We develop a unified framework to study the term structure of risk premia of nontradable factors. Our method delivers level and time variation of risk premia, uncovers their propagation mechanism, is robust to misspecification and weak identification, and allows for segmented markets. Most macroeconomic factors are weakly identified at quarterly frequency, but have increasing (unconditional) term structures with large risk premia at business cycle horizons. Moreover, the slopes of their term structures are strongly procyclical. Most macroeconomic and intermediary-based factors command similar risk premia in equity and corporate bond markets, while we find strong evidence of segmentation for other factors.

Analyzing 563 trillion possible models, we find that the majority of tradable factors designed to price bond markets are unlikely sources of priced risk, and only one novel tradable bond factor, capturing the bond post-earnings announcement drift, should be included in the stochastic discount factor (SDF) with very high probability. Nevertheless, the SDF is dense in the space of observable factors, with both nontradable and equity-based factors being salient for pricing corporate bonds, and a Bayesian model averaging–SDF explains corporate risk premia better than all existing models, both in- and out-of-sample, and captures business cycle and market crash risks.

Replication data available at: Open Source Bond Asset Pricing

Award financial support by INQUIRE Europe, grant 2023-10-03

We analyse the structure of the UK repo market using a regulatory dataset that covers about 70% of this market. We examine the maturity structure, collateral types and different counterparty types that engage in this market and try to estimate the extent of collateral rehypothecation by the banks. We try to address the question of what variables determine haircuts using transaction-level data. We find that collateral rating and transaction maturity have first order importance in setting haircuts. Hedge funds, as borrowers, receive a significantly higher haircut even after controlling for measures of counterparty risk. We find that larger borrowers with higher ratings receive lower haircuts, but this effect can be overshadowed by collateral quality, because weaker borrowers try to use higher quality collateral to receive a lower haircut. Lender characteristics appear to matter for haircuts, but the results are less stable. Finally we examine the structure and attributes of the repo market network and assess if the network structure has an influence over haircuts. We do not find a significant effect in that respect.

Using information in returns we identify the stochastic process of consumption – the crucial ingredient of most macro-finance models. We find that aggregate consumption reacts over multiple quarters to innovations spanned by financial markets, and this persistent component accounts for more than a quarter of consumption variation. These innovations drive most of the time-series variation of equity returns and a small, but significant, share of volatility of bond returns. The data rejects the hypothesis that the stochastic volatility of consumption is proportional to market volatility, and that either of them is priced, posing a novel challenge for consumption-based asset pricing models.

Previously circulated as “Consumption Risk of Bonds and Stocks” and “Consumption”
Winner of Oustanding Paper Award in Investment, Midwest Finance Association, 2016 

We show that a non-parametric estimate of the pricing kernel, extracted using an information-theoretic approach, delivers smaller out-of-sample pricing errors and a better cross-sectional fit than leading factor models. The information SDF (I-SDF) identifies sources of risk not captured by standard factors, generating very large annual alphas (10%-18%) and Sharpe ratios (0.90-1.3). I-SDFs extracted from a wide cross-section of equity portfolios are highly positively skewed and leptokurtic, and imply that about half of the observed risk premia represent a compensation for tail risk. The I-SDF offers a powerful benchmark relative to which competing theories and investment strategies can be evaluated.

Information asymmetries and trading costs, in a financial market with dynamic information, generate a self-exciting equilibrium price process with stochastic volatility, even if news have constant volatility. Intuitively, new information is released to the market at trading times that, due to traders’ strategic choices, differ from calendar times. This generates an endogenous stochastic time change between trading and calendar times, and stochastic volatility of the price process in calendar time. In equilibrium: price volatility is autocorrelated and is a non-linear function of number and volume of trades; the relative informativeness of number and volume of trades depends on the data sam- pling frequency; volatility, price quotes, tightness, depth, resilience, and trading activity, are jointly determined by information asymmetries and trading costs. Our closed form solutions rationalize a large set of empirical evidence and provide a natural laboratory for analyzing the equilibrium effects of a financial transaction tax. 

We propose a novel measure of the cost of consumption fluctuations that does not require taking a stand on neither the specification of preferences nor the dynamic structure of the economy. Using data on consumption and asset prices, we use an information-theoretic approach to estimate the pricing kernel in a model-free setting. The estimated kernel – that has the interpretation being a non-parametric maximum likelihood estimate – accurately prices broad cross-sections of assets and exhibits substantial skewness, the latter suggesting that tail risk is an important of priced risk. The kernel implies that the cost of all consumption fluctuations in consumption is an order of magnitude higher than what has been argued in the literature. Moreover, contrary to earlier literature, the cost of business cycle fluctuations constitutes a substantial proportion of the cost of all consumption fluctuations. The difference in results from earlier literature can be attributed to the pricing ability of the estimated kernel and its non-Gaussian distribution.

We consider an economy in which some agents do not continuously hedge their position in derivative assets using the underlying assets market – i.e. we study the effects of an imbalanced derivate market. We show that, even in the presence of complete markets, the imbalance significantly alters the equilibrium price process of the underlying assets: risk premia and volatility become stochastically time varying, hence option implied volatility is characterized by smile and smirk patterns, momentum-like price dynamics arise as well as price spillovers across underlying assets. Moreover, the derivative imbalance generates self-fulfilling equilibria, e.g. if the imbalance takes the form of a bet on an increase in asset volatility, then the equilibrium volatility does increase. Finally, since our formulation is extremely general, our results also apply to segmented markets where some investments are achievable only via financial intermediation.

This paper shows that social networks have significant effects on loan repayments. In the loan records of a peer-to-peer lending platform, we proxy social networks based on the contact persons that borrowers provide at loan applications. We estimate the effect of the propensity to pay of a borrower on the propensity to pay of their contact persons using a Spatial Autoregressive Probit model. If the probability to repay of a borrower’s contact persons increases by 10%, the repayment probability of the linked borrower increases by 0.8%, which increases the lender’s profit on average by 360 RMB (i.e. $52). In contrast, a borrower’s propensity to pay does not significantly affect the propensity to pay of other borrowers from the same home or work address. We interpret the results as evidence that social networks affect loan repayment decisions beyond common borrower characteristics and financial situations.

We study the implications of human capital hedging for international portfolio diversification. First, we show that given the degree of international economic integration observed in the data, very small domestic redistributive shocks can lead to home country bias in portfolio holdings. Second, we find that the seminal empirical result of Baxter and Jermann (1997) – that the international diversification puzzle is worsened if we consider the human capital hedging motive – is driven by an econometric misspecification that restricts the countries considered in their study to be economically not integrated. Moreover, once this misspecification is corrected, considering the human capital risk does not unequivocally worsen the puzzle, and in some cases helps explaining it. Third, we document that the substantial statistical uncertainty on measuring returns to the aggregate capital stock can rationalize the disagreements in the previous literature. Fourth, we find sharp evidence that if the set of assets that can be used to hedge aggregate human capital is restricted to publicly traded stocks, the human capital hedging motive has a negligible impact on optimal portfolio choice. This last finding is driven by the extremely small correlation between stock market returns and returns to human capital.

This paper shows that in a non-representative agent model in which households face short selling constraints and labor income risk, in the form of both uninsurable shocks and a common aggregate component, small differences in the correlation between aggregate labor income shocks and domestic and foreign stock market returns lead to a very large home bias in asset holdings. Calibrating this buffer-stock saving model to match both microeconomic and macroeconomic U.S. labor income data, I demonstrate that, consistent with the empirical literature, a) investors that enter the stock market will initially specialize in domestic assets, b) individual portfolios become more internationally diversified, adding foreign stocks one at a time, as the level of asset wealth increases, and c) most importantly, the implied aggregate portfolio of U.S. investors shows a large degree of home bias consistent with observed levels.