We generalise a stochastic version of the workhorse SIR (Susceptible-Infectious- Removed) epidemiological model to account for spatial dynamics generated by network interactions. Using the London metropolitan area as a salient case study, we show that commuter network externalities account for about 42% of the propagation of COVID-19. We find that the UK lockdown measure reduced total propagation by 44%, with more than one third of the effect coming from the reduction in network externalities. Counterfactual analyses suggest that: i) the lockdown was somehow late, but further delay would have had more extreme consequences; ii) a targeted lockdown of a small number of highly connected geographic regions would have been equally effective, arguably with significantly lower economic costs; iii) targeted lockdowns based on threshold number of cases are not effective, since they fail to account for network externalities.
We propose a novel framework for analyzing linear asset pricing models: simple, robust, and applicable to high dimensional problems. For a (potentially misspecified) standalone model, it provides reliable price of risk estimates for both tradable and non-tradable factors, and detects those weakly identified. For competing factors and (possibly non-nested) models, the method automatically selects the best specification – if a dominant one exists – or provides a Bayesian model averaging (BMA-SDF), if there is no clear winner. We analyze 2.25 quadrillion models generated by a large set of factors, and find that the BMA-SDF outperforms existing models in- and out-of-sample
BayesianFactorZoo R CRAN package (user-friendly R package that implements all the methods in the paper, with extensive documentation and teaching examples) (extended) Presentation Slides An early video presentation by Svetlana Bryzgalova Full replication codes (including usage examples and posterior draws of the published version, 4.56GB) BMA-SDF (time series of BMA-SDF posterior mean for different SR values, based on factors of Table 3, csv format)
Using a structural model, we estimate the liquidity multiplier of an interbank network and banks’ contributions to systemic risk. To provide payment services, banks hold reserves. Their equilibrium holdings can be strategic complements or substitutes. The former arises when payment velocity is high and payments begets payments. The latter prevails when the opportunity cost of liquidity is large, incentivising banks to borrow neighbors’ reserves instead of holding their own. Consequently, the network can amplify or dampen individual shocks. Empirically, network topology explains cross-sectional heterogeneity in banks’ contribution to systemic risks while changes in the equilibrium type drive the time-series variation.
We consider asset pricing models in which the SDF can be factorized into an observable component and a potentially unobservable one. Using a relative entropy minimization approach, we nonparametrically estimate the SDF and its components. Empirically, we find that the SDF has a business-cycle pattern and significant correlations with market crashes and the Fama-French factors. Moreover, we derive novel bounds for the SDF that are tighter and have higher information content than existing ones. We show that commonly used consumption-based SDFs correlate poorly with the estimated one, require high risk aversion to satisfy the bounds and understate market crash risk.
We study the implications of human capital hedging for international portfolio choice. First, we document that, at the household level, the degree of home country bias in equity holdings is increasing in the labor income to financial wealth ratio. Second, we show that a heterogeneous agent model in which households face short selling constraints and labor income risk, calibrated to match both micro and macro labor income and asset returns data, can both rationalize this finding and generate a large aggregate home country bias in portfolio holdings. Third, the empirical evidence supporting the belief that the human capital hedging motive should skew domestic portfolios toward foreign assets, is driven by an econometric misspecification rejected by the data.
Note: This paper is based upon, and replaces, two companion papers: “The International Diversification Puzzle is not Worse Than You Think” and “Human Capital and International Portfolio Choice.”
Probably not. First, allowing the probabilities of the states of the economy to differ from their sample frequencies, the Consumption-CAPM is still rejected in both U.S. and international data. Second, the recorded world disasters are too small to rationalize the puzzle unless one assumes that disasters occur every 6-10 years. Third, if the data were generated by the rare events distribution needed to rationalize the equity premium puzzle, the puzzle itself would be unlikely to arise. Fourth, the rare events hypothesis, by reducing the cross-sectional dispersion of consumption risk, worsens the ability of the Consumption-CAPM to explain the cross-section of returns.
A reduction in inflation can fuel run-ups in housing prices if people suffer from money illusion. For example, investors who decide whether to rent or buy a house by simply comparing monthly rent and mortgage payments do not take into account the fact that inflation lowers future real mortgage costs. We decompose the price-rent ratio into a rational component — meant to capture the “proxy effect” and risk premia — and an implied mispricing. We find that inflation and nominal interest rates explain a large share of the time-series variation of the mispricing, and that the tilt effect is very unlikely to rationalize this finding.
This paper evaluates the central insight of the consumption capital asset pricing model that an asset’s expected return is determined by its equilibrium risk to consumption. Rather than measure risk by the contemporaneous covariance of an asset’s return and consumption growth, we measure risk by the covariance of an asset’s return and consumption growth cumulated over many quarters following the return. While contemporaneous consumption risk explains little of the variation in average returns across the 25 Fama-French portfolios, our measure of ultimate consumption risk at a horizon of three years explains a large fraction of this variation.
Most households’ portfolios are extremely close to the efficient frontier once we explicitly take into account no short-selling constraints, while the null hypothesis of efficiency is rejected for all portfolios if we don’t consider these constraints.
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.
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 per sistent 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.
Work in Progress
Speculative Trading and Derivative Market Imbalances, with A. Danilova and Y. Stoev
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.
Social Networks and Loan Repayments, with K. Yuan, and Y. Yuan
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.
