Abstract

     This paper studies the US and global economic fundamentals that exacerbate emerging stock markets volatility and can be considered as systemic risk factors increasing financial stability vulnerabilities. We apply the bivariate HEAVY system of daily and intra-daily volatility equations enriched with powers, leverage, and macro-effects that improve its forecasting accuracy significantly. Our macro-augmented asymmetric power HEAVY model estimates the inflammatory effect of US uncertainty and infectious disease news impact on equities alongside global credit and commodity factors on emerging stock index realized volatility. Our study further demonstrates the power of the economic uncertainty channel, showing that higher US policy uncertainty levels increase the leverage effects and the impact from the common macro-financial proxies on emerging markets’ financial volatility. Lastly, we provide evidence on the crucial role of both financial and health crisis events (the 2008 global financial turmoil and the recent Covid-19 pandemic) in raising markets’ turbulence and amplifying the volatility macro-drivers impact, as well.

Introduction

     A common stylized fact about emerging economies is the high volatility of their stock markets (De Santis 1997; Aggarwal et al. 1999; Xu 1999; Cano-Berlanga and Giménez-Gómez 2018). The liberalization of the emerging world’s financial markets, which attracted a significant amount of capital flows by foreign institutional investors, has been the first step of an integration process with significant economies’ interdependence and asset markets’ synchronization. Given that emerging economies are characterized by critical vulnerabilities to external shocks, they exhibit higher equity market fluctuations than the developed markets and it is worth investigating how US and global common economic forces affect their intra-daily volatility. In general, modeling the volatility of financial returns has crucial implications for asset allocation, risk management practices, and financial stability oversight. Robust modeling and reliable forecasting of the volatility trajectory of financial instruments has been the main task and objective of financial economics applications for business operations, given that volatility constitutes one of the fundamental input variables in estimations and decision processes of any corporation on investing and funding choices. Financial volatility is also closely inspected by policymakers since it is perceived to constitute an early warning crisis signal, entailing critical destabilizing threats for the financial system (see, for example, Kürüm et al. 2018).

Conclusions

     Our study has examined the HEAVY model and its extension with leverage, power transformations, and macro-characteristics. For the realized measure, our empirical results favor the macro-augmented cross asymmetric power specification, where the lags of both powered variables—squared negative returns, and realized variance—move the dynamics of the power transformed conditional variance of the latter. Similarly, modeling the returns with a double asymmetric power process, we found that not only the powered realized measure asymmetry but the power transformed squared negative returns, as well, help to forecast the conditional variance of the latter. The macro-augmentation of the asymmetric power model ensures the superiority of our contribution, which can be implemented in several investment and risk management practices. We further demonstrated the forecasting dominance of the extended specifications over the benchmark HEAVY and standard volatility models through the out-of-sample forecasting across multiple short- and long-term horizons.

     Moreover, we demarcate our study from previous literature by estimating the significant US uncertainty effect on the power of leverage (Heavy and Arch), and the macro-determinants of emerging markets realized variance. The US-led uncertainty spillovers shed light on new evidence for volatility modeling and macro-financial linkages literature. Our findings’ novelty is twofold: Given higher (lower) daily US policy uncertainty levels, mostly associated with economic downturns (upturns), (i) heavy and leverage effects become more (less) acute in realized variance modeling, and (ii) US EPU volatility, financial uncertainty, credit conditions, commodity market benchmarks, and disease news impact on emerging financial volatility increases (decreases). Similarly, financial and health crisis events magnify further the Heavy, Arch, and Macro parameters of the bivariate system and the EPU indirect impact on the volatility drivers, as well.

Abstract

     This paper explores the effectiveness of predictors, including nine economic policy uncertainty indicators, four market sentiment indicators and two financial stress indices, in predicting the realized volatility of the S&P 500 index. We employ the MIDAS-RV framework and construct the MIDAS-LASSO model and its regime switching extension (namely, MS-MIDAS-LASSO). First, among all considered predictors, the economic policy uncertainty indices (especially the equity market volatility index) and the CBOE volatility index are the most noteworthy predictors. Although the CBOE volatility index has the best predictive ability for stock market volatility, its predictive ability has weakened during the COVID-19 epidemic, and the equity market volatility index is best during this period. Second, the MS-MIDAS-LASSO model has the best predictive performance compared to other competing models. The superior forecasting performance of this model is robust, even when distinguishing between high- and low-volatility periods. Finally, the prediction accuracy of the MS-MIDAS-LASSO model even outperforms the traditional LASSO strategy and its regime switching extension. Furthermore, the superior predictive performance of this model has not changed with the outbreak of the COVID-19 epidemic.

Introduction

     Financial market volatility is not only a key factor in assessing the risk of financial assets (see, e.g., Wang et al., 2015; Bee et al., 2016; Clements & Liao, 2017; Li & Wei, 2018; Ji et al., 2021) but also an important parameter in the pricing of financial derivatives and asset allocation (Graham & Harvey, 1996; Zhu & Ling, 2015), as well as an important factor in portfolio management (Ayub et al., 2015; Cederburg et al., 2020). Therefore, accurately predicting the volatility of the stock market is particularly important for investors when deciding on the sizes and timing of their investments.

     Despite the many benefits of accurately predicting stock market volatility, improving predictive accuracy has proven challenging. First, the diversity of potential predictors poses a great challenge to the selection of predictors. For example, studies have determined the predictive abilities of macroeconomic and financial variables for stock market volatility (Nonejad, 2017; Paye, 2012). In recent years, with the introduction of economic policy uncertainty indices, some studies have also determined the predictive power of some economic policy uncertainty indicators (e.g., the economic policy uncertainty index, trade policy uncertainty index, monetary policy uncertainty index, equity market volatility index and geopolitical risk index) relative to stock market volatility (Alqahtani et al., 2020a; Gupta & Wohar, 2019; Li et al., 2020a; Olasehinde-Williams, 2021; Paye, 2012; Yu et al., 2018). In addition, studies have found evidence that the stock market can be driven by investors’ psychology (Daniel et al., 2002; Tseng, 2006) and determined the predictive power of some market sentiment indicators (see, e.g., Gupta et al., 2014; Perez-Liston et al., 2014; Oliveira et al., 2017; Jin et al., 2020; Liang et al., 2020a; Wang et al., 2020a). Other indicators, such as financial stress indices, have also been proven to have potential forecasting ability (Gupta et al., 2014; Singh, 2016; Sum, 2014). The predictive abilities of these potential predictors tend to change with changes in various external factors, making it more difficult to find stable predictors.

Conclusions

     In this paper, we explore the effectiveness of 9 economic policy uncertainty indices, 4 market sentiment indicators and 2 financial stress indices in predicting the realized volatility of the S&P 500 index. We use several MIDAS-RV-X models to determine the predictive power of individual predictors and further construct PCA-, PLS- and LASSO-based MIDAS-RV extensions, a MIDAS-LASSO model with regime switching and various combination forecasts to find the optimal forecasting strategy. The out-of-sample forecasting performance of our forecasting models of interest is evaluated mainly by the MCS test and out-of-sample R2 statistics.

     The empirical results first show that the economic policy uncertainty indicators (especially the EMV) and the VIX are more likely to produce better forecasting accuracy than the three market sentiment indicators and two financial stress indices. Among these predictors, the VIX has the strongest predictive power, followed by the EMV. Second, the combination forecasts and MIDAS-LASSO models usually have statistically significant relative forecasting performance. Across all forecasting models of interest, the MIDAS-LASSO model with regime switching has the best forecasting performance, followed by the MIDAS-LASSO model, indicating that LASSO technology can capture more valuable information from a large set of predictors than other information integration methods. The forecasting accuracy of MIDAS-LASSO with regime switching is also superior to that of the traditional HAR-LASSO method and its regime switching extension. When distinguishing between high- and low-volatility regimes, we find that the VIX is the best predictor under both scenarios. In addition, the economic policy uncertainty indicators other than GEPU either help predict high stock market volatility or low stock market volatility, but the market sentiment indices other than the VIX and the financial stress index (OFRFSI) exhibit statistically significant relative predictive performance only during low-volatility regimes. Across all considered forecasting methods, the MS-MIDAS-LASSO model still exhibits the best predictive performance during both high- and low-volatility regimes, but the MIDAS-LASSO model can outperform other forecasting methods except for MS-MIDAS-LASSO only during high-volatility regimes. We finally consider the impact of the COVID-19 epidemic. During the COVID-19 epidemic, the predictive power of the VIX diminished, and it was no longer the best out-of-sample predictor, while the predictive power of the EMV increased, making it the best predictor during this special period. However, the MIDAS-LASSO model with regime switching still exhibited the best forecasting performance across all considered forecasting models during the COVID-19 epidemic.

Abstract

     Using daily credit default swap (CDS) data, we find a positive relation between corporate credit risk and unexpected monetary policy shocks during FOMC announcement days. Positive shocks to interest rates increase the expected loss component of CDS spreads as well as a risk premium component. However, not all firms respond in the same manner. We show that firm-level credit risk is an important driver of the monetary policy response, both in credit and equity markets, and its role is not diminished by the inclusion of other risk proxies. A stylized corporate model of monetary policy, investment, and financing rationalizes our findings.

Introduction

     Understanding the transmission of interest rate shocks to corporations is of paramount importance to policymakers and economic researchers, as corporate borrowing is widely used to fund investment, production, labor, and other real activities. While economic theory suggests that firms obtain credit at a premium that compensates for default risk, it is still widely debated as to how corporate credit risk is affected by monetary policy. This transmission mechanism has played a crucial role in policy responses over the last two decades.

     In this paper, we connect unexpected interest rate movements to fluctuations in credit risk to shed more light on this transmission mechanism. To measure credit risk at the firm-level, we take advantage of daily quoted prices on the corporate, single name, credit default swap (CDS) market. Using CDS rates allows us to conveniently separate the two main components of credit spreads, an expected loss component and a credit risk premium, and independently study their reaction to monetary policy surprises.2

Conclusion

     Monetary policy surprises significantly affect firm-level credit risk. Consistent with recent evidence, we document that both components of firm-level credit risk (i.e., the expected loss and the risk premium components) and equity prices respond more to monetary policy surprises when firms have higher credit risk, as measured by their CDS spreads. Additionally, the importance of firm-level credit risk for the propagation of monetary policy shocks does not diminish when we contemporaneously control for book leverage and market size, two characteristics commonly associated with firm-level risk.

     To rationalize the heterogenous response of credit risk and the noisiness of leverage as a risk indicator, we construct a stylized three-period model that features endogenous financing and investment decisions together with a monetary authority that sets short term interest rates. Central to our results is an intermediary pricing kernel for which larger prices of risk related to interest rate shocks correspond to larger responses of credit spreads and real quantities. We show in the model economy that firms with higher ex-ante spreads, that are closer to default, are those that are more sensitive to interest rate shocks. Meanwhile, firms with higher leverage are able to afford such debt capacity due to their fundamental strength and, as a result, their credit spreads are less sensitive to monetary policy shocks.

     Overall, our work suggests that a simple and intuitive measure such as leverage might not be as effective as a more direct measure of credit risk such as CDS spreads, to understand the pass through of monetary policy at the firm level.

Abstract

     This study examines the impact of the economic policy uncertainty index (EPU) and macroeconomic variables on the volatility of the Pakistan stock market using the GARCH-MIDAS (mixed data sampling) model. The model allows us to observe whether those variables contain valuable information to forecast stock market volatility. Our empirical findings show several outcomes. First, our out-of-sample results show economic policy uncertainty index has predictive power to forecast Pakistan stock market volatility. Second, among all variables, oil prices are the most powerful predictor of volatility with a higher out of sample R square value. Third, all macroeconomic variables including exchange rate, short-term interest rate, money supply M2, foreign direct investment, gold prices, inward remittances, industrial production, and consumer price index (proxy for inflation) contain useful information for stock market volatility forecasting. However, the long-run interest rate is an ineffective indicator of volatility during the sample period study. Finally, we find that the combination forecast information is also useful for volatility forecasting.

Introduction

     Volatility in financial markets is an issue of great concern. As a risk measure, volatility considers an important state variable for asset pricing and investment. Additionally, the volatility dynamics are associated with portfolio allocation, risk management, hedging, and options pricing (Christoffersen & Diebold, 2000; Chun et al., 2020; Mei et al., 2017; Morales and Callaghan, 2011; Naeem et al., 2019; Wang and Nishiyama, 2015; Zhang et al., 2020). The higher volatility creates widespread panic and results in disorderly market situations that reduce investors’ confidence and that lead to declines in business investments and economic growth. Therefore, understanding the mechanism of economic dynamics and accurately estimating future volatility is essential.

     However, it is prudently observed by financial analysts, policymakers, and market practitioners. Nonetheless, before estimation, it is hard to find the main economic drivers of volatility. The seminal work of SCHWERT and WILLIAM (1989), who posits a time-varying link between stock market uncertainty and macroeconomic variables. Moreover, Paye (2012) examines the strong linear connection between macroeconomic indicators and stock market volatility. Both theoretical and empirical studies confirm that institutional and macroeconomic determinants are potential drivers of financial markets volatility (Engle et al., 2013; Bahloul et al., 2017; Fang et al., 2018; Hsu et al., 2019). The literature shows that financial market volatility and macroeconomic indicators are intrinsically associated (Chen et al., 1986; Mele, 2007; Christiansen et al., 2012).

Conclusion

     This research examines the forecasting ability of macroeconomic variables and economic policy uncertainty index (EPU) for Pakistan stock market volatility. The macroeconomic variables include exchange rate, long-run interest rate, money supply (M2), short-term interest rate, remittances, consumer prices index (proxy for inflation), foreign direct investment, industrial production, global oil prices, and gold prices. We use the GARCH-MIDAS model for forecasting short-and long-run volatility. In our empirical analysis, we find some notable results. First, we find economic policy uncertainty index information is useful for Pakistan stock market volatility prediction. Second, we find global indicators oil prices are more powerful predictor of Pakistan market volatility. Moreover, all macroeconomic variables are effective indicators of Pakistan stock market volatility except long run interest rate. In addition, the combination information for all macroeconomic variables is also useful for forecasting volatility. These findings are in line with the literature (Asgharian et al., 2013; Liu & Pan, 2020). However, the abovementioned studies are not directly involved in forecasting Pakistan’s stock market volatility. In addition, we did not find consistent findings in a longer period, such as two months and three months. Overall, results indicate that macroeconomic variables are significant predictors of stock market volatility in Pakistan. Therefore, the government and policymakers should consider these factors in policymaking and stock market volatility estimation.

Abstract

     In today’s Chinese economic system, market system, and development goals, MSMEs play an essential role. The availability of capital is a necessary condition for the development of MSMEs. However, in China, MSMEs still cannot effectively eliminate the development dilemma that financing is difficult and expensive. The deep-seated reason lies in the imperfect credit evaluation system of the banking industry for MSMEs. The purpose of this paper is to establish a mathematical model for credit risk evaluation of MSMEs and to establish a new bank credit evaluation strategy to support and back up MSMEs of different sizes accurately.

Introduction

     As economic globalization develops, Chinese MSMEs have seen rapid development. However, with the further development of MSMEs, the shortage of capital often becomes a significant factor limiting the further development of MSMEs. Due to the limitation of the size of MSMEs, their risk tolerance is weak, leading to further increase of enterprise credit risk and the difficulty of credit assessment by commercial banks. The purpose of this paper is to establish a mathematical model for assessing the credit risk of MSMEs and to establish new bank credit strategies to support and back up MSMEs of different sizes accurately. In this paper, an evaluation model is established by combining the machine learning model XGBoost algorithm with AHP, and the combined model is used to construct and analyze the model to quantify further and evaluate the credit risks of MSMEs. While ensuring the interests of commercial banks, more MSMEs are provided with loans to increase the amount of loanable capital of the whole society. Promote the further development of MSMEs and promote social progress.

Model Evaluation and Conclusions

     This paper establishes a reasonable mathematical model and formulates the bank’s credit strategy by evaluating and ranking the credit risk. We first use the analytic hierarchy process to determine the factors affecting the credit risk and then use the comparative method to quantify the importance of each factor. After the consistency test is passed, an acceptable pairwise comparison matrix is constructed. MATLAB software calculates the proportion of N factors in this layer in target Z. The proportion in the criterion layer is written into a vector and normalized to obtain the weight vector.

     Then, this paper uses SPSS software to quantify the stability of supply and demand in the criterion layer and then quantify the relationship between customer churn rate and reputation level given in the data to calculate the average value of the three levels of ABC. After data preparation, this paper classifies the eigenvalues for similar machine learning. We use Mpai software to substitute effective indicators into the machine learning model XGBOOST regression for training. In this paper, xgboost machine learning is used to substitute the normalized weight vector in the analytic hierarchy process to obtain the quantitative regression value of the company’s strength. Finally, the credit risk rating evaluation formula is constructed using the criteria layer weight and the lower level quantitative data. The final enterprise credit risk ranking is obtained using Mpai software, and the credit strategy is given scientifically and reasonably according to the ranking.

Abstract

     Because cash flow is a critical issue for companies, it is important to effectively operate cash flow to mitigate liquidity risks. However, compared with research on the bullwhip effect, few studies have analyzed the effects and causes of the cash-flow bullwhip in the supply chain. None has considered the influence of credit risk on the cash-flow bullwhip effect from downstream to upstream throughout the supply chain. Thus, this study develops a mathematical model to investigate the influence of credit risk on the cash-flow bullwhip. To achieve this, it analyzes the variability of each member’s account receivable, account payable, and cash level along with three financial performance measures: account receivable turnover, account payable turnover, and cash conversion cycle. The excessive inventory level created by the bullwhip effect is known to cause the cash-bullwhip effect, which leads to supply chain members experiencing liquidity problems. However, the results of this study demonstrate that a consideration of credit risk increases the amounts of account receivable, account payable, and cash from downstream members to upstream members. In addition, this study demonstrates that when considering the credit risk, the account receivable turnover index accurately illustrates the cash-bullwhip effect of each member throughout the supply chain.

Introduction

     Supply chain management controls material, information, and cash flows to efficiently convert raw materials from upstream into products to downstream through the supply chain. Because supply chain management is considered essential for companies wishing to achieve a sustainable business operation, the management of material, information, and cash is deemed important in the contemporary business environment. To improve the efficiency of the supply chain, most companies have strived to identify and solve the problems occurring from the flows of material, information, and cash through the entire supply chain. However, compared with the efforts companies expend on the management of material and information flows, there has been little focus on the cash flow of the supply chain as a means of improving efficiency.

     One of the critical problems in the supply chain is a bullwhip effect, which refers to an increasing inventory fluctuation moving from downstream members to upstream members in response to distorted information on customer demand. Distorted demand results in an inefficient production and excess inventory which exacerbates the liquidity problems of supply chain members. In other words, the bullwhip effect further leads to a cash-bullwhip effect, which is defined as an increase in the variance of a cash conversion cycle from downstream to upstream through the supply chain that corresponds to inventory fluctuation (Tangsucheeva & Prabhu, 2013). Like the bullwhip effect, the cash-bullwhip effect is important for companies wishing to achieve sustainable business operations through the efficient and effective management of production and inventory. To mitigate the effects of the cash-bullwhip effect, a prerequisite is to identify its causes and impacts on all members of the supply chain.

Conclusion

     Although the bullwhip effect and the credit risk are critical issues in material flow and cash flow throughout the supply chain, no studies have considered these two important issues simultaneously. For these reasons, this study investigated the impact of credit risk on the cash-flow bullwhip effect from downstream members to upstream members along the supply chain in terms of account receivable, account payable, and cash. To analyze the cash-bullwhip effect, three financial performance indicators were used— account receivable turnover, account payable turnover, and cash conversion cycle.

     Having applied the developed cash-bullwhip effect mathematical model, the results demonstrate that credit risk consideration improves the liquidity problems of each supply chain member by increasing the amounts of account receivable, account payable, and cash from downstream to upstream. The results also indicate that the account receivable turnover accurately indicates the cash-bullwhip effect of each member. However, the account payable turnover and the cash conversion cycle do not accurately illustrate the cash-bullwhip effect phenomenon in the case of credit risk consideration.

Abstract

JEL classification

Keywords

1. Introduction

2. Theoretical analysis and hypotheses

3. Data and model

4. Empirical results and analysis

5. Further analysis

6. Conclusions

Declaration of Competing Interest

References

Abstract

Based on a macro and micro cross view, we empirically test the interactions among monetary policy, accounting information comparability, and investment efficiency with a sample of A-share listed firms in China from 2005 to 2019. We find that tightening monetary policy will aggravate underinvestment behavior, such as inefficient investment, while improvement in accounting information comparability can effectively mitigate the negative impact of tightening monetary policy on underinvestment, so as to improve firm investment efficiency. Further study shows that, in a period of monetary policy tightening, firms with high agency costs and high financing constraints can significantly reduce underinvestment by improving the comparability of accounting information, thus improving the investment efficiency of firms. After distinguishing the type of firm ownership (state-owned enterprises vs. private enterprises), we find that firms can reduce the effect of insufficient investment during a period of monetary tightening by improving the comparability of accounting information, which is more significant in the sample of private enterprises (PEs).

1. Introduction

Using a series of strict assumptions, such as complete information, no transaction cost, and zero-risk debt financing, some scholars have confirmed that a company’s internal financing and external financing are equivalent, the choice of financing mode does not affect the company’s investment behavior, and purely technical factors – such as future profitability and the capital cost of an investment project – are the only influencing factors that determine the value of a company and can maximize its value when it reaches an optimal investment level. However, this series of assumptions is difficult to achieve perfectly in reality. Trade friction caused by information asymmetry tends to cause a company’s actual value to deviate from the theoretical value in varying degrees in the capital market, which leads to inefficient investment. The inefficient investment includes underinvestment (projects with a positive net present value [NPV] are rejected) and overinvestment (projects with a negative NPV are carried out). This inefficient investment not only reduces the value of a firm at the micro level and hinders the development of the firm but also affects the efficiency of resource allocation in the entire economy at the macro level (Bian and Li, 2009), thus curbing sustainable and healthy development of the macro economy. Fig. 1 illustrates changes in the pattern of inefficient investment in China, in which the sample of listed firms with inefficient investment increased from 1,038 in 2005 to 3,159 in 2019, a rate of 204.335%, and the sample of listed firms with underinvestment was larger than that with overinvestment during the sample period, which indicates that underinvestment is more common than overinvestment. Therefore, it is of great theoretical research value to explore the reasons for low investment efficiency at firms and to reveal which factors can reverse underinvestment by firms and raise investment efficiency at listed firms.

1- Introduction

2- Households’ inflation expectations in the US

3- Empirical model

4- Results

5- Conclusion

References

Abstract

Well-anchored inflation expectations play an important role in the achievement of price stability. The (de-)anchoring of long-term inflation expectations in the US has been under debate since the sub-prime crisis. This paper assesses and explains the evolution of the degree of (de-)anchorage of households’ long-term inflation expectations in the US during the period of 1990 to 2019, in a time-varying framework. We find the long-term inflation expectations to be de-anchored during the entire study period. The de-anchorage was greater in the first half of the 1990s. Subsequently, it has declined but has not yet anchored. An increase in inflation perception reduces the degree of de-anchorage in a (persistently) low-inflation perception period, whereas it causes a rise in the degree of de-anchorage when inflation perception is around its long-term average or is persistently high. Further, a rise in economic policy uncertainty also increases the de-anchorage of households’ long-term inflation expectations. This suggests that the Federal Reserve System (Fed) may find it beneficial to pay more attention to households’ inflation perception.

Introduction

Well-anchored long-term inflation expectations are very important for the successful conduct of monetary policy in an inflationtargeting economy. Central banks often adopt an explicit numeral inflation target, partly to anchor long-term inflation expectations. If the central bank is credible, the target inflation should anchor the long-term inflation expectations. Accordingly, the long-term inflation expectations are expected to be determined only by target inflation, not by actual inflation, news, and/or short-term inflation expectations. The (de-)anchoring of long-term inflation expectations in the US, the Euro area, and the emerging economies has been under debate since the sub-prime crisis. The empirical results are mixed, ranging from perfect anchoring to severe de-anchoring. For instance, in the context of the US, several studies have found the long-term inflation expectations to be perfectly anchored (Blanchard, 2016; Buono and Formai, 2018; Nautz et al., 2019; Strohsal et al., 2016), whereas a few studies have shown them to be de-anchored (Kumar et al., 2015; Nautz and Strohsal, 2015). Similarly, in the context of the Euro area, Scharnagl and Stapf (2015) have found that the medium- to long-term inflation expectations are well anchored, whereas Cruijsen and Demertzis (2011) have determined that inflation expectations are less well-anchored. Further, de Mendonça (2018) has shown that the inflation expectations in seven inflation-targeting emerging economies (Brazil, Chile, Colombia, Mexico, Poland, South Africa, and Turkey) are not perfectly anchored.

1- Introduction

2- The link between inflation targeting and natural disasters

3- Empirical strategy and data

4- Inflation targeting and macroeconomic performance

5- Economic mechanisms

6- Sensitivity analysis

7- Conclusions

References

Abstract

We study the characteristics of inflation targeting as a shock absorber, using quarterly data for a large panel of countries. To overcome an endogeneity problem between monetary regimes and the likelihood of crises, we propose to study large natural disasters. We find that inflation targeting improves macroeconomic performance following such exogenous shocks. It lowers inflation, raises output growth, and reduces inflation variability compared to alternative monetary regimes. This performance is mostly due to a different response of monetary policy and fiscal policy under inflation targeting. Finally, we show that only hard, but not soft, targeting reaps the rewards: deeds, not words, matter for successful monetary stabilization.

Introduction

Inflation targeting has become a dominant framework for monetary policy since the 1980s. It is praised not just for its success in bringing down inflation, but also increasing the credibility and accountability of policymakers (Bernanke and Mishkin, 1997; Ball, 2010). Its popularity is reflected in an increasing number of countries adopting inflation targeting (IT). However, the global financial crisis dramatically changed the perception of IT as an optimal framework for achieving macroeconomic stability, especially when the economy is confronting large real or financial shocks. It is argued that IT, by focusing narrowly on inflation, may contribute to a build-up of financial instability (Taylor, 2007; Frankel, 2012), leading central banks to neglect other objectives, such as employment (Stiglitz, 2008), and constraining those monetary authorities dealing with deep recessions (Borio, 2014). As a result, scholars and policymakers call for refining the IT framework to allow for more flexibility (Svensson, 2009).

We develop a dynamic stochastic general equilibrium model to examine how monetary policy shocks affect income inequality and the equity premium. The model features Ricardian and non-Ricardian households and shows that a monetary policy tightening causes an endogenous redistribution of income from non-Ricardians to Ricardians. Ricardians’ consumption comoves more strongly with asset returns, giving rise to high equity premia. We extend our model with several frictions and estimate it with generalized method of moments using US macroeconomic and financial data from 1960 to 2007. We find that the estimated model jointly matches the bond and equity premia. We complement our theoretical model with vector autoregression estimations and show that a tightening of US monetary policy increases equity premia.

Introduction

Following the recent financial crisis, there is a renewed interest in exploring the interactions between monetary policy and income inequality (Coibion et al., 2017; Davtyan, 2017). Traditionally, it has been assumed that the distributional effects of the monetary policy rate net out over the business cycle, and therefore, the interactions between monetary policy and inequality have rarely been examined. However, some recent papers show that monetary policy affects income inequality (Coibion et al., 2017) through various channels, such as financial segmentation. It has also been shown that income inequality can become a monetary policy transmission channel itself through earnings heterogeneity (Auclert, 2019). This is because low-income households typically have a higher marginal propensity to consume, and therefore, these households may benefit from monetary expansion more than rich households do. Although the literature examining the effects of monetary policy on income inequality is growing, the implications of income redistribution from a macro-finance perspective have not received sufficient attention in the literature so far. We try to bridge this gap in this paper. Specifically, our paper measures the effect of income redistribution on matching financial and macroeconomic moments jointly in a model with limited asset market participation (LAMP). Importantly, in our paper, income redistribution occurs endogenously, i.e., it is caused by monetary policy shocks. This approach extends the previous literature, which assumes exogenous redistribution shocks (see, e.g., Lansing (2015)). The current literature examining the effects of monetary policy on equity premia has focused on other issues, for example, on the role of market volatility (Mallick et al., 2017). Our first contribution is to show that monetary policy shocks are the source of the high equity risk premia in a simple model with LAMP. Our model is populated by Ricardian and non-Ricardian households. The former have access to bonds to smooth their consumption (‘optimizers’), while the latter do not. This unequal access implies that a limited share of the population participates in financial markets. NonRicardians receive only labor income, while Ricardians hold equity shares in firms.

We examine the European Central Bank’s ad-hoc communication and explore how it informs future monetary policy decisions. Using the rich dataset of the inter-meeting verbal communication by the members of the European Central Bank’s Governing Council between 2008 and 2016, we construct a measure of communication evaluating its inclination towards easing, tightening or maintaining the monetary policy stance. We find that this measure provides useful additional information about future monetary policy decisions, even when we control for market-based interest rate expectations and lagged decisions. Our results also suggest that, in particular, communication related to conventional measures and/or by the ECB President explain the future ECB rate changes well. All aforementioned results hold also in the environment of the zero lower bound. Overall, these results point to the importance of transparent communication in understanding the future course of monetary policy.

Introduction

Central banks currently use a wide set of communication tools to manage financial markets’ expectations, and the use of these tools has become more intensive during the financial crisis (Blinder et al., 2017). Many central banks started operating in ultra-low interest rate environments and complemented their conventional monetary policy with several unconventional measures, such as asset purchases and forward guidance. As a result, the content of central bank communication has broadened, and central banks have started regularly communicating their views about asset purchases, liquidity conditions or interest rate commitments when referring to the stance of monetary policy. In this paper, we examine whether central bank communication contributed to better monetary policy predictability in the (post)-crisis period (2008–2016) using detailed data on the European Central Bank’s (ECB) verbal communication.1 To our knowledge, this is a novel examination because the empirical literature investigating the effect of communication on monetary policy predictability has largely focused on examining how the voting records and minutes from monetary policy meetings are informative about the future course of monetary policy (Gerlach-Kristen, 2004; Horvath et al., 2012; El-Shagi and Jung, 2015; Jung, 2016).

This study investigates the asymmetric effects of unanticipated monetary shocks on stock prices in India over the period 1994M4–2018M11. We find that the evolution of stock prices is state-dependent across different monetary policy processes. Unanticipated monetary shocks appear to have significantly asymmetrically lagged effects on stock prices, namely: (i) the positive effect of negative unanticipated shocks in bull markets; and (ii) the negative effect of positive unanticipated shocks in bear markets. Our findings imply that monetary policy-markers should attend to these situations for the future of money-supply policies to diminish the degree of uncertainty about the money supply in adjusting stock prices.

Introduction

The relationship between monetary policy and the real economy has been well developed in the specialized literature. Lucas (1972), Barro (1978), and Frydman and Rappoport (1987) indicated that if anticipated monetary policy is neutral, only unanticipated monetary shocks are probably to entirely impact real output. Interestingly, the literature argues that the impacts of unanticipated monetary shocks can be asymmetric on real economic activities. This is attributed to the following factors: (i) the business cycle’s different stages (Galí, 2015; Iacoviello, 2005); (ii) the contraction versus the expansion in the conduct of monetary policy (Jiang, 2018; Shiu-Sheng, 2007); and (iii) the different levels of monetary policy effect on real economic activities. In the same vein, regarding financial markets, several studies found that the relationship between the monetary policy process and stock market prices is also asymmetric (Ravn, 2014; Ülke and Berument, 2016). Most studies on the relationship between monetary policy and stock market employed such estimation techniques as event analysis (Val et al., 2018), VAR models (Fausch and Sigonius, 2018; Singh and Nadkarni, 2018), DSGE models (Ravn, 2014), and Markov Switching models (Ivrendi and Guloglu, 2012).