The Impact and Stability Analysis of Commercial Banks’ Risk Preference on SMEs’ Credit Financing Based on DSGE Model

As an important driving force for China’s economic transformation and upgrading, the problems of financing difficulties and expensive financing for SMEs have become increasingly prominent. The main objective of this paper was to analyze the impact of financial intermediary departments’ risk preference on corporate finance. Under the revised DSGE framework, this paper discusses the impact and stability analysis of commercial banks’ risk preferences on SMEs’ financing. The results show that positive interest rate shocks inhibit commercial banks’ credit to SMEs, and with the increasing weight of commercial banks’ risk preference for default rate, the trend of credit repression will be intensified.

Shocks Matter

We study the optimal management of capital flows in a small open economy model with financial frictions and multiple policy instruments. The paper reports two main findings. First, both foreign exchange intervention (FXI) and macroprudential polices are tools complementary to the monetary policy rate that can largely reduce inflation and output volatility in a scenario of capital outflows. Second, the optimal policy mix depends on the underlying shock driving capital flows. FXI takes the leading role in response to foreign interest rate shocks, while macroprudential policy becomes the prominent tool for domestic risk shocks. These results highlight the importance of calibrating the use of multiple instruments according to the underlying shocks that induce shifts in capital flows.

Usefulness of Interest Income Sensitivity Disclosures

We document multiple dimensions of usefulness of banks' interest income sensitivity disclosures. First, we find management-generated sensitivity measures are predictive of future realized changes in net interest income. Second, we find financial analysts' forecasts of net interest income reflect information provided by interest income sensitivity disclosures. Third, we find equity market responses to interest rate shocks as well as firms' interest rate betas are larger for banks with greater disclosed sensitivity of net interest income to interest rate changes. Across all of these tests, the informativeness of income sensitivity measures is incremental to that of regulatory data. These results suggest that interest income sensitivity disclosures are informative measures of interest rate risk. Our results contradict assertions that these disclosures are useless due to lack of relevance of income sensitivity, poor modeling techniques, and/or redundancy relative to regulatory data.

A DSGE Model-Based Analysis of the Indian Slowdown

A New Keynesian Dynamic Stochastic General Equilibrium (DSGE) model with habit persistence used to examine the US slowdown is also used to analyze the contribution of basic demand and supply shocks to the Indian slowdown. Kalman filter-based maximum likelihood estimation is undertaken with Indian output, inflation and interest rate data. First, our model based output gap tracks the statistical Hodrick–Prescott filter-based output gap well. Second, comparison of estimated parameters, impulse responses and forecast error variance decomposition between India and the US brings out the differences in policy responses, the structure of the two economies and their inflationary processes. There is a higher impact of interest rate shocks on output and inflation, and lower impact of technology shocks on output but higher on inflation in comparison to US. The former indicates monetary policy over-reaction and the latter validates a supply curve that technology shocks shift and inadequate adjustment of actual to potential output. Habit persistence is higher, markup and interest rate shocks are more volatile in India. Markup shocks play a much larger role in determination of Indian inflation again pointing to the importance of supply side factors. Third, smoothed states obtained from the Kalman filter to create counterfactual paths of output and inflation (during 2009:Q4 to 2013:Q2) in the presence of a given shock, show monetary shocks imposed significant output cost. The output gap was negative post the 2011 slowdown and in 2016.