Issue 2 of Review of Finance, Volume 2, 2023
March 20, 2023

Dear EFA Member,

As a current or recent EFA member, we are pleased to forward you the contents of the recently published Issue 2 of Volume 27 of the Review of Finance – the EFA’s own journal – along with digests (short summaries) and abstracts.


Authors: Leonardo Gambacorta, Yiping Huang, Zhenhua Li, Han Qiu, Shu Chen

Collateral is used in debt contracts to mitigate agency problems arising from asymmetric information. Banks usually require their borrowers to pledge tangible assets, such as real estate, to lessen ex ante adverse selection problems or as a way to reduce ex post frictions, such as moral hazard. The use of collateral is more widespread for opaque firms, such as small and medium-sized enterprises (SMEs). It is common for SME owners to pledge their homes to finance their firms. According to a recent survey by the Financial Stability Board, 90% of bank loans to SMEs in the US are collateralised, compared with 82% in Switzerland and 65% in Canada. This drops to 53% in China, where many SMEs lack basic documentation and are geographically remote from bank branches.

With the development of fintech, especially the entry of large technology firms (big techs) into financial services, nontraditional data play an increasingly important role in credit assessment for SMEs. The business model of big techs rests on enabling direct interactions among a large number of users. An essential by-product of their business is the large stock of user data. Data are used as an input to offer a range of services that exploit natural network effects, generating further user activity. Increased user activity then completes the circle, as it generates yet more data. The mutually reinforcing data-network-activity feedback loop helps big tech firms identify the characteristics of their clients and offer them financial services that best suit their needs.

Using a unique dataset of more than 2 million Chinese firms that received credit from both an important big tech firm (Ant Group) and traditional commercial banks, we find that big tech credit does not correlate with local business conditions (see figure) and house prices when controlling for demand factors, but that it does react strongly to changes in firm-specific characteristics, such as the transaction volumes and network scores used to calculate firm credit ratings. This is particularly the case when a borrower firm conducts its business activity on the relevant e-commerce platform managed by the big tech. By contrast, both secured and unsecured bank credit reacts significantly to local house price dynamics, which incorporate useful information on the client’s creditworthiness and the business conditions in which it operates. This evidence implies that the wider use of big tech credit could reduce the importance of the collateral channel but, at the same time, make lending more reactive to changes in firms’ business activity.

Figure 1: Elasticity of credit with respect to house prices and GDP

Note: The figure reports the coefficient of three different regressions (one for each credit types) in which the log of credit is regressed with respect to the log of house prices at the city level, the log of GDP at the city level and a complete set of time dummies. Significance level: ** p<0.05; *** p<0.01


Authors: Alexander Bechtel, Angelo Ranaldo, Jan Wrampelmeyer

We propose a new channel that outlines how liquidity risk impacts interest rates in short term funding markets, the liquidity risk channel. We show that borrowers have a higher willingness to pay for liquidity if they experience liquidity shocks that need to be balanced urgently. High liquidity risk banks experience more liquidity shocks, are more likely to become constrained, and thus pay higher interest rates for short term funding. We test, and quantify this liquidity risk channel, revealing systematic heterogeneity across banks' liquidity risk, their willingness to pay for immediate access to liquidity, and hence their funding cost. This heterogeneity is persistent over more than ten years, suggesting that the channel is related to liquidity management in general and not only arises during crisis times.

Three major identification challenges arise: First, we must isolate funding transactions from transactions with other intentions, such as market making, speculation, arbitrage trading, or sourcing a specific security. Second, we must construct a measure for the idiosyncratic liquidity risk of borrowers. Finally, we must isolate the markup a borrower is willing to pay due to being liquidity constrained from the premiums demanded by lenders. We overcome these challenges by exploiting the institutional design of the European interbank funding market, including anonymous electronic trading and central clearing.

Our results are important for at least three reasons. First, the liquidity risk channel provides an explanation for how liquidity risk can lead to liquidity constraints that affect interest rates in short-term funding markets. Second, it contributes to explain the systematic and persistent heterogeneity in funding costs. Third, the liquidity risk channel becomes even more important in light of the transition to real-time payment systems. The need to settle payments immediately creates additional challenges for the liquidity management of banks and could potentially be a new source of financial instability. Thus, understanding the origins and dynamics of funding constraints is crucial for regulators that aim to maintain financial stability and curtail the liquidity risk of banks (e.g., Basel III and the Dodd-Frank Act).

Figure 1

Panel (a) of Figure 1 presents for each borrower the average daily overnight repo rate paid in excess of the volume-weighted market average rate. In other words, it shows the average additional borrowing costs paid by each bank compared to the market average. Banks on the x-axis are ordered according to their performance. The filled black circles denote statistical significance at the 5% level. One outlier at 8.4 basis points is omitted. Panel (b) depicts the time variation of the banks with a significant positive and negative average excess rate, that is, those above and below the solid vertical line in Panel (a).


Authors: Thomas Schneider, Philip E Strahan, Jun Yang

Bank stress testing by the U.S. Federal Reserve (the Fed) is the most important innovation in bank capital regulation to have emerged after the 2008 Global Financial Crisis. In our recent paper, we investigate whether bank stress testing has been contaminated by capture of regulators by banks. Capture is plausible because the costs and benefits of stress testing are concentrated among the regulated, and the rules and implementation are both complex and opaque. Perhaps surprisingly, our results suggest that stress testing has furthered the public interest. However, we caution that recent changes to this regulatory framework may reduce the efficacy of bank stress testing and its ability to prevent a future financial crisis.

We provide unambiguous evidence that the large U.S. trading banks – those most likely deemed “Too Big to Fail” – face tougher stress tests than other banks across all dimensions. First, large trading banks’ portfolios decline much more under the Fed’s stress testing model than under the large trading banks’ own models, compared to other banks. The difference is large: almost 1.5 percentage points of risk-weighted assets. Second, large trading banks are more likely to fail stress tests, even after controlling fully for their quantitative stress test results.The higher rate of failure for the trading banks reflects greater regulatory scrutiny of internal risk-management practices and governance. Third, large trading banks make more conservative capital plans than other banks. Despite their more conservative capital plans, the large trading banks still fail their tests more frequently than other banks. Last, regulatory capital ratios increase for trading banks in the advent of the stress-testing regime, relative to other banks. We conclude that stress testing has served the public interest by forcing recapitalization of these most systematically important banks.

Also consistent with the public interest, we find no evidence that either political lobbying efforts or bank-regulatory connections affect leniency on the quantitative components of stress tests. We do find some evidence that banks with regulatory connections are less likely to fail the qualitative dimension of stress tests. We hesitate to over-emphasize this result, however, because the effects of connections are generally weak and insignificant in all our other tests.

As we move further from the Global Financial Crisis, public pressure for regulatory oversight has fallen. Banks and their advocates have complained about various aspects of stress testing, such as its opacity, and this advocacy has had an impact very recently. Changes have both relaxed constraints on bank capital distributions and reduced the information disclosed from the stress tests, potentially creating new opportunities for regulatory capture going forward.


Authors: Xuepeng Liu, Heiwai Tang, Zhi Wang, Shang-Jin Wei

Capital controls are common in many developing countries. With capital controls, the standard financial market transactions needed for currency carry trade are hard to implement. Yet, as long as there is a big difference between domestic and foreign interest rates, the incentive to engage in currency carry trade is present. Using detailed trade data reported by both the mainland Chinese and Hong Kong’s governments, we present evidence that indirect currency carry trade likely takes place via round-trip reimports. We find that reimport activities are heavier when the return to currency carry trade is stronger, especially for products with a high value to weight ratio. We also show that greater state control in terms of more state-owned firms does not reduce such “carry trade by trucks.”.


Authors: Adam Farago, Erik Hjalmarsson

ndividual investors often have long investment horizons. For certain saving purposes, like retirement savings, the relevant horizon can easily span several decades. Understanding the properties of stock returns over such horizons is therefore important.

A long-run investor collects the total compound returns over the investment horizon. Multiplicative compounding implies that the moments of long-run returns are non-trivial functions of the moments of short-run period-by-period returns. This is particularly true for higher-order moments such as skewness. We show in this study that skewness becomes a characterizing feature of long-run return distributions. Specifically, our main message is that compounding inevitably leads to (strongly) positively skewed long-run returns. The strength of the skew-inducing effect of compounding depends primarily on the level of volatility in the single-period return -- the higher the volatility, the stronger the effect -- and is not qualitatively affected by potential asymmetries in the single-period return distribution.

Large positive skewness implies that mean (expected) long-run returns are often considerably larger than median returns. Focusing on long-run expected returns can therefore be misleading. As a simple illustration, consider the rule-of-thumb that if an asset delivers a 7% annual expected return, it takes 10 years to double the initial investment. This statement is valid in expected terms, but it ignores the uncertainty in the 10-year outcome. Under standard assumptions, if the asset has a 17% annual volatility (like the U.S. market), there is a 50% chance that the initial investment is doubled only after 13 years, and there is a 30% chance that it takes at least 20 years. For more volatile portfolios, these effects become even more pronounced. Our results highlight that an understanding of the likely long-run outcomes of an investment requires considerably more mental effort than the corresponding short-run exercise. Transforming statistics on annual returns into a meaningful characterization of the distribution of 10- or 30-year returns is clearly beyond the capabilities of most investors.

More formal procedures can also be misleading when applied to long-run returns. Investor-preferences for skewness and higher-order moments are often captured by Taylor expansions of standard utility functions. The large effects of compounding on higher-order moments are shown to affect the validity of such Taylor expansions, when applied to returns of annual or longer horizons.

We also show that for horizons longer than a year, skewness in long-run returns is often impossible to empirically estimate with any reasonable accuracy. Indeed, skewness in compound returns turns out to be so difficult to estimate that care is needed also when running Monte Carlo simulations. Larger-than-normal sample sizes are often needed and, for sufficiently long horizons, obtaining reliable simulation results become virtually impossible.


Authors: Jing-Zhi Huang, Bibo Liu, Zhan Shi

What drives short-term credit spreads is a very important question in credit markets, especially given the role played by short-term corporate debt in the global financial crisis. However, in spite of a large literature on the determinants of credit spreads in general, the empirical literature on short-term spreads is very limited, perhaps because of data limitations.

In this paper, we shed light on the determinants of short-term corporate credit spreads from at least three new perspectives. First, we employ a novel data set of secondary market transactions in Chinese commercial paper over the period May 2014 to December 2020. This market has four unique features that make it particularly suitable for addressing the main question of this study: (1) Secondary market transactions account for 78% of total daily transaction volumes in this market, compared with less than 10% in the US market. This feature makes it possible to implement transaction-based liquidity measures for the commercial paper market. (2) The Chinese commercial paper issuers are heterogeneous in terms of creditworthiness, whereas almost all commercial paper issuers in the US are large, well-capitalized firms. (3) commercial paper in China tends to have a much longer maturity than commercial paper in the US. For instance, the average maturity is about 248 days for Chinese commercial paper and about 45 days for the US in our sample. (4) Longer-term corporate debts and commercial paper are traded in the same market; as such, commercial paper in China can be viewed as exactly equivalent to short-term corporate bonds.

Second, we quantify liquidity and default risk components in short-term spreads using the structural approach to credit risk modeling. In particular, we propose and implement a jump-diffusion structural model that incorporates corporate debt market illiquidity and therefore is particularly suitable for modeling commercial paper spreads. The model is essentially a simplified He and Xiong (2012) model augmented with a double-exponential jump component in the underlying asset return process, albeit without rollover risk. Importantly, the model-implied corporate yield spreads can be decomposed into a diffusion credit component, a jump credit component, and a liquidity component.

Third, we show that liquidity is much more important than credit risk in determining commercial paper spreads in China (see Figure). For instance, our model-based decomposition results show that, on average, credit risk and market liquidity account for about 25% and 52% of commercial paper yield spreads, respectively. Moreover, based on a more recent sample of commercial paper issues and more recently developed liquidity measures, we find similar results in the US commercial paper market over the period May 2014 to April 2020.

Overall, this paper provides a comprehensive study on the determinants of short-term credit spreads using security level data in both the Chinese and the US commercial paper markets. We find that there is a credit spread puzzle (à la Huang and Huang 2012) in both markets. Market liquidity, however, shows much greater importance than credit risk in explaining these commercial paper spreads and therefore helps mitigate the puzzle.

Figure 1: Mean and Median of Predicted Commercial Paper Yield Spreads in China

This figure plots the mean and median of commercial paper yield spreads by rating category. The five bars in each rating category, in turn, represent yield spreads in the data (blue) and those generated by our proposed structural model ─ the He-Xiong model with double-exponential jumps (HX-J in green) ─ and its three special cases. The latter include the Black and Cox (1976) model (orange), the double-exponential jump-diffusion (DEJD) model (yellow), and a simplified He and Xiong (2012) model (HX in purple). Note that the Black-Cox median spread is virtually zero for all rating groups. The sample period spans from May 2014 to December 2020.


Authors: Morris A Davis, William D Larson, Stephen D Oliner, Benjamin R Smith

This paper provides a comprehensive account of the evolution of default risk for newly originated home mortgages over the past quarter century. We bring together several data sources to produce this history, including loan-level data for the entire Enterprise (Fannie Mae and Freddie Mac) book. Our dataset includes more than 200 million home mortgage loans originated from 1990 through 2019. All the results presented in the paper and many other series are available for download at https:/www.fhfa.gov/papers/wp1902.aspx.

We calculate a stressed default rate for every loan in the dataset based on the observed default experience of similar loans originated nationwide in 2006 and 2007. The stressed default rate for a given loan represents its expected performance had it been hit shortly after origination with a replay of the financial crisis and experienced the national average decline in house prices.

Figure 1 shows the stressed default rate for all loans in our sample. The figure clearly shows that the riskiness of mortgage originations in the aggregate rose over the entire period from the mid-1990s to 2006, indicating that seeds of the financial crisis were planted far in advance of the event itself. This finding cuts against the common view that mortgage lending conditions were normal in the early 2000s. After the crisis, mortgage risk fell precipitously and has remained low since.

We analyze the characteristics of mortgage loans that account for the increase in risk over 1994-2006 and find more than half of the rise was due to “plain-vanilla” risk factors like rising loan-to-value ratios and increasing debt-payment to income ratios. Thus, a narrative that focuses primarily on risky product features, such as loans with low or no documentation of income, overstates their role during the boom and underplays the risk-increasing effect of more prosaic forms of leverage.

We also use the data to evaluate the role of “subprime” borrowers (borrowers with a credit score below 660) in the housing boom. We find that the share of mortgage loans made to subprime borrowers was flat on net from 2000 through 2006, the primary years of the housing boom, though this share rose somewhat in the 1990s. We also show that over the entire period from 1994 to 2006, the stressed default rate for subprime borrowers moved in close alignment with the stressed default rate of borrowers with higher scores. These findings are not favorable to a “subprime-centric” view of the financial crisis.

Although our full dataset ends in 2019, we use a subset of the data to provide an update extending into the COVID-19 episode. This update includes Enterprise, FHA, and VA loans and shows that the riskiness of mortgage originations fell as the mix of both home purchase and refinance loans shifted toward lower-risk borrowers.

Figure 1: Stressed Default Rate, All Loans, 1994-2019

Note: The series shown covers first-lien home purchase and refinance mortgage loans secured by 1-4 unit properties. It includes a regression-based adjustment to control for changes in refinance volume. Shading is for 2000-2003.


Authors: Scott Cederburg, Travis L Johnson, Michael S O’Doherty

We study the effects of time-varying volatility and investment horizon on the economic significance of stock market return predictability from the perspective of Bayesian investors. Using a vector autoregression framework with stochastic volatility (SV) in market returns and predictor variables, we assess a broad set of twenty-six predictors with both in-sample and out-of-sample designs. Volatility and horizon are critically important for assessing return predictors, as these factors affect how an investor learns about predictability and how she chooses to invest based on return forecasts. We find that statistically strong predictors can be economically unimportant if they tend to take extreme values in high volatility periods, have low persistence, or follow distributions with fat tails. Several popular predictors exhibit these properties such that their impressive statistical results do not translate into large economic gains. We also demonstrate that incorporating SV leads to substantial utility gains in real-time forecasting.


Authors: Sean Foley, Tom G Meling, Bernt Arne Ødegaard

We examine the market quality effects of explicit tick size competition between trading venues. The laboratory is the European “tick size war” of 2009 in which, owing to the absence of a regulator-mandated uniform tick size, entrant trading venues were able to undercut the tick sizes of the primary exchanges.

Exchanges that reduced their tick size immediately captured the market shares of both quoted and executed volume from the exchanges that kept their ticks large, as shown in the below figure. This tick size competition improves market quality, reducing trading costs and increasing both aggregate depth and volume. These market quality improvements are strongest in stocks where the bid-ask spread was previously most constrained by the tick size, with liquidity providers using the finer pricing grid to engage in price competition.

Our findings are very much consistent with the notion that the “one size fits all” approach currently taken to tick size regulation globally may require revision.

Figure 1

Changing pre--trade and volume market shares. Oslo Stock Exchange. June: Entrant markets lower tick size relative to Oslo. September: Tick Sizes same across all market.


Authors: Vikas Agarwal, Brad Barber, Si Cheng, Allaudeen Hameed, Ayako Yasuda

Mutual fund families set and report values of their private startup holdings, which affect the fund net asset value (NAV) at which investors buy/sell fund shares. We test three hypotheses related to the valuation practice: (i) information cost/access, (ii) litigation risk, and (iii) strategic NAV management. Consistent with (i), families with larger PE holdings and/or stronger information access update valuations more frequently in the absence of public information releases, their updates co-move less with other families, and their fund returns jump less at follow-on financings. We find no support for hypotheses (ii) or (iii). We also find that high-PE-exposure funds are subject to greater financial fragility.


Authors: Hanming Fang, Zhe Li, Nianhang Xu, Hongjun Yan

We study how firms build relations with local governments in emerging markets without established rules of political lobbying. We document that following a turnover of the Party Secretary or mayor of a city in China, firms (especially privately owned enterprises, POEs hereafter) headquartered in that city significantly increase their “perk spending,” for example, expenses for travel and entertainment among others. Both the instrumental-variable-based results and heterogeneity analysis are consistent with the interpretation that the perk spending is used to build relations with local governments. In addition, we find that local political turnover in a city tends to be followed by changes of the Chairmen or the CEOs of state-owned enterprises that are controlled by the local government. We also discuss and rule out several alternative explanations for the above findings.


NOTE: This and previous issues of Review of Finance [ISSN 1572-3097 | EISSN 1573-692X] are freely available to current EFA members as a benefit of annual membership.  For information on how to submit a manuscript to Review of Finance, please visit the RF Editorial Office website revfin.org.  Follow us on LinkedIn.

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