Browsing by Author "Verona, Fabio"

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  • Verona, Fabio; Martins, Manuel M. F.; Drumond, Inês (2013)
    Bank of Finland Research Discussion Papers 4/2013
    Published in International Journal of Central Banking, Volume 9, Number 3, September 2013, Pages 73-117 ; http://www.ijcb.org/journal/ijcb13q3a3.htm
    Motivated by the U.S. events of the 2000s, we address whether a too low for too long interest rate policy may generate a boom-bust cycle. We simulate anticipated and unanticipated monetary policies in state-of-the-art DSGE models and in a model with bond financing via a shadow banking system, in which the bond spread is calibrated for normal and optimistic times. Our results suggest that the U.S. boom-bust was caused by the combination of (i) interest rates that were too low for too long, (ii) excessive optimism and (iii) a failure of agents to anticipate the extent of the abnormally favourable conditions. Keywords: DSGE model, shadow banking system, too low for too long, boom-bust JEL codes: E32, E44, E52, G24
  • Lubik, Thomas A.; Matthes, Christian; Verona, Fabio (2019)
    Bank of Finland Research Discussion Papers 5/2019
    We study the behavior of key macroeconomic variables in the time and frequency domain. For this purpose, we decompose U.S. time series into various frequency components. This allows us to identify a set of stylized facts: GDP growth is largely a high-frequency phenomenon whereby inflation and nominal interest rates are characterized largely by low-frequency components. In contrast, unemployment is a medium-term phenomenon. We use these decompositions jointly in a structural VAR where we identify monetary policy shocks using a sign restriction approach. We find that monetary policy shocks affect these key variables in a broadly similar manner across all frequency bands. Finally, we assess the ability of standard DSGE models to replicate these findings. While the models generally capture low-frequency movements via stochastic trends and business cycle fluctuations through various frictions they fail at capturing the medium-term cycle.
  • Martins, Manuel M. F.; Verona, Fabio (2021)
    Finance Research Letters March
    The typical increase of the corporate bond-to-bank ratio during downturns is known to mitigate business cycle recessions. In the three longest and deepest post-war U.S. recessions this ratio didn't increase from their outsets. In this paper we focus on the timing of the corporate bank-to-bond substitution in the Great Recession, simulating counterfactual paths for output growth under plausible notional behaviors of the bond-to-bank ratio. We find that the Great Recession would have been milder and the recovery much stronger if the bank-to-bond substitution had started since the outset of the recession and evolved thereafter as in most U.S. recessions.
  • Verona, Fabio; Martins, Manuel M. F.; Drumond, Inês (2014)
    Bank of Finland Research Discussion Papers 21/2014
    Published in Journal of Macroeconomics, special issue on “Banking in macroeconomic theory and policy” ; 54 ; Part B ; December ; 1339-1351 ; https://doi.org/10.1016/j.jmacro.2017.04.004
    We assess the performance of optimal Taylor-type interest rate rules, with and without reaction to financial variables, in stabilizing an economy following financial shocks. The analysis is conducted in a DSGE model with loan and bond markets, each featuring financial frictions. This allows for a wide set of financial shocks and transmission mechanisms and can be calibrated to match the bond-to-bank finance ratio featured in the US financial system. Overall, we find that monetary policy that reacts to credit growth, a form of the so-called “leaning against the wind”, improves the ability of the central bank to achieve its mandate in the wake of financial shocks. The specific policy implications depend partly on the origin and the persistence of the financial shock, but overall not on the assignment of a mandate for financial stability in the central bank’s objective function.
  • Verona, Fabio; Martins, Manuel M. F.; Drumond, Inês (2017)
    Journal of Macroeconomics December
    BoF DP 21/2014
    We assess the performance of optimal Taylor-type interest rate rules, with and without reaction to financial variables, in stabilizing an economy following financial shocks. The analysis is conducted in a DSGE model with loan and bond markets, each featuring financial frictions. This allows for a wide set of financial shocks and transmission mechanisms and can be calibrated to match the bond-to-bank finance ratio featured in the US financial system. Overall, we find that monetary policy that reacts to credit growth, a form of the so-called “leaning against the wind”, improves the ability of the central bank to achieve its mandate in the wake of financial shocks. The specific policy implications depend partly on the origin and the persistence of the financial shock, but overall not on the assignment of a mandate for financial stability in the central bank’s objective function.
  • Martins, Manuel M. F.; Verona, Fabio (2020)
    Bank of Finland Research Discussion Papers 4/2020
    We show that the New Keynesian Phillips Curve (NKPC) outperforms standard benchmarks in forecasting U.S. inflation once frequency-domain information is taken into account. We do so by decomposing the time series (of inflation and its predictors) into several frequency bands and forecasting separately each frequency component of inflation. The largest statistically significant forecasting gains are achieved with a model that forecasts the lowest frequency component of inflation (corresponding to cycles longer than 16 years) flexibly using information from all frequency components of the NKPC inflation predictors. Its performance is particularly good in the returning to recovery from the Great Recession.
  • Faria, Gonçalo; Verona, Fabio (2016)
    Bank of Finland Research Discussion Papers 29/2016
    Published in Journal of Empirical Finance, 45, January, 2018, 228–242 https://doi.org/10.1016/j.jempfin.2017.11.009
    We generalize the Ferreira and Santa-Clara (2011) sum-of-the-parts method for forecasting stock market returns. Rather than summing the parts of stock returns, we suggest summing some of the frequency-decomposed parts. The proposed method signi cantly improves upon the original sum-of-the-parts and delivers statistically and economically gains over historical mean forecasts, with monthly out-of-sample R2 of 2.60% and annual utility gains of 558 basis points. The strong performance of this method comes from its ability to isolate the frequencies of the parts with the highest predictive power, and from the fact that the selected frequency-decomposed parts carry complementary information that captures di erent frequencies of stock market returns.
  • Faria, Gonçalo; Verona, Fabio (2018)
    Journal of Empirical Finance January 2018
    Published in Bank of Finland Research Discussion Papers 29/2016.
    We generalize the Ferreira and Santa-Clara (2011) sum-of-the-parts method for forecasting stock market returns. Rather than summing the parts of stock returns, we suggest summing some of the frequency-decomposed parts. The proposed method significantly improves upon the original sum-of-the-parts and delivers statistically and economically gains over historical mean forecasts, with monthly out-of-sample R2 of 2.60% and annual utility gains of 558 basis points. The strong performance of this method comes from its ability to isolate the frequencies of the parts with the highest predictive power, and from the fact that the selected frequency-decomposed parts carry complementary information that captures different frequencies of stock market returns.
  • Faria, Gonçalo; Verona, Fabio (2017)
    Bank of Finland Research Discussion Papers 1/2017
    We show that the out-of-sample forecast of the equity risk premium can be signi ficantly improved by taking into account the frequency-domain relationship between the equity risk premium and several potential predictors. We consider fi fteen predictors from the existing literature, for the out-of-sample forecasting period from January 1990 to December 2014. The best result achieved for individual predictors is a monthly out-of-sample R2 of 2.98 % and utility gains of 549 basis points per year for a mean-variance investor. This performance is improved even further when the individual forecasts from the frequency-decomposed predictors are combined. These results are robust for di fferent subsamples, including the Great Moderation period, the Great Financial Crisis period and, more generically, periods of bad, normal and good economic growth. The strong and robust performance of this method comes from its ability to disentangle the information aggregated in the original time series of each variable, which allows to isolate the frequencies of the predictors with the highest predictive power from the noisy parts.
  • Faria, Gonçalo; Verona, Fabio (2020)
    Bank of Finland Research Discussion Papers 2/2020
    We assess the benefits of using frequency-domain information for active portfolio management. To do so, we forecast the bond risk premium and equity risk premium using a methodology that isolates frequencies (of the predictors) with the highest predictive power. The resulting forecasts are more accurate than those of traditional forecasting methods for both asset classes. When used in the context of active portfolio management, the forecasts based on frequency-domain information lead to better portfolio performances than when using the original time series of the predictors. It produces higher information ratio (0.57 vs 0.45), higher CER gains (1.12% vs 0.81%), and lower maximum drawdown (19.1% vs 19.6%).
  • Martins, Manuel M. F.; Verona, Fabio (2021)
    Bank of Finland Research Discussion Papers 8/2021
    Policymakers and researchers see inflation characterized by cyclical fluctuations driven by changes in resource utilization and temporary shocks, around a trend influenced by inflation expectations. We study the in-sample inflation dynamics and forecast inflation out-of-sample by analyzing a New Keynesian Phillips Curve (NKPC) in the frequency domain. In-sample, while inflation expectations dominate medium-to-long-run cycles, energy prices dominate short cycles and business-to-medium cycles once expectations became anchored. While statistically significant, unemployment is not economically relevant for any cycle. Out-of-sample, forecasts from a low-frequency NKPC significantly outperform several benchmark models. The long-run component of unemployment is key for such remarkable forecasting performance.
  • Verona, Fabio (2013)
    Bank of Finland Research Discussion Papers 18/2013
    Published in Journal of Money, Credit and Banking, Volume 46, Issue 8, December 2014:1627–1656
    Investment in physical capital at the micro level is infrequent and large, or lumpy. The most common explanation for this is that firms face non-convex physical adjustment costs. The model developed in this paper shows that information costs make investment lumpy at the micro level, even in the absence of non-convex adjustment costs. When collecting and processing information is costly, the firm optimally chooses to do it sporadically and to be inactive most of the time. This behavior results in infrequent and possibly large capital adjustments. The model fits plant-level investment rate moments well, and it also matches some higher order moments of aggregate investment rates. Keywords: investment dynamics, information costs, inattentiveness, lumpy investment JEL classification: D21, D83, D92, E22
  • Verona, Fabio (2019)
    Oxford Bulletin of Economics and Statistics 2
    Published in BoF DP 26/2017.
    The investment literature has long acknowledged the time- and frequency-varying dynamics of the relationship between investment, Tobin’s Q and cash flow. In this paper, we use continuous wavelet tools to estimate and assess the relationship between these variables simultaneously at different frequencies and over time. We find that i) Q and cash flow are complementary sources of information for investment, the former being more important for firms’ investment decisions in the medium-to-long run and the latter at business cycles frequencies and ii) investment-Q sensitivity declines over time at all frequencies, while investment-cash flow sensitivity declines at business cycles frequencies but remains largely stable over the medium-to-long run.
  • Verona, Fabio (2013)
    Bank of Finland Research Discussion Papers 16/2013
    In this paper, I introduce lumpy micro-level capital adjustment into a sticky information general equilibrium model. Lumpy adjustment arises because of inattentiveness in capital investment decisions instead of the more common assumption of non-convex adjustment costs. The model features inattentiveness as the only source of stickiness. I find that the model with lumpy investment yields business cycle dynamics which differ substantially from those of an otherwise identical model with frictionless investment and are much more consistent with the empirical evidence. These results therefore strengthen the case in favour of the relevance of microeconomic investment lumpiness for the business cycle. Keywords: sticky information, general equilibrium, lumpy investment, business cycle JEL classification: D83, E10, E22, E32
  • Verona, Fabio (2017)
    Bank of Finland Research Discussion Papers 26/2017
    Pulished in Oxford Bulletin of Economics and Statistics as "Investment, Tobin’s Q, and cash flow across time and frequencies" 2019 ; 82 ; 2 ; 331-346 ; https://doi.org/10.1111/obes.12321
    The empirical performance of the Q theory of investment can be significantly improved by simultaneously considering the time- and the frequency-varying features of the investment-Q relationship. Using continuous wavelet tools, I assess the investment-Q sensitivity at different frequencies and its evolution over time, as well as the interaction of the financial cycle with the Q theory. The results show that there is a positive, stable medium-to-long-run relationship between investment and Q that begins after a positive, stable long-run relationship between credit and Q materializes. In such case, credit leads and slowly fuels the stock price boom.
  • Verona, Fabio; Wolters, Maik H. (2013)
    Bank of Finland Research Discussion Papers 5/2013
    Published in Computational Economics, Volume 43, Issue 3, March 2014, Pages 357-370
    Macroeconomic models with sticky information include an infinite number of lagged expectations. Several authors have developed specialized solutions algorithms to solve these models under rational expectations. We demonstrate that it is also possible to implement this class of models in Dynare - a widely used software package for solving dynamic stochastic general equilibrium (DSGE) models. Using the Dynare macro language one can easily construct and change the required large number of lagged expectation terms. We assess the accuracy of simulations run with different truncation points for the lagged expectations terms and find that the solution is reasonably precise even for moderate truncation points. Keywords: sticky information, Dynare, macro-processor, lagged expectations
  • Kilponen, Juha; Verona, Fabio (2016)
    Bank of Finland Research Discussion Papers 32/2016
    We revisit the empirical performance of the Q theory of investment, explicitly taking into account the frequency dependence of investment, Tobin’s Q, and cash flow. The time series are decomposed into orthogonal components of different frequencies using wavelet multiresolution analysis. We find that the Q theory fits the data much better than might be expected (both in-sample and out-of-sample) when the frequency relationship between the variables is taken into account. Merging the wavelet approach and proxies for Q recently suggested in the investment literature also significantly improves the quality of short-term forecasts.
  • Kilponen, Juha; Orjasniemi, Seppo; Ripatti, Antti; Verona, Fabio (2016)
    Bank of Finland Research Discussion Papers 16/2016
    Revised version of the paper and updated zip file published in April 2020.
    This paper presents Aino 2.0 – the dynamic stochastic general equilibrium (DSGE) model currently used at the Bank of Finland for forecasting and policy analysis. The paper provides a detailed theoretical description of the model, its estimation and how it can be used to interpret the evolution of the Finnish economy between 1995 and 2014, including the rise and fall of the electronics industry, the global financial crisis, and the stagnant growth performance since the end of the financial crisis.
  • Silvo, Aino; Verona, Fabio (2020)
    Bank of Finland Research Discussion Papers 9/2020
    In this paper we present Aino 3.0, the latest vintage of the dynamic stochastic general equilibrium (DSGE) model used at the Bank of Finland for policy analysis. Aino 3.0 is a small-open economy DSGE model at the intersection of the recent literatures on so-called TANK (“Two-Agent New Keynesian”) and MONK (“Mortgages in New Keynesian”) models. It aims at capturing the most relevant macro-financial linkages in the Finnish economy and provides a rich laboratory for the analysis of various macroeconomic and macroprudential policies. We show how the availability of a durable consumption good (housing), on the one hand, and the presence of credit-constrained households, on the other hand, affect the transmission of key macroeconomic and financial shocks. We also illustrate how these new transmission channels affect model dynamics compared to the previous model vintage (the Aino 2.0 model of Kilponen et al., 2016).
  • Faria, Gonçalo; Verona, Fabio (2018)
    Bank of Finland Research Discussion Papers 7/2018
    Published in Journal of Financial Markets as "The yield curve and the stock market: mind the long run" 2020 ; 50 ; September ; https://doi.org/10.1016/j.finmar.2019.100508
    We extract cycles in the term spread (TMS) and study their role for predicting the equity risk premium (ERP) using linear models. The low frequency component of the TMS is a strong and robust out-of-sample ERP predictor. It obtains out-of-sample R-squares (versus the historical mean benchmark) of 1.98% and 22.1% for monthly and annual data, respectively. It forecasts well also during expansions and outperforms several variables that have been proposed as good ERP predictors. Its predictability power comes exclusively from the discount rate channel. Contrarily, the high and business-cycle frequency components of the TMS are poor out-of-sample ERP predictors.