Longer Maturities Research Articles

Forecasting Chinese Stock Market Volatility With Volatilities in Bond Markets

ABSTRACTIn this paper, we investigate whether the bond markets contain important information that can improve the accuracy of stock market volatility forecasts in China. We use realized volatility (RV) implemented by different maturity treasury bond futures contracts to predict the Chinese stock market volatility. Our work is based on the heterogeneous autoregressive (HAR) framework. Empirical results show that the volatility of treasury bond contracts with longer maturities (especially 10 years) has the best effect on predicting the Chinese stock market volatility, both in sample and out of sample. Two machine learning methods, the scaled principal component analysis (SPCA) and the least absolute shrinkage and selection operator (lasso), are also more effective than the HAR benchmark model's prediction. Finally, mean–variance investors can achieve substantial economic gains by allocating their investment portfolios based on volatility forecasts after introducing treasury bond futures volatility.

On the Distribution of the Mixed-Lognormal-Weibull Option Pricing Model

This paper intends to test whether the Mixed-Lognormal-Weibull Distribution (MLWD) option pricing model comes from the same distribution and whether the model is a good fit in Black-Scholes option pricing model. The data for this study were obtained from Australian Clearing House of Australian Securities Exchange (ASX) which consist of 50 enlisted stock as products of monthly market summary for long term options collected from January, 3rd 2017 to December, 31st 2017, comprising 720 trading days arranged in accordance to 25, 27, 28, 29 and 30 maturity days. Maximum Likelihood Estimate was used to obtain the parameters of both the lognormal and Weibull distributions which were applied in Black-Scholes model. The data were test Wilcoxon Rank Sum test since the mixture model became distribution and Chi-square goodness-of-fit test for the model fit, and the result shows that the mixture model does not follow any of the lognormal or Weibull distributions. The result also shows that the mixture model is a good fit in Black-Scholes option pricing model with the P-value>0.05 when they are shorter maturity days with small sample sizes than longer maturity days with larger sample sizes. Hence, the model is recommended to be used for financial practitioners who are interested in modeling option pricing.

The effects of FinTech adoption on bank loan spreads

PurposeThis paper aims to examine the impact of traditional banks’ financial technology (FinTech) adoption on corporate loan spreads and lending practices.Design/methodology/approachThis study examines the impact of FinTech adoption by banks on corporate loan spreads and lending practices. By analyzing data from bank 10-K filings, we develop a novel metric to assess FinTech adoption at the individual bank level. Our analysis reveals a significant positive correlation between increased FinTech adoption and higher corporate loan spreads, particularly for loans that are relatively informationally opaque. This causality is further validated through a quasi-natural experiment. Additionally, we identify trends toward loans with smaller sizes and longer maturities in banks with advanced FinTech integration.FindingsUsing a sample of corporate loans issued from 1993 to 2020, this paper documents a significant positive relationship between a bank’s increased FinTech adoption and higher loan spreads. This correlation is especially noticeable for loans that are informationally opaque. Moreover, the paper reveals trends toward smaller loan sizes and longer maturities with advanced FinTech integration in banks. Overall, these findings indicate FinTech enhances efficiency in processing hard information and holds the potential to enhance financial inclusion.Originality/valueThis paper contributes to two significant strands of finance literature. First, it highlights how banks with advanced FinTech integration gain advantages through enhanced processing of hard information. Furthermore, it underscores the role of FinTech in promoting financial inclusion, particularly for those borrowers facing informational opacity.

Being stranded with fossil fuel reserves? Climate policy risk and the pricing of bank loans

AbstractDo banks price the risk of stranded fossil fuel reserves? To address this question, we hand collect global data on corporate fossil fuel reserves from 2002 to 2016, match it with syndicated loans, and subsequently compare the loan rate charged to fossil fuel firms — along their climate policy exposure — to other firms. We find that banks price climate policy exposure, especially after 2015. We also uncover that our main effect further increases for loans with longer maturity, that loan size to fossil fuel firms increases, and that ‛Green’ banks also charge higher loan rates to fossil fuel firms.

The Valuation of Collateral in Bank Lending

AbstractWe study the valuation of collateral by comparing spreads on loans by the same bank, to the same borrower, at the same origination date, but backed by different types of collateral. Pledging collateral reduces borrowing costs by 23 BPS on average. The effect varies across different types of collateral, with marketable securities being most valuable, and real estate and accounts receivables and inventory being more valuable than fixed assets and a blanket lien. Further, the rate reduction from pledging collateral is sensitive to the value of the underlying collateral, and collateral tends to be more valuable for smaller and private firms and for loans with longer maturity.

Empirical Test on CAPM Model of China's Stock Market by Industry Grouping

Capital Asset Pricing Model (CAPM) is a hotly debated issue of modern financial theory research, and the viability of the capital resource evaluating model in China's flow capital market requires further experimental examination and testing. The indexes of 27 industries and the Shanghai Shenzhen 300 index are taken as the research objects, and the day to day exchanging information from January 5, 2015 to December 31, 2021 are used for regression analysis. This study assesses the risks and rewards of each business by computing the coefficients for various industries throughout various time periods. It then verifies the accuracy of the CAPM model by using the linear relationship between the average return rate and coefficients. Although the return on the stock market is linearly connected with the beta coefficient for the majority of regression findings with a shorter time horizon (less than 100 days), systematic risk is insufficient to account for the expected return on the stock portfolio, which is in conflict with the theoretical assumptions of the capital asset pricing model. Meanwhile, the regression's fit coefficient is modest, indicating that other risk factors have a non-negligible impact on stock returns. For the regression results divided by longer maturity as well as 40 days as maturity, the stock market return shows a non-linear correlation with the beta coefficient. Thus, it may be said that the CAPM model does not apply to the stock market in China.

Deposit market power, funding stability and long-term credit

By increasing funding stability, deposit market power reduces banks’ funding risk over the cycle and provides the flexibility to originate long-term loans. Banks with deposit HHI one standard deviation above average extend loans with about 20% longer maturity than those one standard deviation below average. Deposit market power also allows banks to charge lower maturity premiums. The effects persist in the sample of zero-duration, floating rate loans. This has real effects: access to banks raising funds in less competitive markets improves growth in bank-dependent borrowers needing long-term finance. Deposit market power, by stabilizing bank funding costs, helps alleviate credit cycles.

Voluntary disclosure in P2P lending: Information or hyperbole?

Using a robust textual analytic method, we decompose the P2P loan description into common and distinctive contents, which contain general and unique information provided by borrowers. We then investigate the role of the distinctive content in affecting P2P lending decisions and outcomes. Controlling for loan/borrower characteristics, loan applications with more distinctive content are more likely to be funded, and have larger amounts and longer maturity, but these loans carry higher interest rates and default probability. Overall, the evidence suggests that borrowers use self-reported loan descriptions to hype their loan applications rather than provide soft information to reduce information asymmetry.

High-speed railway and shadow banking: evidence from finance leases

ABSTRACT This paper empirically investigates the effect of improved transportation infrastructure on finance leases, a representative form of China’s shadow banking sector, using a sample of Chinese listed companies. We find robust evidence that there is a significant decline in firms’ finance leases after the opening of the high-speed railway (HSR). Furthermore, HSR brings more favourable contract terms of leasing, including larger financing size, longer maturity, and lower cost. The heterogeneity analysis shows that HSR mainly reduces the costly nonbank-affiliated leases, and finance leases are reduced primarily in state-owned enterprises.

A close look at wealth management products from the Buyer's perspective: Evidence from China

This study contributes to the shadow banking literature by investigating the determinants, market reactions, and consequences of wealth management products (WMPs) purchased by listed companies in China. The empirical results show that non-state-owned enterprises (non-SOEs) with a higher percentage of equity financing, more cash, higher profitability, larger total assets, lower sales growth, younger firm age, and lower Shanghai interbank offered rate (SHIBOR, which represents the overall availability of liquidity and credit in the economy) are more likely to buy WMPs. However, these factors largely fail to explain the WMP purchase behaviour of SOEs; in particular, compared to local SOEs, central SOEs are less likely to buy WMPs. We further find that the market reaction is significantly negative around the announcement of WMP purchases, and this finding is more pronounced for companies that have been listed for less than one year and are non-SOEs. Moreover, our results show that WMPs with longer maturity and larger amounts tend to have higher expected and real returns; in addition, SOEs are more likely to hold WMPs with longer maturity and have higher returns. This study is the first to investigate the determinants, market reactions, and consequences of WMP purchase behaviour in SOEs and non-SOEs, and it has implications for the financial assets held by non-financial firms. The results will be of interest to regulators, policymakers, and future academic research.

Fintech business and firm access to bank loans

AbstractFirms have widely participated in financial technology (Fintech) in recent years. Through a proprietary dataset of listed firms on the Shanghai and Shenzhen Stock Exchanges, we find that Fintech positively contributes to firm access to bank loans, and such link is stronger among private firms, non‐manufacturing firms or those in regions of better marketisation. Further tests suggest that enhanced trust underlies our primary findings, and that Fintech also positively contributes to loans without collateral, longer maturity and lower costs. In addition, Fintech may effectively curb firm's overinvestment. Our findings may have policy implications for firm investment behaviour and financing activities.

National development banks and loan contract terms: Evidence from syndicated loans

In this study, we examine whether national development banks (NDBs) fill market gaps in the syndicated loan market. Existing theories make conflicting arguments, but little empirical research has systematically investigated the roles of NDBs in credit markets. To fill the gap, we build a novel and comprehensive list of NDBs worldwide. Using a large, international sample of non-sovereign syndicated loans over the past two decades, we find that loans with NDBs have longer maturity and higher loan spreads than those without. These results hold using the propensity score matching technique and the treatment effects model that account for the endogenous selection of NDBs. We also find that NDBs provide financial support for credit-rationed borrowers and play a countercyclical role during global liquidity cycles. Our study implies that NDBs address market failures in the syndicated loan market.

Modelling health-data breaches with application to cyber insurance

Data breaches have been increasing noticeably after 2021 notwithstanding the efforts of regulatory bodies to strengthen cybersecurity measures to protect health information. We provide a modelling framework that assesses the risk of private health data breaches focusing on the data sets compiled by the Privacy Rights Clearinghouse and the U.S. Department of Health and Human Services. We show that the counting process of the data-breach incidents is adequately modelled by the Markov-modulated non-homogeneous Poisson process (MMNPP) whilst the logarithm of the breach sizes is well-captured by the generalised Pareto distribution. The cyber insurance premium per institution and two risk measures Value-at-Risk (VaR) and Average VaR are obtained. The computed results indicate that cyber insurance policies with longer maturity are more cost effective. A comprehensive analysis, parameter estimation and implementation of the MMNPP to model cyber risks are underscored as the principal contributions of this research. Some implications to practitioners in handling the modelling of data breaches for a group of institutions are given.

Loan Terms and Collateral: Evidence from the Bilateral Repo Market

ABSTRACTWe study secured lending contracts using a proprietary, loan‐level database of bilateral repurchase agreements containing groups of simultaneous loans backed by multiple tranches within a securitization. We show that lower‐quality loans (i.e., loans backed by lower‐rated collateral) have higher margins and spreads. We calibrate a model using collateral asset prices and find that lower‐quality loans are riskier despite the higher margins, yet cheaper for the borrower. This finding is consistent with a combination of lender optimism and reaching for yield. We also show that lower‐quality loans have longer maturity, consistent with models of rollover concerns with asymmetric information.

Are all types of real transaction management equal in the eyes of bank lenders?

AbstractThis study examines whether bank lenders react differently to various types of real transaction management (RTM) by borrowing firms. Drawing upon the differential cash and cash flow effects of alternative forms of RTM, we predict and find that banks provide more favorable loan terms, that is, lower interest spread and reduced likelihood of required collateral, for firms reporting more discretionary reductions in research and development (R&D) expenditures. In contrast, lending banks respond unfavorably to borrowers’ engagement in RTM through aggressive sales discounts and overproduction of inventories. Additional analysis reveals that the favorable effect of discretionary R&D reductions on loan pricing is greater (smaller) for borrowing firms with a prior relationship with the bank or with a lower level of cash holdings (loans with longer maturity). Overall, our findings suggest that banks, with their unique payoff functions and monitoring incentives, do not view all forms of RTM negatively.

CEO compensation and bank loan contracts

This paper studies the relationship between bank CEO inside debt holdings and banks’ syndicated loan decisions. Using a two-stage selection model, we find that banks with higher CEO inside debt holdings extend syndicated loans to safer borrowers and have a smaller number of lenders in the syndication; these loans have a lower spread, less restrictive covenant, and longer maturity. These results suggest higher bank screening efforts and lower risk-taking incentives associated with bank CEO inside debt. The findings are robust to potential endogeneity bias and simultaneity of various loan terms.

A Bayesian analysis of time-varying jump risk in S&P 500 returns and options

We examine time-varying jump risk for modeling stock price dynamics and cross-sectional option prices. We explore jump-diffusion specifications with two independently evolving processes for stochastic volatility and jump intensity, respectively. We explicitly impose time-series consistency in model estimation using a Markov Chain Monte Carlo (MCMC) method. We find that both the jump size and standard deviation of jump size premia are more prominent under time-varying jump risk. Simultaneous jumps in returns and volatility help reconcile the time series of returns, volatility, and jump intensities. Finally, independent time-varying jump intensities improve the cross-sectional fit of option prices, especially at longer maturities.

Are banking and accounting expertise on the audit committee related to bank loan terms?

AbstractThe extant literature reveals that audit committee (AC) members with industry expertise can enhance the AC's effectiveness in monitoring the financial reporting process. In this study, we focus on AC banking and accounting expertise and examine their effects on bank loan contracting, respectively. We find that banks offer more favorable price term (i.e., interest rate) and non‐price terms (i.e., lower likelihood of providing collateral, more financial covenants, and longer maturity) to borrowers whose AC members have banking or accounting expertise. Second, we find that the association between AC expertise and loan terms is driven more by banking expertise, either alone or in conjunction with accounting expertise, than by accounting expertise alone. The results are robust to a battery of robustness tests. Overall, our results suggest that banks value borrowers’ AC banking experts as well as accounting expertise when designing loan terms.

The relative pricing of WTI and Brent crude oil futures: Expectations or risk premia?

This paper studies the spread of Brent-WTI futures prices using a no-arbitrage term structure model with one common and two latent idiosyncratic risk factors. We document more negative risk premia for WTI than for Brent, and the differences are more pronounced at longer maturities. The expectation of future spot price dominates the risk premium in determining the term structure of Brent-WTI futures spread, especially at short maturities. The common risk premia in both markets are negative and similar, while their corresponding idiosyncratic risk premia have opposite signs. The common risk prices of WTI and Brent are generally related to the US crude commercial stock, inflation, economic uncertainty, and hedging pressure; however, idiosyncratic risk prices are more related to their corresponding local production, short rate, and the term structure factors. The variance decomposition indicates that the idiosyncratic factors account for a considerable part at longer forecast horizons in both markets.

Political ties and raising capital in global markets: Evidence from Yankee bonds

This paper examines whether state-to-state political ties help firms obtain better terms when raising funds in global capital markets. Focusing on the Yankee bonds market, we find that issuances by firms from countries with close political ties with the US feature lower yield spreads, higher issuance amounts, and longer maturities. Such an association is more pronounced for firms located in low income and highly indebted countries as well as firms in government-related industries, first-time issuers, and relatively smaller firms. Our study provides evidence supporting the notion that country-level political relationship is an important factor when raising capital in international markets.