Interbank Frictions, Business Cycle Fluctuations and Monetary Policy Trade-offs

Download or read book Interbank Frictions, Business Cycle Fluctuations and Monetary Policy Trade-offs written by Yujung Suh and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The Great Recession and consequent slow overall economic recovery have reignited research interests in financial factors in business cycle fluctuations and monetary policy rules. In the recent financial crisis, highly interconnected and leveraged financial institutions through wholesale financial markets have been blamed as the main culprit in exacerbating the initial shock in the financial market. The role of market-based financial institutions in the supply of credit has been expanded in the United States especially before the Great Recession. The market-based institutions have used securitization as one of their important tools to raise funds. Market-based lending and the related securitization process are a part of the shadow banking system, which is broadly defined as ''credit intermediation involving entities and activities (fully or partially) outside the regular banking system'' by the Financial Stability Board. My dissertation investigates the importance of financial factors in the business cycle fluctuations and monetary policy rules. The first chapter explores the role of interbank friction shocks in accounting for the business cycle fluctuations. I augment financial intermediation of Gertler and Kiyotaki (2011) with an otherwise standard New Keynesian DSGE model with nominal rigidities in wages and prices. I then fit the model to US data, using two new financial variables (interbank loans and the net worth of banks) in the literature and allowing the interbank frictions to vary over time following exogenous shocks to these frictions. According to the Bayesian estimation of the model, shocks to interbank frictions are important factors in explaining the fluctuations of the economy, accounting for 7% of the output fluctuations, 11% of the investment fluctuations, 53% of the fluctuations in the premium and 8% of the fluctuations in interbank loans. Analyses of historical decompositions show that interbank frictions shock plays an important role in the movement of key macro variables early in the downturn of the recent financial crisis. In the second chapter, I evaluate the effect of financial frictions and shocks on monetary policy. The identification method of Justiniano, Primiceri and Tambalotti (2011) is adopted to tackle the criticism on the identification between labor supply shocks and wage markup shocks by Chari, Kehoe and McGrattan (2009). The model is re-estimated with more data series on nominal wage inflation and the output gap, defined as the difference between the actual output and potential out, is derived. The output gap is estimated to be large and displays low-frequency movements under Taylor-rule type monetary policy. Estimated shocks including interbank friction shocks then are fed into the model under Ramsey optimal monetary policy to evaluate the impact of financial shocks on the evolution of the economy and the counterfactual simulation on the evolution of the economy is conducted. The counterfactuals show that, unlike the volatile movements of output gap under Taylor-rule type monetary policy, the output gap is more stabilized and the trade-offs between conflicting policy objectives are moderate if monetary policy is conducted optimally. The last chapter explores the possibility of regime shifts in the financial frictions and the volatility of shocks to financial frictions. The preliminary estimation results on the regime-switching DSGE model show that the regime has switched between low friction and high friction regimes. At this sage, the results are mostly preliminary since the numerical optimization may stop at a local but not global maximum of the posterior distribution due to the possibility that the objective function is flat or multimodal with shifts in regimes. More through results will be obtained by gradient-free global optimization methods. The artificial bee colony (ABC) algorithm and the differential evolution algorithm are now employed to estimate the regime-switching DSGE model in the last chapter.