical Miller-formula for the net tax benefits of debt no longer holds. We offer a novel explanation for the "debt conservatism puzzle" by showing that financially constrained firms choose to limit their debt usages in order to preserve their cash holdings. In the presence of these servicing costs, a financially constrained firm may even choose not to exhaust its risk-free debt capacity. We also provide a valuation model for debt and equity in the presence of taxes and external financing costs and show that the classical adjusted present value methodology breaks down for financially constrained firms.

**Publications**

We document several facts about corporate debt maturity: (1) debt maturity is pro-cyclical; (2) higher-beta firms tend to have longer debt maturity; (3) shorter maturity amplifies the sensitivity of credit spreads to aggregate shocks. We build a dynamic capital structure model that explains these facts. In the model, leverage and maturity choices are highly interdependent, which reflects the tradeoffs of liquidity discounts of long-term debt, repayment risks of short-term debt, and the benefit of short-term debt as a commitment device for timely leverage adjustments. Additionally, the model quantifies the effects of maturity dynamics on the term structure of credit spreads.

Mortgage refinancing activity associated with extraction of home equity contains a strongly counter-cyclical component consistent with household demand for liquidity. We estimate a structural model of liquidity management featuring counter-cyclical idiosyncratic labor income uncertainty, both long-term and short-term mortgages, and realistic borrowing constraints. We then empirically evaluate its predictions for the households' choices of leverage, liquid assets, and mortgage refinancing using micro-level data. Taking the observed historical paths of house prices, aggregate income, and interest rates as given, the model quantitatively accounts for many salient features in the evolution of balance sheets and consumption in the cross section of households over the 2001-2012 period.

We propose a new measure of financial intermediary constraints based on how the intermediaries manage their tail risk exposures. Using a dataset for the trading activities in the market of deep out-of-the-money S&P 500 put options, we identify periods when the variations in the net amount of trading between financial intermediaries and public investors are likely to be mainly driven by shocks to intermediary constraints. We then infer tightness of intermediary constraints from the quantities of option trading during such periods. We show that a tightening of intermediary constraint according to our measure is associated with increasing option expensiveness, higher risk premia for a wide range of financial assets, deterioration in funding liquidity, and deleveraging of broker-dealers.

Full Text Internet Appendix Data: Option-based constraint measure

We develop a structural credit risk model to examine how the interactions between default and liquidity affect corporate bond pricing. The model features debt rollover and bond-price dependent holding costs for illiquid corporate bonds. Both over the business cycle and in the cross section (across ratings), our model does a good job matching the average default rates and credit spreads in the data, and it captures important variations in bid-ask spreads and bond-CDS spreads. A structural decomposition reveals that the default-liquidity interactions can account for 10% to 24% of the level of credit spreads and 16% to 46% of the changes in spreads over the business cycle. Through this decomposition, we estimate that liquidity-related corporate bond financing costs amount to about 6% of total issuance amount for the period of 1996 to 2015. We also apply our framework to evaluate the impact of liquidity-provision policies for the bond market.

Since corporate debt tends to be riskier in recessions, transfers from equity holders to debt holders that accompany corporate decisions also tend to concentrate in recessions. Such systematic risk exposures of debt overhang have important implications for corporate investment and financing decisions, and for the ex ante costs of debt overhang. Using a calibrated dynamic capital structure model, we show that the costs of debt overhang become higher in the presence of macroeconomic risk. We also provide several new predictions on how the cyclicality of a firm's assets in place and growth options affect its investment and capital structure decisions.

We study an investor's optimal consumption and portfolio choice problem when he is confronted with two possibly misspecified submodels of stock returns: one with IID returns and the other with predictability. We adopt a generalized recursive ambiguity model to accommodate the investor's aversion to model uncertainty. The investor deals with specification doubts by slanting his beliefs about submodels of returns pessimistically, causing his investment strategy to be more conservative than the Bayesian strategy. This effect is especially strong when the submodel with a low Bayesian probability delivers a much smaller continuation value. Unlike in the Bayesian framework, the hedging demand against model uncertainty may cause the investor's stock allocation to decrease sharply given a small doubt of return predictability, even though the predictive variable is large. Adopting the Bayesian strategy can lead to sizable welfare costs for an ambiguity-averse investor, especially when he has a strong prior of return predictability.

Firms face uncertain financing conditions, which can be quite severe as exemplified by the recent financial crisis. We capture the firm's precautionary cash hoarding and market timing motives in a tractable model of dynamic corporate financial management when external financing conditions are stochastic. Firms value financial slack and build cash reserves to mitigate financial constraints. The finitely-lived favorable financing condition induces them to rationally time the equity market. This market timing motive can cause investment to be decreasing (and the marginal value of cash to be increasing) in financial slack, and can lead a financially constrained firm to gamble. Quantitatively, we find that firms' optimal responses to the threat of a financial crisis can significantly smooth out the impact of financing shocks on investments, marginal values of cash, and the risk premium over time. Thus, a firm may still appear unconstrained based on its relatively smooth investment over time despite significant underinvestment. This smoothing effect can be used to disentangle financing shocks from productivity shocks empirically.

Risks of rare economic disasters can have large impact on asset prices. At the same time, difficulty in inference regarding both the likelihood and severity of disasters as well as agency problems can effectively lead to significant disagreements among investors about disaster risk. We show that such disagreements generate strong risk sharing motives, such that just a small amount of optimists in the economy can significantly reduce the disaster risk premium. Our model highlights the "latent" nature of disaster risk: the disaster risk premium will likely be low and smooth during normal times, but can increase dramatically when the risk sharing capacity of the optimists is reduced, for example, following a disaster. The model also helps reconcile the difference in the amount of disaster risk implied by financial markets and international macro data, and provides caution to the approach of extracting disaster probabilities from asset prices, which can disproportionately reflect the beliefs of a small group of optimists. Finally, our model predicts an inverse U-shaped relation between the equity premium and the size of the disaster insurance market.

Non-linearity is an important consideration in many problems of finance and economics, such as pricing securities and solving equilibrium models. This paper provides analytical treatment of a general class of nonlinear transforms for processes with tractable conditional characteristic functions, which extends existing results on characteristic function based transforms to a substantially wider class of nonlinear functions while maintaining low dimensionality by avoiding the need to compute the density function. We illustrate the applications of the generalized transform in pricing defaultable bonds with stochastic recovery. We also use the method to analytically solve a class of general equilibrium models with multiple goods and apply this model to study the effects of time-varying labor income risk on the equity premium.

We propose a model of dynamic investment, financing, and risk management for financially constrained firms. The model highlights the central importance of the endogenous marginal value of liquidity (cash and credit line) for corporate decisions. Our three main results are: 1) investment depends on the ratio of marginal q to the marginal value of liquidity, and the relation between investment and marginal q changes with the marginal source of funding; 2) optimal external financing and payout are characterized by an endogenous double-barrier policy for the firm's cash-capital ratio; and 3) liquidity management and derivatives hedging are complementary risk management tools.

We develop a dynamic incomplete-markets model of entrepreneurial firms, and demonstrate the implications of nondiversifiable risks for entrepreneurs' interdependent consumption, portfolio allocation, financing, investment, and business exit decisions. We characterize the optimal capital structure via a generalized tradeoff model where risky debt provides significant diversification benefits. Nondiversifiable risks have several important implications: more risk-averse entrepreneurs default earlier, but choose higher leverage; lack of diversification causes entrepreneurial firms to underinvest relative to public firms, and risky debt partially alleviates this problem; entrepreneurial risk aversion can overturn the risk-shifting incentives induced by risky debt. We also analytically characterize the idiosyncratic risk premium.

I build a dynamic capital structure model that demonstrates how business cycle variation in expected growth rates, economic uncertainty, and risk premia influence firms' financing policies. Countercyclical fluctuations in risk prices, default probabilities, and default losses arise endogenously through firms' responses to macroeconomic conditions. These comovements generate large credit risk premia for investment grade firms, which helps address the credit spread puzzle and the under-leverage puzzle in a unified framework. The model generates interesting dynamics for financing and defaults, including market timing in debt issuance and credit contagion. It also provides a novel procedure to estimate state-dependent default losses.