We propose a novel measure, "Liquidity Mismatch Index (LMI)," to gauge the mismatch between the market liquidity of assets and the funding liquidity of liabilities for the banking sector. LMI can be used for macro-prudential liquidity stress test, and for predicting cross-sectional bank risk. The outperformance of LMI than Basel III's LCR, NSFR results from the time-varying liquidity sensitivity weights which are driven by market prices. 

Data:​   click here to download

-- The first principal component of haircut across assets

-- The proxy of the liquidity premium: the 3-month Tbill-OIS spread

-- Sample period: 2002Q2 - 2015Q1

Note: To construct LMI, you also need bank holding data which can be downloaded from the website of the Federal Reserve Bank of Chicago (link). Please refer to Appendix A of the paper for the details of LMI calculation. If you need Stata code, please contact me.  

Foreign banks, similar to their domestic counterparts, play a vital role in the financial system of the United States. Nevertheless, our knowledge regarding foreign banks' business activities in America, specifically their lending counterparties and lending terms, is less comprehensive compared to our extensive understanding of U.S. bank loans. In this paper, we leverage the confidential data and examine their role in the U.S. lending market. 

The securities lending market allows institutional investors, such as insurance companies, to lend out asset holdings in exchange for cash collateral, an important but understudied source of funding. Since securities lenders are also primary investors in corporate bonds, we hypothesize that their lending preference for certain types of bonds can influence corporate financing policies. Indeed, we observe that a higher lender preference for long-term bonds stimulates firms to issue more such bonds and helps boost bond prices. The analysis exploiting a quasi-experiment supports a causal interpretation. 

We identify the unique role of the government bond lending market in accessing safe assets during periods of market stress: collateral transformation. 

Even though credit spreads on short-maturity investment grade bonds appear "high" given their low historical default rates, these high Sharpe ratios should be interpreted as "fair compensation" for downward jump-risk.  

A model specifies asset dynamics instead of equity dynamics alone can explain the large spread in prices between put options written on individual banks and options written on the bank index during the financial crisis, even without the government bail-out story. It's important to look at equity and debt together! 

We investigate the cross-sectional variation in the CDS-bond basis. The evidence is consistent with `limits to arbitrage' theories in that deviations are larger for bonds with higher frictions as measured by trading liquidity, funding cost, counterparty risk, and collateral quality. Surprisingly, however, we find that the basis is more negative when the bond lending fee is higher, suggesting that arbitrageurs are unwilling to engage in a negative basis trade when short interest on the bond is high.

This paper examines the capability of firm fundamentals in explaining the cross-sectional variation of CDS spreads.  

Reduced form models of default that attribute a large fraction of credit spreads as compensation for credit event risk typically preclude the most plausible economic justification for such risk to be priced, namely, a ``contagious" response of the market portfolio during the credit event.  

Strong property rights institutions tend to weaken the sovereign transfer risk, whereas contracting institutions (protection of creditor rights or minority shareholder rights) do not matter. 

Chengtou bond is the soli asset with market prices that can capture the funding cost of Chinese local government debt. In contrast to the U.S. municipal bonds, Chengtou bonds are issued by private corporations but implicitly guaranteed by the local hence central governments, which are reflected by novel risk characteristics---real estate GDP and political risk. One standard deviation increase in local real estate GDP (political risk) corresponds to 10 (9) basis points decrease (increase) in bond yields, respectively. However, conditional on political risk, real estate GDP actually increases bond yields, suggesting that only local governments with low political risk can enjoy the low funding costs driven by high real estate growth. ​ 

We provide time-series and cross-sectional evidence on the significance of a risk-return tradeoff in the bond and equity markets. We also propose novel measures of systematic and idiosyncratic risk for individual corporate bonds and find a significantly positive cross-sectional relation between systematic risk and expected bond returns, whereas there is no significant link between idiosyncratic risk and future bond returns. 

Data: Replication of BBW factors with WRDS data  | The updated factors are here.

Public SAS codes can be downloaded here. 

The finance industry has grown, financial markets have become more liquid, information technology has been revolutionized. But have financial market prices become more informative?  

Can physical-human-interaction-based information be substituted by machine-based information? Is human-interaction-based information tied to physical contacts or virtual meetings sufficient to produce it?

What drives the value of information about corporate bonds? How to quantify the dollar value of private information for bond investors?

Despite common wisdom that equities and bonds are segmented, the organization structure of fund families can offset frictions regarding cross-asset segmentation. We find that actively-managed equity funds and corporate bond funds linked within a mutual fund family exhibit a significant co-movement in holdings of commonly-held firms' equities and bonds. Such cross-holdings facilitate information spillover, manifesting itself in the co-movement. Synthesizing cross-asset information  can predict future equity returns and create profits for fund families as well as general investors. Our findings accentuate the importance of collaboration between equity funds and bond funds within fund families. 

We document the rise in China's global corporate ownership across 162 countries over the past decade using detailed firm level data, and assess its real impact on target firms, its spillover effect on non-Chinese-owned firms in the same country and industry, and its spillback effect on ultimate owners’ firms in China. We find that China’s global ownership increased at a rate of 30 percent per annum to reach a global share of 1.2 percent as of 2019. After being acquired by Chinese shareholders, target firms tend to increase R&D investments by 16.4% on average, gain larger market shares, become less profitable, and hold less cash, while also becoming more integrated into China-related supply chains. In contrast, competing firms in the same country and industry exhibit reduced R&D investments and diminished market share. These effects are more pronounced when the acquiring firms are affiliated with the Chinese government and state-owned entities, consistent with a strategic emphasis on R&D by the Chinese government. We also find that government-related entities target natural resource sectors strategically. Lastly, we identify a significant spillback effect, whereby Chinese firms augment domestic R&D investment following overseas investment in R&D-intensive firms, indicative of knowledge transfers.

We study how the implementation of emissions trading systems (ETS) impacts emissions reductions and the usage of renewable energy using a panel sample of the largest 100 countries worldwide.  

We analyze two reasons for export prices to be different across markets, namely quality differentiation and variable markups, and attempt to parse their relative importance and some of their underlying drivers. To overcome the substantial measurement issues in this task, we consider a particular industry as a special case: Chinese fine art. 

​We examine the relationship between MIDAS and Kalman filter state space models applied to mixed frequency data.