Research Interest

My research is mainly focused on corporate finance and banking. Particularly, my research studies lines of credit (a flexible loan) from the demand side (corporation) and the supply side (bank) and how credit lines are related to corporate credit risk, liquidity risk, banks' funding risk, XVA, and central bank's monetary policies. Recently, I study how credit lines can mitigate climate risk.

My studies also focus on interdisciplinary topic, such as the combination of signal processing and asset pricing.

Conferences

Working Papers

Bank Capital Structure, Valuation Adjustments and Financial Market Liquidity (Policy note. With Mario Cerrato)

Abstract: Valuation adjustments (XVAs) to systemic US banks' derivatives portfolios – caused by swings in their own creditworthiness and that of their clients; for example, COVID-19 has had a significant impact on their revenues and, therefore, market intermediation. This paper studies the implications of funding value adjustments (FVA) on banks' equity holders. Indeed, it is important to understand this implication, as dealers work in the interests of their shareholders. Therefore, intermediation could result in impairment when that cannot be achieved due to friction. Our findings offer critical insights into how financial institutions navigate valuation adjustments and their impact on banks' balance sheets and discuss policy implications related to the main results.

Access to Credit and Short-Term Liquidity Sprint: Evidence from the European Labour Market (With Hormoz Ramian and Mario Cerrato, under review by journal)

Abstract: Corporate credit lines have remained an indispensable source of short-term liquidity management, particularly across the European financial landscape. We provide novel empirical evidence that in the first quarter of 2022, the outstanding funds acquired via the credit lines by European companies reached €87bn, accounting for 5.15% of their total assets. Our study first provides a causal identification to explain corporate credit line drawdown decisions as a response to unexpected shortfalls in the realized earnings. We show that drawdowns increase by an average of 3.17 percentage points, measured in terms of credit line drawdowns scaled by the total assets, in response to a one percentage point (unanticipated) decline in the corporate earnings to their total assets. We further investigate the comprising components of company earning outcomes and show that the inelastic nature of labour in generating corporate earnings during the 2020:Q2 provides an alternative causal identification framework to trace exogenous shocks to the labour initially towards earning outcomes and to subsequently measure the corresponding variations on the drawdown decisions. The results remain consistent with the earlier findings, where the quantity of reliance on corporate credit lines is shown to be 3.34 percentage points given a change in the earning realizations for the companies with higher exposures to labour shocks while consistently establishing no credit drawdown result for companies with lower exposures to the shock.

Quantitative Easing, Banks' Debt Overhang Costs, and Credit Line Prices (With Mario Cerrato. Job market paper. Presented in EFMA 2024 and Lancaster University)

Abstract: Recently, Cooperman et al. (2023) show that the covariance of banks’ funding costs and credit lines draw-downs is debt overhang costs for the bank’s equity holders. In this paper, we empirically and theoretically study whether this cost can be mitigated by central banks’ quantitative easing. We focus on the COVID-19 shock. Based on Cooperman et al. (2023), we empirically find that funding costs generate frictions related to banks’ shareholders (debt overhang cost), and banks transfer that cost to the credit lines’ fees. However, our econometric analysis, event studies, and theory suggest and formalise why central banks’ quantitative easing (QE) can be crucial to mitigating that cost, thereby ensuring a cheaper supply of credit to the economy. Our findings shed further light on the intricate relationship between banks’ funding costs and related debt overhang (Andersen et al. 2019), focusing on an important source of credit for firms: credit lines.

The Dark Side of FX Volume: Evidence from Large Dealer Banks (With Mario Cerrato and Tongtong Wang. Working paper. Accepted by EFMA 2025.)

Abstract: We employ two novel proprietary fx volume and order flows data-sets and provide evidence on the informative role of fx volume before and after 2008. Our empirical evidence suggests that US$ spot fx trading volume post-2008, is highly associated with the scrambling for US dollars discussed in Bianchi et al (2021). In short, FX dealers, hit by an adverse shock on capital, reduce the intermediation (supply) of US dollar and increase that of the risky asset (foreign currency) by skewing the foreign quote to attract US dollars. We present evidence suggesting that one possible mechanism behind this effect is related to dealers' balance sheet frictions. In this paper we focus on debt overhang cost (Myres (1974)). We show that debt overhang cost introduces a shadow cost on capital for the dealer and this cost could explain why she has an incentive to increase the intermediation of foreign currency and reduce that of US dollars. We propose and discuss a theoretical model to motivate it. 

European firms, Panic Borrowing and Credit Lines Drawdowns: What did we learn from the COVID-19 Shock? (With Mario Cerrato and Hormoz Ramian, online appendix, submitted manuscript)

Abstract: Using data for European firms and COVID-19 as an exogenous shock, we show that at the peak of the COVID-19 shock in 2020:Q2, European firms went into a "panic borrowing'' status and drew down  €87bn in a very short period. This aligns with what was reported in early studies for US firms (Acharya & Steffen 2020b). More interestingly, we explore the heterogeneity across European countries and industries and the interplay between banks (supply of credit lines) and firms. We report some new and interesting results. First, heterogeneity across countries and industrial exposure to the COVID-19 shock help us understand why some firms drew down credit lines in March 2020. Banks accommodated the demand for credit insurance during the same period. Our study exploits the implications of social distancing policies on corporate operations across Europe. The novel aspect of our study is that, unlike the previous literature, we focus on shocks unrelated to firms' fundamentals.

Firm-Oriented Credit Line Model

Abstract: Literature focuses on lenders’ determinants in credit line issuance, but little work mentions why borrowers choose this debt financing tool. We develop a corporate financing and investment model and explore the optimal operation decision for demanding credit lines. Our model highlights the solvency risk in firms’ credit line usage and provides rationales for firms drawing credit lines for cash savings in aggregate shocks. Using European data during the COVID-19 crisis, we provide stylized facts about credit line usage, pandemic exposure, and corporate productivity. 

The Time Function of Stock Price: An integral white noise model and its time- and frequency- domain characteristics (With Hong Gao)

Abstract: This paper defines the quantitative relationship between the stock price and time as a time function. Based on the empirical evidence that “the log-return of a stock is the series of white noise”, a mathematical model of the integral white noise is established to describe the phenomenon of stock price movement. A deductive approach is used to derive the auto-correlation function, displacement formula and power spectral density (PSD) of the stock price movement, which reveals not only the characteristics and rules of the movement but also the predictability of the stock price. The deductive fundamental is provided for the price analysis, prediction and risk management of portfolio investment.

Asset Price Model and its Frequency-Domain Characteristics

Abstract: Mathematical models are widely used to measure the fundamental value of an asset. These models, based on different asset pricing theories, provide quantitative methods to determine the appropriate return and therefore help to design a portfolio or a derivative. However, the existing models have false assumptions of the asset price movement, which makes them deviate from reality and invalidate the asset pricing confronting the crisis. This dissertation aims to establish a new asset price model to not only correct the false assumption of the existing models, but also bridge the gap between the fundamental and technical analysis. This newly established model redefines the asset price movement as a sample function of the random process with one-to-one corresponding time and finds certain rules of the asset price from the perspective of the frequency domain. Meanwhile, the mathematical model of the finite impulse response (FIR) filter is constructed for the empirical application with the new model. The dissertation also constructs a computerized algorithm of the asset price model, based on the curve-fitting tool. The result reveals that the power spectral density of the asset price movement has a 1/f distribution, corresponding to frequency-domain characteristics defined by the new model, and proves the feasibility of the curve-fitting tool through a high goodness-of-fit.