Browsing by Subject "G17"

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  • Kauko, Karlo; Tölö, Eero (2019)
    BoF Economics Review 4/2019
    Published in Applied Economics Quarterly 2019 ; 65 ; 9 http://urn.fi/URN:NBN:fi:bof-202002181134
    Indicators based on the ratio of credit to GDP have been found to be highly useful predictors of banking crises. We study the difference in this ratio as an early warning indicator. We test a large number of different versions of the differenced credit-to-GDP ratio with data on Euro area members. The optimal time interval of the difference is about two years. Using the moving average of GDP instead of the latest annual data has little impact on forecasting performance. The indicator is a particularly promising choice at relatively short forecasting horizons, such as two or three years.
  • Kauko, Karlo; Tölö, Eero (2019)
    Applied Economics Quarterly 4
    Also as BoF Economics Review 4/2019 http://urn.fi/URN:NBN:fi:bof-201906061225
  • Juselius, Mikael; Tarashev, Nikola (2021)
    BoF Economics Review 3/2021
    While corporate credit losses have been low since the start of the Covid-19 pandemic, their future evolution is quite uncertain. Using a forecasting model with a solid track record, we find that the baseline scenario (“expected losses”) is benign up to 2024. This is due to policy support measures that have kept debt service costs low. However, high indebtedness, built up when the pandemic impaired real activity, suggests increased tail risks: plausible deviations from the baseline scenario (“unexpected losses”) feature ballooning corporate insolvencies. Taken at face value, the low expected loss forecasts are consistent with low bank provisions, whereas the high unexpected loss forecasts call for substantial capital.
  • Haavio, Markus; Mendicino, Caterina; Punzi, Maria Teresa (2013)
    Bank of Finland Research Discussion Papers 35/2013
    Published in Applied Economics Letters, Volume 21, Issue 6, April 2014, Pages 407-412 ; https://doi.org/10.1080/13504851.2013.864025
    This article empirically studies the linkages between financial variable downturns and economic recessions. We present evidence that real asset prices tend to lead real cycles, while loan-to-GDP and loan-to-deposit ratios lag them. Using a probit analysis, we document that downturns in real asset prices, particularly real house prices, are useful leading indicators of economic recessions. Keywords: macro-financial linkages; turning point analysis; probit models JEL classification numbers: C53, E32, E37, G17
  • Juselius, Mikael; Tarashev, Nikola (2020)
    Bank of Finland Research Discussion Papers 18/2020
    Extending a standard credit-risk model illustrates that a single factor can drive both expected losses and the extent to which they may be exceeded in extreme scenarios, ie “unexpected losses.” This leads us to develop a framework for forecasting these losses jointly. In an application to quarterly US data on loan charge-offs from 1985 to 2019, we find that financial-cycle indicators – notably, the debt service ratio and credit-to-GDP gap – deliver reliable real-time forecasts, signalling turning points up to three years in advance. Provisions and capital that reflect such forecasts would help reduce the procyclicality of banks’ loss-absorbing resources.
  • 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%).
  • Qin, Duo; He, Xinhua (2012)
    BOFIT Discussion Papers 25/2012
    Ways of extracting financial condition indices (FCI) are explored and alternative FCIs external to the Chinese economy are constructed to model their predictive content. The exploration aims at highlighting the rich and varied dynamic features of financial variables underlying FCIs and the importance of synchronising dynamic information between FCIs and the real-sector variables to be forecasted. The modelling experiment aims at improving the forecasting model upon which the FCIs are assessed. Four variables are chosen as the likely macro channel of the FCIs affecting the Chinese economy. It is found that the FCI-led models enjoy forecasting advantages over a benchmark model in three out of the four variables, although the benchmark model is not dominated by the FCI-led models when judged by in-sample encompassing tests. The evidence indicates the increasing exposure of the Chinese economy to the global financial conditions. Key words: financial index, dynamic factor, VAR, error correction, encompassing JEL Classification: E17, F37, G17, C43
  • 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.
  • Cheung, Yin-Wong; Hui, Cho-Hoi; Tsang, Andrew (2017)
    BOFIT Discussion Papers 7/2017
    On August 11, 2015, China revamped its procedure for setting the official central parity of the renminbi (RMB) against the US dollar. Our empirical investigation suggests that the intertemporal dynamics of China’s central parity shifted after this policy change, though the deviation of the RMB offshore rate from the central parity and the US dollar index remained the two significant determi-nants of central parity after the policy change. In contrast, the VIX index only offered explanatory power up to August 2015. Thereafter, the onshore RMB rate and the difference between the one-month offshore and onshore RMB forward points have significant impacts on the central parity. While the US dollar index effect remains, we find no evidence of a rate-fixing role for the RMB exchange rate against the currency basket announced by China in December 2015.
  • Faria, Gonçalo; Verona, Fabio (2020)
    Journal of Financial Markets September
    Published in BoF DP 7/2018 "The equity risk premium and the low frequency of the term spread" http://urn.fi/URN:NBN:fi:bof-201804041428
    We extract cycles from the term spread and study their role for predicting the equity premium using linear models. When properly extracted, the trend of the term spread is a strong and robust out-of-sample equity premium predictor, both from a statistical and an economic point of view. It outperforms several variables recently proposed as good equity premium predictors. Our results support recent findings in the asset pricing literature that the low-frequency components of macroeconomic variables play a crucial role in shaping the dynamics of equity markets. Hence, for policymakers and financial market participants interested in gauging equity market developments, the trend of the term spread is a promising variable to look at.
  • Faria, Gonçalo; Verona, Fabio (2020)
    Bank of Finland Research Discussion Papers 6/2020
    Online First in Quantitative Finance https://doi.org/10.1080/14697688.2020.1820071
    Any time series can be decomposed into cyclical components fluctuating at different frequencies. Accordingly, in this paper we propose a method to forecast the stock market's equity premium which exploits the frequency relationship between the equity premium and several predictor variables. We evaluate a large set of models and find that, by selecting the relevant frequencies for equity premium forecasting, this method significantly improves in both statistical and economic sense upon standard time series forecasting methods. This improvement is robust regardless of the predictor used, the out-of-sample period considered, and the frequency of the data used.