Fintech Entry, Firm Financial Inclusion, and Macroeconomic Dynamics in Emerging Economies

Financial inclusion is strikingly low in emerging economies. In only a few years, financial technologies (fintech) have led to a dramatic expansion in the number of non-traditional credit intermediaries, but the macroeconomic and credit-market implications of this rapid growth of fintech are not known. We build a model with a traditional banking system and endogenous fintech intermediary creation and find that greater fintech entry delivers positive long-term effects on aggregate output and consumption. However, greater entry bolsters aggregate firm financial inclusion only if it stems from lower barriers to accessing fintech credit by smaller, unbanked firms. Decreasing entry costs for fintech intermediaries alone has only marginal effects in the aggregate. While firms that adopt fintech credit are less sensitive to domestic financial shocks and contribute to a reduction in output volatility, greater fintech entry also leads to greater volatility in bank credit, thereby introducing a tradeoff between output volatility and credit-market volatility.

The Impacts of Credit Standards on Aggregate Fluctuations in a Small Open Economy: The Role of Monetary Policy

Empirical evidence demonstrates that credit standards, including lending margins and collateral requirements, move in a countercyclical direction. In this study, we construct a small open economy model with financial frictions to generate the countercyclical movement in credit standards. Our analysis demonstrates that countercyclical fluctuations in credit standards work as an amplifier of shocks to the economy. In particular, the existence of endogenous credit standards increases output volatility by 21%. We also suggest three alternative tools for policymakers to dampen the effects of endogenous credit standards on macroeconomic volatility. First, the introduction of credit growth to the monetary policy succeeds in counteracting the fluctuation of lending, and thus decreasing the additional volatility considerably. Second, the exchange rate augmented monetary policy, if well-constructed, is considered an efficient tool to eliminate most of the additional fluctuations caused by deep habits in the banking sector. Finally, the introduction of the foreign interest augmented policy also proves successful in dampening the effect of endogenous movements in lending standards.

Foreign Direct Investment and Output Volatility Nexus: A Global Analysis

Volatility in output growth remains a genuine concern around the globe because of its detrimental effects on growth, poverty and welfare. In the realm of output volatility, the role of FDI and its consistency is particularly important and worth considering. This article examines the role of FDI inflows and specifically the instability in it on output growth volatility using a panel dataset of 141 world economies for the period 1971–2017. The study employs a variety of estimation techniques like pooled ordinary least squares (POLS), LS fixed effects (FE), LS random effects (RE), two stage least squares (2SLS) and generalised methods of moments (GMM). Findings of the study suggest that FDI acts as the volatility reducing factor, whereas uncertainty in it increases output volatility. On the policy front, this study recommends policies that not only encourage FDI inflows but also ensure the inflows to be more consistent and stable. Our results are robust corresponding to various above-mentioned estimation techniques and sensitivity analysis. JEL Codes: C23, E32, F21

Business Cycles Analysis in Cameroon an overview

This paper aims at conducting a thorough analysis of business cycles in Cameroon by statistically assessing their main characteristics. The analysis is carried out by considering the three dimensions of macroeconomic fluctuations. By assessing output volatility, light on the sensitivity of the economy to exogenous shocks as well as to endogenous sources of instability is shed. Likewise, analysing the co-movements of aggregate variables of interest helps in understanding the extent to which the observed fluctuations relate to other aggregates in the economy and hence, the main forces driving the dynamics of this economy. Eventually, more light could be shed on macroeconomic dynamics by analysing the timing and persistence of business cycles. Overall, such analysis is conducted using basic statistical tools commonly used in the empirical literature on business cycles. These are the standard deviation as a measure of volatility, cross-correlations as a means of analysing co-movements and auto-correlations as measures of persistence. The main limitation in this study is the linear consideration of observed data. In fact, many macroeconomic and financial time-series that are used in quantitative macroeconomic models are subject to a number of regime-switching in reality. This fact needs to be taken into account in the subsequent research

Volatility Risk Pass-Through

We develop a novel measure of volatility pass-through to assess international propagation of output volatility shocks to macroeconomic aggregates, equity prices, and currencies. An increase in country’s output volatility is associated with a decrease in its output, consumption, and net exports. The average consumption pass-through is 50% (a 1% increase in output volatility increases consumption volatility by 0.5%) and it increases to 70% for shocks originating in smaller countries. The equity volatility pass-through is larger and in the order of 90%. A novel channel of risk sharing of volatility risks can explain our empirical findings.

Quantitative Evaluation Methods of Cluster Wind Power Output Volatility and Source-Load Timing Matching in Regional Power Grid

The quantitative evaluation of cluster wind power output volatility and source-load timing matching is vital to the planning and operation of the future power system dominated by new energy. However, the existing volatility evaluation methods of cluster wind power output do not fully consider timing volatility, or are not suitable for small sample data scenarios. Meanwhile, the existing source-load timing matching evaluation indicator ignores the impact of wind power permeability on the timing matching degree between wind power output and load. Therefore, the authors propose quantitative evaluation methods of cluster wind power output volatility and source-load timing matching in regional power grid. Firstly, the volatility-based smoothing coefficient is defined to quantitatively evaluate the smoothing effect of wind-farm cluster power output. Then, the source-load timing matching coefficient considering wind power permeability is proposed to quantitatively evaluate the timing matching degree of regional wind power output and load, and the corresponding function model of volatility-based smoothing coefficient and source-load timing matching coefficient is established. Finally, the validity and applicability of the proposed methods are verified by MATLAB software based on the actual power output of 10 wind farms and actual grid load in a certain grid dispatching cross-section of northeast China. The results demonstrated that the proposed volatility-based smoothing coefficient can accurately represent the smoothing effect of wind farm cluster power output while maintaining the volatility continuity of wind power output time series and without affect from the data sample size. The source-load timing matching coefficient can accurately characterize the difference in the timing matching degree between wind power output and grid load under different wind power permeability and the influence degree on grid load.