Pub Date : 2022-01-03DOI: 10.1108/jrf-11-2020-0230
G. Dorfleitner, Isabel Scheckenbach
PurposeSocial trading platforms are considered to be amongst the major innovations in online trading. The purpose of this article is to analyze the trading activity of traders on social trading networks by taking a behavioral approach. Additionally, the authors investigate the factors that influence the irrational part of trading activity derived from the key characteristics of these platforms, i.e. those dealing with social interaction.Design/methodology/approachThe investigation utilizes an extensive set of trading data from two major platforms in Germany to study the trading behavior. The authors apply a fixed effects two-stage least squares (2SLS) approach to quantify the relationship between trading activity and performance and define overconfidence as the part of trading activity that is irrationally motivated and results in negative returns.FindingsThe results provide evidence for the negative relationship between overconfidence and return on social trading platforms. The authors find that the number of followers and some platform-specific features significantly affect the trading behavior of the traders.Originality/valueThe authors contribute to the existing literature by exploring how the novel social interaction characteristics of online trading impact trading activity by giving rise to a new dimension of overconfidence. In addition, the authors evidence that the different frameworks of the platforms motivate heterogenous behavioral responses by the signalers. Finally, the authors refine existing studies by applying a distinct methodology for modeling overconfidence.
{"title":"Trading activity on social trading platforms – a behavioral approach*","authors":"G. Dorfleitner, Isabel Scheckenbach","doi":"10.1108/jrf-11-2020-0230","DOIUrl":"https://doi.org/10.1108/jrf-11-2020-0230","url":null,"abstract":"PurposeSocial trading platforms are considered to be amongst the major innovations in online trading. The purpose of this article is to analyze the trading activity of traders on social trading networks by taking a behavioral approach. Additionally, the authors investigate the factors that influence the irrational part of trading activity derived from the key characteristics of these platforms, i.e. those dealing with social interaction.Design/methodology/approachThe investigation utilizes an extensive set of trading data from two major platforms in Germany to study the trading behavior. The authors apply a fixed effects two-stage least squares (2SLS) approach to quantify the relationship between trading activity and performance and define overconfidence as the part of trading activity that is irrationally motivated and results in negative returns.FindingsThe results provide evidence for the negative relationship between overconfidence and return on social trading platforms. The authors find that the number of followers and some platform-specific features significantly affect the trading behavior of the traders.Originality/valueThe authors contribute to the existing literature by exploring how the novel social interaction characteristics of online trading impact trading activity by giving rise to a new dimension of overconfidence. In addition, the authors evidence that the different frameworks of the platforms motivate heterogenous behavioral responses by the signalers. Finally, the authors refine existing studies by applying a distinct methodology for modeling overconfidence.","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2022-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45336871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-27DOI: 10.1108/jrf-10-2020-0217
J. R. Barber
PurposeThis paper determines a simple transformation that nearly linearizes the bond price formula. The transformed price can be used to derive a highly accurate approximation of the change in a bond price resulting from a change in interest rates.Design/methodology/approachA logarithmic transformation exactly linearizes the price function for a zero coupon bond and a reciprocal transformation exactly linearizes the price function for a perpetuity. A power law transformation combines aspects of both types of transformations and provides a superior approximation of the bond price sensitivity for both short-term and long-term bonds.FindingsIt is demonstrated that the new formula, based on power-law transformation, is a much better approximation than either the traditional duration-convexity approximation and the more recently developed approximations based on logarithmic transformation of the price function.Originality/valueThe new formula will be used by risk managers to perform stress-testing on bond portfolios. The new formula can easily be inverted, making it possible to relate the distribution of prices (which are observable in the market) to the distribution of yields (which are numerical solutions that are not directly observable).
{"title":"Power law bond price and yield approximation","authors":"J. R. Barber","doi":"10.1108/jrf-10-2020-0217","DOIUrl":"https://doi.org/10.1108/jrf-10-2020-0217","url":null,"abstract":"PurposeThis paper determines a simple transformation that nearly linearizes the bond price formula. The transformed price can be used to derive a highly accurate approximation of the change in a bond price resulting from a change in interest rates.Design/methodology/approachA logarithmic transformation exactly linearizes the price function for a zero coupon bond and a reciprocal transformation exactly linearizes the price function for a perpetuity. A power law transformation combines aspects of both types of transformations and provides a superior approximation of the bond price sensitivity for both short-term and long-term bonds.FindingsIt is demonstrated that the new formula, based on power-law transformation, is a much better approximation than either the traditional duration-convexity approximation and the more recently developed approximations based on logarithmic transformation of the price function.Originality/valueThe new formula will be used by risk managers to perform stress-testing on bond portfolios. The new formula can easily be inverted, making it possible to relate the distribution of prices (which are observable in the market) to the distribution of yields (which are numerical solutions that are not directly observable).","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":"1 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41975551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-14DOI: 10.1108/jrf-07-2021-0107
Yann Ferrat, Frédéric Daty, R. Burlacu
PurposeThe growth of socially responsible assets has been exponential over the last decade, they now account for almost a third of professional investments. As the growth persists, faith and conviction investors reshape the equity markets. To fully comprehend the impact of socially conscious participants on security returns, this paper attempts to provide insights on how responsible investment growth has impacted the returns of sustainable stocks. The examination is split by investment horizon to account for short and long effects.Design/methodology/approachUsing an exclusive dataset of non-financial ratings, provided by MSCI ESG research, the authors examine the cross-sectional returns of US and European sustainability-leading and lagging corporations between 2007 and 2019. Panel models robust to country, firm-year and industry effects were then employed to examine the impact of responsible investment growth on future stock returns.FindingsThe authors find evidence that the impact of responsible investment growth is dual contingent upon the timeframe considered. In the short run, sustainability-leading and lagging firms display similar stock returns. However, the spread in returns is negative over long horizons and increasing over time.Originality/valueThe examination performed in this study highlights the significant effect of responsible investment growth on future stock returns. Overall, the authors’ findings are consistent with the price pressure hypothesis in the short run and the cost of capital alteration over longer horizons.
{"title":"Short- and long-term effects of responsible investment growth on equity returns","authors":"Yann Ferrat, Frédéric Daty, R. Burlacu","doi":"10.1108/jrf-07-2021-0107","DOIUrl":"https://doi.org/10.1108/jrf-07-2021-0107","url":null,"abstract":"PurposeThe growth of socially responsible assets has been exponential over the last decade, they now account for almost a third of professional investments. As the growth persists, faith and conviction investors reshape the equity markets. To fully comprehend the impact of socially conscious participants on security returns, this paper attempts to provide insights on how responsible investment growth has impacted the returns of sustainable stocks. The examination is split by investment horizon to account for short and long effects.Design/methodology/approachUsing an exclusive dataset of non-financial ratings, provided by MSCI ESG research, the authors examine the cross-sectional returns of US and European sustainability-leading and lagging corporations between 2007 and 2019. Panel models robust to country, firm-year and industry effects were then employed to examine the impact of responsible investment growth on future stock returns.FindingsThe authors find evidence that the impact of responsible investment growth is dual contingent upon the timeframe considered. In the short run, sustainability-leading and lagging firms display similar stock returns. However, the spread in returns is negative over long horizons and increasing over time.Originality/valueThe examination performed in this study highlights the significant effect of responsible investment growth on future stock returns. Overall, the authors’ findings are consistent with the price pressure hypothesis in the short run and the cost of capital alteration over longer horizons.","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42339304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-06DOI: 10.1108/jrf-04-2020-0073
Richard Watt, Philip Gunby
PurposeThe Arrow–Pratt approximation to the risk premium is only valid for small risks. In this paper we consider a second approximation, based on risk-neutral probabilities and which requires no greater information than the Arrow–Pratt approximation, that works well for both small and large risks.Design/methodology/approachThe paper is theoretical in nature, although it also provides illustrative numerical simulations.FindingsThe new approximation proposed here appears to be significantly superior to Arrow–Pratt for approximating the true value of the risk premium when the risk is large. It may also approximate better even for relatively small risks.Originality/valueAs far as we are aware, there are no other known approximations for the risk premium when the risk involved is large.
{"title":"A new approximation for the risk premium with large risks","authors":"Richard Watt, Philip Gunby","doi":"10.1108/jrf-04-2020-0073","DOIUrl":"https://doi.org/10.1108/jrf-04-2020-0073","url":null,"abstract":"PurposeThe Arrow–Pratt approximation to the risk premium is only valid for small risks. In this paper we consider a second approximation, based on risk-neutral probabilities and which requires no greater information than the Arrow–Pratt approximation, that works well for both small and large risks.Design/methodology/approachThe paper is theoretical in nature, although it also provides illustrative numerical simulations.FindingsThe new approximation proposed here appears to be significantly superior to Arrow–Pratt for approximating the true value of the risk premium when the risk is large. It may also approximate better even for relatively small risks.Originality/valueAs far as we are aware, there are no other known approximations for the risk premium when the risk involved is large.","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44146300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-04DOI: 10.1108/jrf-01-2021-0002
Rangga Handika
PurposeThis paper offers an alternative approach to assessing contagions in price and load in the Australian interconnected power markets. This approach enabled us to identify a high-risk region and assess the direction of contagions from both buyers' and sellers' perspectives.Design/methodology/approachThe author used a multinomial logit method to measure contagions. Having identified the exceedance and coexceedances, the author estimated the multinomial logit coefficients of the covariates explaining the probability of a certain number of coexceedances.FindingsMarket participants should recognize the presence of contagion risk and scrutinize price and load dynamics in the NSW and VIC regions to anticipate any simultaneous extreme changes. Regulators need to stabilize the demand and supply sides in those regions to minimize any possible contagions.Originality/valueThis paper presents a pioneering study investigating contagion in the Australian interconnected power markets.
{"title":"Contagions in interconnected power markets","authors":"Rangga Handika","doi":"10.1108/jrf-01-2021-0002","DOIUrl":"https://doi.org/10.1108/jrf-01-2021-0002","url":null,"abstract":"PurposeThis paper offers an alternative approach to assessing contagions in price and load in the Australian interconnected power markets. This approach enabled us to identify a high-risk region and assess the direction of contagions from both buyers' and sellers' perspectives.Design/methodology/approachThe author used a multinomial logit method to measure contagions. Having identified the exceedance and coexceedances, the author estimated the multinomial logit coefficients of the covariates explaining the probability of a certain number of coexceedances.FindingsMarket participants should recognize the presence of contagion risk and scrutinize price and load dynamics in the NSW and VIC regions to anticipate any simultaneous extreme changes. Regulators need to stabilize the demand and supply sides in those regions to minimize any possible contagions.Originality/valueThis paper presents a pioneering study investigating contagion in the Australian interconnected power markets.","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43404944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-28DOI: 10.1108/jrf-10-2020-0226
Omar Farooq, Harit Satt, F. Bendriouch, Diae Lamiri
PurposeThe aim of this paper is to document the impact of dividend policies on the downside risk in stock prices.Design/methodology/approachThe authors use the data for non-financial firms from the MENA region to test our arguments by estimating the pooled OLS regressions. The data cover the period between 2010 and 2018.FindingsThis paper shows that firms with higher dividend payouts have significantly lower downside risk in their stock prices than the other firms. The findings of this paper are robust across various proxies of dividend policy and across various sub-samples. This paper contends that lower downside risk associated with the stock prices of firms paying high dividends is due to the fact that these firms have lower agency problems. Lower agency problems reduce the downside risk in stock prices.Originality/valueTo the best of the authors’ knowledge, most of the prior research (covering the MENA region) overlooks the impact of dividend policy on the downside risk in stock prices. This paper fills this gap by documenting the relationship between the two by using the data for firms from the MENA region.
{"title":"Dividend policy and the downside risk in stock prices: evidence from the MENA region","authors":"Omar Farooq, Harit Satt, F. Bendriouch, Diae Lamiri","doi":"10.1108/jrf-10-2020-0226","DOIUrl":"https://doi.org/10.1108/jrf-10-2020-0226","url":null,"abstract":"PurposeThe aim of this paper is to document the impact of dividend policies on the downside risk in stock prices.Design/methodology/approachThe authors use the data for non-financial firms from the MENA region to test our arguments by estimating the pooled OLS regressions. The data cover the period between 2010 and 2018.FindingsThis paper shows that firms with higher dividend payouts have significantly lower downside risk in their stock prices than the other firms. The findings of this paper are robust across various proxies of dividend policy and across various sub-samples. This paper contends that lower downside risk associated with the stock prices of firms paying high dividends is due to the fact that these firms have lower agency problems. Lower agency problems reduce the downside risk in stock prices.Originality/valueTo the best of the authors’ knowledge, most of the prior research (covering the MENA region) overlooks the impact of dividend policy on the downside risk in stock prices. This paper fills this gap by documenting the relationship between the two by using the data for firms from the MENA region.","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2021-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43804284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-21DOI: 10.1108/jrf-04-2021-0060
Maximilian Bär, Nadine Gatzert, Jochen Russ
PurposeThe aim of this paper is to modify the shape of utility functions traditionally used in expected utility theory (EUT) to derive optimal retirement saving decisions. Inspired by current reference point based approaches, the authors argue that utility functions with jumps or kinks at certain threshold points might very well be rational.Design/methodology/approachThe authors suggest an alternative to typical utility functions used in EUT, to be applied in the context of retirement saving decisions. The authors argue that certain elements that are used to model biases in behavioral models should–in the context of optimal retirement saving decisions–be considered “rational” and hence be included in a normative setting as well. The authors compare the optimal asset allocation derived under such utility functions with results under traditional power utility.FindingsThe authors find that the considered threshold levels can have a significant impact on the optimal investment decision for some individuals. In particular, the authors show that a much riskier investment than under EUT can become optimal if some level of income is secured by a social security and a significant portion of the distribution of terminal wealth lies below this level.Originality/valueContrary to previous work, this model is especially designed to assess the question of optimal product choice/asset allocation in the specific setting of retirement planning and from a normative point of view. In this regard, the authors first motivate the use of several thresholds and then apply this approach in a capital market model with stochastic stocks and stochastic interest rates to two illustrative investment alternatives.
{"title":"Optimal asset allocation in retirement planning: threshold-based utility maximization","authors":"Maximilian Bär, Nadine Gatzert, Jochen Russ","doi":"10.1108/jrf-04-2021-0060","DOIUrl":"https://doi.org/10.1108/jrf-04-2021-0060","url":null,"abstract":"PurposeThe aim of this paper is to modify the shape of utility functions traditionally used in expected utility theory (EUT) to derive optimal retirement saving decisions. Inspired by current reference point based approaches, the authors argue that utility functions with jumps or kinks at certain threshold points might very well be rational.Design/methodology/approachThe authors suggest an alternative to typical utility functions used in EUT, to be applied in the context of retirement saving decisions. The authors argue that certain elements that are used to model biases in behavioral models should–in the context of optimal retirement saving decisions–be considered “rational” and hence be included in a normative setting as well. The authors compare the optimal asset allocation derived under such utility functions with results under traditional power utility.FindingsThe authors find that the considered threshold levels can have a significant impact on the optimal investment decision for some individuals. In particular, the authors show that a much riskier investment than under EUT can become optimal if some level of income is secured by a social security and a significant portion of the distribution of terminal wealth lies below this level.Originality/valueContrary to previous work, this model is especially designed to assess the question of optimal product choice/asset allocation in the specific setting of retirement planning and from a normative point of view. In this regard, the authors first motivate the use of several thresholds and then apply this approach in a capital market model with stochastic stocks and stochastic interest rates to two illustrative investment alternatives.","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48434025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-20DOI: 10.1108/jrf-10-2020-0222
Á. León, T. Ñíguez
PurposeThe authors apply their method to analyze which portfolios are capable of providing superior performance to those based on the Sharpe ratio (SR).Design/methodology/approachIn this paper the authors illustrate the use of conditional copulas for identifying differences in alternative portfolio performance strategies. The authors analyze which portfolios are capable of providing superior performance to those based on the SR.FindingsThe results show that under the Gaussian copula, both expected tail ratio (ETR) and skewness-kurtosis ratio portfolios exhibit remarkably low correlations respecting the SR portfolio. This means that these two portfolios are different respecting the SR one. The authors also find that copulas which focus on either the upper tail (Gumbel) or the lower tail (Clayton) render significant differences. In short, the copula analysis is useful to understand what kind of equity-screening strategy based on its corresponding performance measure (PM) performs better in relation to the SR portfolio.Practical implicationsCopula methods for evaluating relative tail forecasting performance provide an alternative tool when forecast differences are very small or found non statistically significant through standard tests.Originality/valueOur copula methods to evaluate models' performance differences are significant because when models' performance is rather similar, conclusions on statistical differences, can be defective as they may hinge on the subsample type or size used, leading to inefficient investment decisions. Our method based in copula is novel in this research topic.
{"title":"Copula methods for evaluating relative tail forecasting performance","authors":"Á. León, T. Ñíguez","doi":"10.1108/jrf-10-2020-0222","DOIUrl":"https://doi.org/10.1108/jrf-10-2020-0222","url":null,"abstract":"PurposeThe authors apply their method to analyze which portfolios are capable of providing superior performance to those based on the Sharpe ratio (SR).Design/methodology/approachIn this paper the authors illustrate the use of conditional copulas for identifying differences in alternative portfolio performance strategies. The authors analyze which portfolios are capable of providing superior performance to those based on the SR.FindingsThe results show that under the Gaussian copula, both expected tail ratio (ETR) and skewness-kurtosis ratio portfolios exhibit remarkably low correlations respecting the SR portfolio. This means that these two portfolios are different respecting the SR one. The authors also find that copulas which focus on either the upper tail (Gumbel) or the lower tail (Clayton) render significant differences. In short, the copula analysis is useful to understand what kind of equity-screening strategy based on its corresponding performance measure (PM) performs better in relation to the SR portfolio.Practical implicationsCopula methods for evaluating relative tail forecasting performance provide an alternative tool when forecast differences are very small or found non statistically significant through standard tests.Originality/valueOur copula methods to evaluate models' performance differences are significant because when models' performance is rather similar, conclusions on statistical differences, can be defective as they may hinge on the subsample type or size used, leading to inefficient investment decisions. Our method based in copula is novel in this research topic.","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45257130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-23DOI: 10.1108/JRF-08-2020-0184
Peter Cincinelli, Domenico Piatti
PurposeThe paper aims to disentangle the physiological credit risk from the credit risk coming from the inefficient screening and monitoring management process. The analysis is conducted on a sample of 338 Italian banks–56 joint-stock banks (SpA), 23 cooperative banks (Popolari) and 259 mutual banks (BCCs)–over the time period 2006–2017.Design/methodology/approachThe authors use the maximum likelihood method to estimate the efficient frontier, as a set of best management credit practices, which minimises the credit risk defined on the basis of the level of loans granted, the technical structure of the loan portfolio (such as credit lines, mortgages, consumer loans and other technical loan categories) and the interest rate charges.FindingsThe empirical results show that the increase in non-performing loans (NPLs) is related both to the severe and protracted recession in Italy, which significantly reduced borrowers' capacity to service their debt, and to other factors, such as banks' lending monitoring policies with limited capacity to work-out defaulted loans.Originality/valueThe authors propose a new approach to the study of the performance of the credit process. With the stochastic frontier, the physiological credit risk, assumed by the bank according to its lending activity and management choices, is separated from the credit risk resulting from an inefficient management of the screening and monitoring process. In addition, the authors analyse the determinants of the excess of NPLs. This aspect is considered particularly original because the scientific contributions which consider the causes of NPLs have largely focused on the level of NPLs not considering the physiological part, linked to the structure of the bank's loan portfolio and its operational strategy and therefore not compressible and in any case not attributable to mismanagement or moral hazard.
{"title":"How inefficient is an inefficient credit process? An analysis of the Italian banking system","authors":"Peter Cincinelli, Domenico Piatti","doi":"10.1108/JRF-08-2020-0184","DOIUrl":"https://doi.org/10.1108/JRF-08-2020-0184","url":null,"abstract":"PurposeThe paper aims to disentangle the physiological credit risk from the credit risk coming from the inefficient screening and monitoring management process. The analysis is conducted on a sample of 338 Italian banks–56 joint-stock banks (SpA), 23 cooperative banks (Popolari) and 259 mutual banks (BCCs)–over the time period 2006–2017.Design/methodology/approachThe authors use the maximum likelihood method to estimate the efficient frontier, as a set of best management credit practices, which minimises the credit risk defined on the basis of the level of loans granted, the technical structure of the loan portfolio (such as credit lines, mortgages, consumer loans and other technical loan categories) and the interest rate charges.FindingsThe empirical results show that the increase in non-performing loans (NPLs) is related both to the severe and protracted recession in Italy, which significantly reduced borrowers' capacity to service their debt, and to other factors, such as banks' lending monitoring policies with limited capacity to work-out defaulted loans.Originality/valueThe authors propose a new approach to the study of the performance of the credit process. With the stochastic frontier, the physiological credit risk, assumed by the bank according to its lending activity and management choices, is separated from the credit risk resulting from an inefficient management of the screening and monitoring process. In addition, the authors analyse the determinants of the excess of NPLs. This aspect is considered particularly original because the scientific contributions which consider the causes of NPLs have largely focused on the level of NPLs not considering the physiological part, linked to the structure of the bank's loan portfolio and its operational strategy and therefore not compressible and in any case not attributable to mismanagement or moral hazard.","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48195240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-22DOI: 10.1108/JRF-05-2020-0113
B. Engelmann
PurposeThe purpose of this article is to derive formulas for lifetime expected credit loss of loans that are required for the calculation of loan loss reserves under IFRS 9. This is done both for fixed-rate and floating rate loans under different assumptions on LGD modeling, prepayment, and discount rates.Design/methodology/approachThis study provides exact formulas for lifetime expected credit loss derived analytically together with the mathematical proofs of each expression.FindingsThis articles shows that the formula most commonly applied in the literature for calculating lifetime expected credit loss is inconsistent with measuring expected loss based on expected discounted cash flows. Formulas based on discounted cash flows always lead to more conservative numbers.Practical implicationsFor banks reporting under IFRS 9, the implication of this research is a better understanding of the different approaches used for computing lifetime expected loss, how they are connected, and what assumptions are underlying each approach. This may lead to corrections in existing frameworks to make applications of risk management systems more consistent.Originality/valueWhile there is a lot of literature explaining IFRS 9 and evaluating its impact, none of the existing research has systematically analyzed the calculation of lifetime expected credit loss for this purpose and how the formula changes under different modeling assumptions. This gap is filled by this study.
{"title":"Calculating lifetime expected loss for IFRS 9: which formula is measuring what?","authors":"B. Engelmann","doi":"10.1108/JRF-05-2020-0113","DOIUrl":"https://doi.org/10.1108/JRF-05-2020-0113","url":null,"abstract":"PurposeThe purpose of this article is to derive formulas for lifetime expected credit loss of loans that are required for the calculation of loan loss reserves under IFRS 9. This is done both for fixed-rate and floating rate loans under different assumptions on LGD modeling, prepayment, and discount rates.Design/methodology/approachThis study provides exact formulas for lifetime expected credit loss derived analytically together with the mathematical proofs of each expression.FindingsThis articles shows that the formula most commonly applied in the literature for calculating lifetime expected credit loss is inconsistent with measuring expected loss based on expected discounted cash flows. Formulas based on discounted cash flows always lead to more conservative numbers.Practical implicationsFor banks reporting under IFRS 9, the implication of this research is a better understanding of the different approaches used for computing lifetime expected loss, how they are connected, and what assumptions are underlying each approach. This may lead to corrections in existing frameworks to make applications of risk management systems more consistent.Originality/valueWhile there is a lot of literature explaining IFRS 9 and evaluating its impact, none of the existing research has systematically analyzed the calculation of lifetime expected credit loss for this purpose and how the formula changes under different modeling assumptions. This gap is filled by this study.","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2021-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42820370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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