Quarterly Review of Economics and Finance最新文献

Bond and bank financing coexist despite their similarities as debt financing. I hypothesize that strengthening the rule of law in each country impacts corporate monitoring and firms’ financing preferences. I use panel data from 50 countries to analyze how the strength of the rule of law differs depending on countries’ legal origins. By using a regression model with an interaction term, I estimate marginal effects to determine if a stricter rule of law promotes bank or bond financing. The findings show that countries with common-law legal origins tend to have a stronger rule of law than civil law countries. A stronger rule of law increases bank lending but has a negative impact on bond issuance. This effect has a more significant impact in countries with Scandinavian legal origins and only a minor effect in countries with French legal origins. These differences can be attributed to how each country addresses agency problems, concerns regarding bank influence, and the availability of additional banking services.

Based on the data of listed companies in Shanghai and Shenzhen that have implemented private equity placements (PEP) from 2007 to 2020, we examine the impact of different types of government subsidies on institutional investors' participation in PEPs. We found that, first, the more government R&D subsidies obtained by companies issuing PEP, the greater the proportion of strategic investors’ subscriptions, with corporate R&D investment playing an intermediary role. Second, the participation of strategic investors in PEPs can effectively contribute to the positive impact of R&D subsidies on firms' innovation output, while the participation of financial investors doesn’t. This study reveals the heterogeneity of the information transfer effects of government subsidies and the investment preferences of institutional investors. Our research provides empirical evidence for the authority to improve the government subsidy policy and PEP system, and provide a referenced theoretical basis for institutional investors who intend to participate in PEPs.

Generally, in the real market, empirical findings suggest that either local volatility (LV) or stochastic volatility (SV) models have a limit to capture the full dynamics and geometry of the implied volatilities of the given equity options. In this study, to overcome the disadvantage of such LV and SV models, we propose a special type of hybrid stochastic-local volatility (SLV${}^{\ast }$) model in which the volatility is given by the squared logarithmic function of the underlying asset price added to a function of a fast mean-reverting process. By making use of asymptotic analysis and Mellin transform, we derive analytic pricing formulas for European derivatives with both smooth and non-smooth payoffs under the SLV${}^{\ast }$ model. We run numerical experiments to verify the accuracy of the pricing formulas using a Monte-Carlo simulation method and to display that the proposed new model fits the geometry of the market implied volatility more closely than other models such as the Heston model, the stochastic elasticity of variance (SEV) model, the hybrid stochastic and CEV type local volatility (SVCEV) model and the multiscale stochastic volatility (MSV) model, especially for short time-to-maturity options.

The effectiveness of inflation targeting is linked to the stationarity properties of inflation. Without making apriori assumptions about the order of integration, we examine whether there is a change in the inflation persistence in one hundred and twenty-seven countries (developed and developing) using monthly data over the 1970–2021 period. For the inflation targeters, we find that the endogenously identified break dates are not consistent with the formal adoption of IT. Logit analysis reveals that inflation targeters do not experience an increased probability of a change in inflation persistence. The quality of institutions emerges as more significant for taming inflation.

This paper modifies the quantity theory of money to forecast inflation, relating the latter to scale variables such as monetary aggregate M2 and government bonds that measure the money demand for asset transactions. The out-of-sample forecast results show that at least since the early 1990s, the money/asset model that uses the money supply/government debt ratio as a predictor has been significantly improved upon univariate and multivariate models, such as Phillips curve and term spread models, for forecasting U.S. inflation over one- to three-year horizons. In using real-time vintage data, I find that, since 2000Q1, the forecasts derived from the money/asset model have slightly improved upon those from the Greenbook in forecasting quarter-over-quarter CPI inflation at short horizons, from two- to four-quarter. These results imply that the Federal Reserve can use the money supply/government debt ratio to forecast and control the inflation rates, coordinating monetary policy with fiscal policy. Moreover, the money supply/government debt ratio can partly explain the U.S. inflation dynamics from the early 1960s until COVID-19.

Using firm-level data from Turkiye, we investigate the effects of earthquakes on firms’ balance sheets. We find that earthquakes increase firms’ liabilities but have a smaller effect on firms’ assets, both in magnitude and significance. Using surveys sent to the finance and/or accounting managers of the largest 100 firms in Turkiye we identify common themes in their perceptions. Our findings reveal a consensus among respondents attributing the increased liabilities to exchange rate depreciation and lower business activity following a disaster. Conversely, higher availability of external credit is associated with a decrease in liabilities. Our analysis also indicates that finance managers with higher educational attainment may be underestimating the effects of earthquakes.

The European Monetary Union (EMU) sovereign debt crisis has been thoroughly investigated in the literature. However, our analysis attempts to shed light on the link between the U.S. and the PIIGS (Portugal, Ireland, Italy, Greece, and Spain) bond markets during the Great Recession. We employ a daily 12-year period dataset and utilize an EGARCH-X approach. Our results reveal significant contagion effects from the U.S. bond market towards the yields of PIIGS bonds. However, our findings suggests that the distribution imposed on the standardized residuals is crucial for identifying the magnitude of the contagion.

This study examines the relationship between environmental, social, and corporate disclosures and its affects on financial position of the firm. The study is based on India’s listed companies which disclosed the ESG variable over the last decade. We examine the five measures of financial performance and firm ESG disclosures. In addition to financial performance measures as response variables, this study uses of Piotroski F scores as a proxy for financial position is unique and novel approach in sustainable finance research. The results show the social disclosures have positive significant affect on firm financial position. Firm value, valuation of the stock and cost of capital. The study will help regulators to strengthen the ESG disclosures and for investors it gives insight about the relationship between ESG disclosure and financial performance of a firms.

This study utilizes weekly datasets on loan growth in Colombia to develop a daily indicator of credit expansion using a two-step machine learning approach. Initially, employing Random Forests (RF), missing data in the raw credit indicator is filled using high frequency indicators like spreads, interest rates, and stock market returns. Subsequently, Quantile Random Forest identifies periods of excessive credit creation, particularly focusing on growth quantiles above 95 %, indicative of potential financial instability. Unlike previous studies, this research combines machine learning with mixed frequency analysis to create a versatile early warning instrument for identifying instances of excessive credit growth in emerging market economies. This methodology, with its ability to handle nonlinear relationships and accommodate diverse scenarios, offers significant value to central bankers and macroprudential authorities in safeguarding financial stability.

Using data on Chinese A-share listed firms from 2010 to 2020, this study employs a partial observable bivariate probit model and introduces fraud triangle theory to explain the mechanisms of overconfidence, short selling, and corporate fraud. Our findings show that overconfidence offers rationalization to investors and corporations, reduces fraud detection, and increases corporate incentives to commit fraud. Short selling promotes information transparency, increases fraud detection, and reduces the opportunities to commit fraud. Moreover, it moderates the relationship between overconfidence and corporate fraud. In addition, overconfidence and short selling affect different types of fraud (operational, executive, and information disclosure fraud). Furthermore, our results show heterogeneity among the ownership types. This study provides a theoretical basis for corporate fraud governance in China’s stock market.