Executive Summary: In this paper, we evaluate the regulatory structure for risk management at U.S. banking institutions as compared to technology companies. We also evaluate the appropriate regulatory structure for cloud service providers to U.S. banking institutions, as banking institutions are increasing their reliance on cloud service providers for their data needs and effective risk management regulation can safely facilitate that transition. Part I of our paper provides a comprehensive review of the regulation of corporate governance and risk management at U.S. banking institutions with a focus on how the regulatory structure is tailored to address the business activities of U.S. banks. We find that the regulation of risk management processes by U.S. banking institutions is highly prescriptive and that U.S. banking regulators have centralized key risk management responsibilities with the board of directors and senior management. Part II of our paper reviews the regulation of corporate governance and risk management at U.S. technology companies. We find that the regulation of risk management at technology companies is principles-based and does not shift prescriptive responsibilities to technology companies’ board of directors. Part III of our paper considers whether the banking approach to the regulation of risk management or the technology approach to the regulation of risk management is better suited for cloud service providers to U.S. banks. In doing so, we consider key differences between the risks faced by U.S. banking institutions as compared to cloud service providers. We conclude that a principles-based and decentralized approach to the regulation and supervision of cloud service providers and other technology services providers to U.S. banking institutions would better address the risks inherent in such services and facilitate continued adoption of cloud services by U.S. banking institutions.
{"title":"Regulation of Governance & Risk Management: The Intersection of Banking & Technology","authors":"H. Scott, D. Campbell, John W. Gulliver","doi":"10.2139/ssrn.3887043","DOIUrl":"https://doi.org/10.2139/ssrn.3887043","url":null,"abstract":"Executive Summary: In this paper, we evaluate the regulatory structure for risk management at U.S. banking institutions as compared to technology companies. We also evaluate the appropriate regulatory structure for cloud service providers to U.S. banking institutions, as banking institutions are increasing their reliance on cloud service providers for their data needs and effective risk management regulation can safely facilitate that transition. Part I of our paper provides a comprehensive review of the regulation of corporate governance and risk management at U.S. banking institutions with a focus on how the regulatory structure is tailored to address the business activities of U.S. banks. We find that the regulation of risk management processes by U.S. banking institutions is highly prescriptive and that U.S. banking regulators have centralized key risk management responsibilities with the board of directors and senior management. Part II of our paper reviews the regulation of corporate governance and risk management at U.S. technology companies. We find that the regulation of risk management at technology companies is principles-based and does not shift prescriptive responsibilities to technology companies’ board of directors. Part III of our paper considers whether the banking approach to the regulation of risk management or the technology approach to the regulation of risk management is better suited for cloud service providers to U.S. banks. In doing so, we consider key differences between the risks faced by U.S. banking institutions as compared to cloud service providers. We conclude that a principles-based and decentralized approach to the regulation and supervision of cloud service providers and other technology services providers to U.S. banking institutions would better address the risks inherent in such services and facilitate continued adoption of cloud services by U.S. banking institutions.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130517073","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}
René M. Stulz, James G. Tompkins, Rohan Williamson, Z. Ye
We develop a theory of bank board risk committees. With this theory, such committees are valuable even though there is no expectation that bank risk is lower if the bank has a well-functioning risk committee. As predicted by our theory (1) many large and complex banks voluntarily chose to have a risk committee before the Dodd-Frank Act forced bank holding companies with assets in excess of $10 billion to have a board risk committee, and (2) establishing a board risk committee does not reduce a bank’s risk on average. Using unique interview data, we show that the work of risk committees is consistent with our theory in part.
{"title":"Why Do Bank Boards Have Risk Committees?","authors":"René M. Stulz, James G. Tompkins, Rohan Williamson, Z. Ye","doi":"10.2139/ssrn.3893882","DOIUrl":"https://doi.org/10.2139/ssrn.3893882","url":null,"abstract":"We develop a theory of bank board risk committees. With this theory, such committees are valuable even though there is no expectation that bank risk is lower if the bank has a well-functioning risk committee. As predicted by our theory (1) many large and complex banks voluntarily chose to have a risk committee before the Dodd-Frank Act forced bank holding companies with assets in excess of $10 billion to have a board risk committee, and (2) establishing a board risk committee does not reduce a bank’s risk on average. Using unique interview data, we show that the work of risk committees is consistent with our theory in part.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114063276","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}
We propose a novel stochastic time series model able to explain the stylized features of daily irradation level data in 5 cities in Germany. The model is suitable for applications to risk management of photovoltaic power production in renewable energy markets. The suggested dynamics is a low order autoregressive time series with seasonal level given by an atmospheric clear-sky model. Moreover, we detect a skewness property in the residuals which we explain by a winter-summer regime switch. The stochastic variance is modelled by a seasonally varying GARCH-dynamics. The winter and summer standardized residuals are proposed to be a Gaussian mixture model to capture the bimodal distributions. We estimate the model on the observed data, and perform a validation study. An application to energy markets studying the production at risk for a PV-producer is presented.
{"title":"A Stochastic Time-Series Model for Solar Irradiation","authors":"Karl Larsson, Rikard Green, F. Benth","doi":"10.2139/ssrn.3878453","DOIUrl":"https://doi.org/10.2139/ssrn.3878453","url":null,"abstract":"We propose a novel stochastic time series model able to explain the stylized features of daily irradation level data in 5 cities in Germany. The model is suitable for applications to risk management of photovoltaic power production in renewable energy markets. The suggested dynamics is a low order autoregressive time series with seasonal level given by an atmospheric clear-sky model. Moreover, we detect a skewness property in the residuals which we explain by a winter-summer regime switch. The stochastic variance is modelled by a seasonally varying GARCH-dynamics. The winter and summer standardized residuals are proposed to be a Gaussian mixture model to capture the bimodal distributions. We estimate the model on the observed data, and perform a validation study. An application to energy markets studying the production at risk for a PV-producer is presented.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121534931","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}
English Abstract: Armed with the copula distribution function that describes the asymmetric tail dependence, and the marginal distributions that capture the fat-tailed behavior, we estimate risk measures such as the Value-at-Risk and expected shortfall and evaluate whether those from the Gaussian copula function underestimate or overestimate true risk measures. We also investigate the impact of asymmetric tail dependence between the portfolio returns on the out-of-sample predictability of the returns. We fit the GPD as the two margins and a variety of copula functions in the extant literature in evaluating the risk measures. We compute the VaR and ES from the benchmark Gaussian copula model and the competing copula models and confirm that the benchmark model underestimate the levels of risk regardless of the measures of dependence. We use the out-of-sample predictability test to evaluate the performance of the competing copula models. We compare the out-of-sample predictability of the three copula-based competing models by calculating the out-of-sample log-likelihood. The Gaussian and the Student’s t copula models are the representative symmetric copulas, while the Clayton copula is selected as the representative asymmetric copula model. According to the test statistics, the out-of-sample predictability of the Clayton and the Student’s t copula models are superior to that of the Gaussian copula model. Overall, the Clayton copula model turns out to be the best out-of-sample forecasting copula model.
Korean Abstract: 주식수익률의 표본외 예측가능성은 동태적 자산배분과 포트폴리오 위험관리에 중요한 역할을 한다. 위험관리에서 다변량 정규분포의 가정과 주식수익률간 선형의존성 가정이 주로 이용되는 반면, 실제 데이터에서 다변량 정규분포와 선형의존성 가정은 위배되는 것이 일반적이다. 본 연구는 포트폴리오를 구성하는 주식수익률간 꼬리부분의 비대칭 의존성과 비정규성을 코퓰라 분포함수로 모형화하고 포트폴리오를 구성하는 자산의 한계분포는 일반화 파레토분포로 모형화하여 KOSPI 200과 S&P 500으로 구성된 지수포트폴리오의 위험척도를 측정하였다. 일반화 파레토분포와 가우스 코퓰라함수를 결합하여 측정하는 모형은 일반화 파레토분포와 Clayton 코퓰라함수를 이용하는 모형에 비해 지수 포트폴리오의 위험을 과소추정하는 것으로 나타났다. 또한 지수포트폴리오의 꼬리부분의 비대칭 의존성이 표본외 예측력에 미치는 영향을 평가하였는데, Clayton 코퓰라함수에 의한 지수포트폴리오 수익률의 표본외 예측력이 가장 뛰어난 것으로 나타났다. Clayton 코퓰라함수는 다변량 수익률분포에서 왼쪽 꼬리분포간의 비대칭 의존성을 반영하는, 즉 포트폴리오 구성자산들에서 동시에 양의 수익률이 나타나는 경우에 비해 동시에 음의 수익률이 나타나는 경우가 확률적으로 높은 사실을 반영하는, 통계적 특성을 가진 코퓰라 함수이다. 본 연구는 위험척도 추정과 수익률의 표본외 예측력 검정에서 Clayton 코퓰라함수가 우월한 것은 이러한 통계적 특성에 기인한 것임을 실증하였다.
{"title":"The Out-of-Sample Predictability of Asymmetric Dependence of Portfolio Returns - The Multivariate Copula Distribution Function Approach (포트폴리오 수익률 분포의 비대칭적 의존성의 표본외 예측가능성: Copula 분포함수에 의한 추정)","authors":"Hojin Lee","doi":"10.2139/ssrn.3877092","DOIUrl":"https://doi.org/10.2139/ssrn.3877092","url":null,"abstract":"<b>English Abstract:</b> Armed with the copula distribution function that describes the asymmetric tail dependence, and the marginal distributions that capture the fat-tailed behavior, we estimate risk measures such as the Value-at-Risk and expected shortfall and evaluate whether those from the Gaussian copula function underestimate or overestimate true risk measures. We also investigate the impact of asymmetric tail dependence between the portfolio returns on the out-of-sample predictability of the returns. We fit the GPD as the two margins and a variety of copula functions in the extant literature in evaluating the risk measures. We compute the VaR and ES from the benchmark Gaussian copula model and the competing copula models and confirm that the benchmark model underestimate the levels of risk regardless of the measures of dependence. We use the out-of-sample predictability test to evaluate the performance of the competing copula models. We compare the out-of-sample predictability of the three copula-based competing models by calculating the out-of-sample log-likelihood. The Gaussian and the Student’s t copula models are the representative symmetric copulas, while the Clayton copula is selected as the representative asymmetric copula model. According to the test statistics, the out-of-sample predictability of the Clayton and the Student’s t copula models are superior to that of the Gaussian copula model. Overall, the Clayton copula model turns out to be the best out-of-sample forecasting copula model.<br><br><b>Korean Abstract:</b> 주식수익률의 표본외 예측가능성은 동태적 자산배분과 포트폴리오 위험관리에 중요한 역할을 한다. 위험관리에서 다변량 정규분포의 가정과 주식수익률간 선형의존성 가정이 주로 이용되는 반면, 실제 데이터에서 다변량 정규분포와 선형의존성 가정은 위배되는 것이 일반적이다. 본 연구는 포트폴리오를 구성하는 주식수익률간 꼬리부분의 비대칭 의존성과 비정규성을 코퓰라 분포함수로 모형화하고 포트폴리오를 구성하는 자산의 한계분포는 일반화 파레토분포로 모형화하여 KOSPI 200과 S&P 500으로 구성된 지수포트폴리오의 위험척도를 측정하였다. 일반화 파레토분포와 가우스 코퓰라함수를 결합하여 측정하는 모형은 일반화 파레토분포와 Clayton 코퓰라함수를 이용하는 모형에 비해 지수 포트폴리오의 위험을 과소추정하는 것으로 나타났다. 또한 지수포트폴리오의 꼬리부분의 비대칭 의존성이 표본외 예측력에 미치는 영향을 평가하였는데, Clayton 코퓰라함수에 의한 지수포트폴리오 수익률의 표본외 예측력이 가장 뛰어난 것으로 나타났다. Clayton 코퓰라함수는 다변량 수익률분포에서 왼쪽 꼬리분포간의 비대칭 의존성을 반영하는, 즉 포트폴리오 구성자산들에서 동시에 양의 수익률이 나타나는 경우에 비해 동시에 음의 수익률이 나타나는 경우가 확률적으로 높은 사실을 반영하는, 통계적 특성을 가진 코퓰라 함수이다. 본 연구는 위험척도 추정과 수익률의 표본외 예측력 검정에서 Clayton 코퓰라함수가 우월한 것은 이러한 통계적 특성에 기인한 것임을 실증하였다.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131790361","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}
Binary option pays a fixed dollar amount if it matures in the money and nothing otherwise. While this cash-or-nothing payoff structure is very attractive to speculators, it also creates incentives to manipulate the underlying asset price in order to gain extra payoff. In this paper, we propose better designs for binary options that disincentivize market manipulation and highlight two key features of such designs. We demonstrate the effectiveness of the new contract designs using numerical examples.
{"title":"Analoging the Digital: Designing Better Binary Option Contracts","authors":"Yisong S. Tian","doi":"10.2139/ssrn.3873460","DOIUrl":"https://doi.org/10.2139/ssrn.3873460","url":null,"abstract":"Binary option pays a fixed dollar amount if it matures in the money and nothing otherwise. While this cash-or-nothing payoff structure is very attractive to speculators, it also creates incentives to manipulate the underlying asset price in order to gain extra payoff. In this paper, we propose better designs for binary options that disincentivize market manipulation and highlight two key features of such designs. We demonstrate the effectiveness of the new contract designs using numerical examples.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116178480","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}
We develop a LSTM neural network for the joint prediction of volatility, realized volatility and Value-at-Risk. Regularization by means of pooling the dynamic structure for the different outputs of the models is shown to be a powerful method for improving forecasts and smoothing VaR estimates. The method is applied to daily and high-frequency returns of the S&P500 index over a period of 25 years.
{"title":"A Neural Network with Shared Dynamics for Multi-Step Prediction of Value-at-Risk and Volatility","authors":"N. Basturk, P. Schotman, Hugo Schyns","doi":"10.2139/ssrn.3871096","DOIUrl":"https://doi.org/10.2139/ssrn.3871096","url":null,"abstract":"We develop a LSTM neural network for the joint prediction of volatility, realized volatility and Value-at-Risk. Regularization by means of pooling the dynamic structure for the different outputs of the models is shown to be a powerful method for improving forecasts and smoothing VaR estimates. The method is applied to daily and high-frequency returns of the S&P500 index over a period of 25 years.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133004369","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}
Simon Fritzsch, Felix Irresberger, Gregor N. F. Weiß
This paper presents a robust new finding that delta-hedged equity option returns include a volatility risk premium. To separate volatility risk premia from confounding effects, we estimate conditional quantile curves of implied volatilities using machine learning. We find that a zero-cost trading strategy that is long (short) in the portfolio with low (high) implied volatility -- conditional on the options' moneyness and realized volatility -- produces an economically and statistically significant average monthly return. Using conditional quantile curves not only helps in distinguishing volatility risk premia from other effects, most notably realized volatility, it also leads to returns that are higher than those reported in previous work on similar volatility strategies.
{"title":"Cross-Section of Option Returns and the Volatility Risk Premium","authors":"Simon Fritzsch, Felix Irresberger, Gregor N. F. Weiß","doi":"10.2139/ssrn.3864131","DOIUrl":"https://doi.org/10.2139/ssrn.3864131","url":null,"abstract":"This paper presents a robust new finding that delta-hedged equity option returns include a volatility risk premium. To separate volatility risk premia from confounding effects, we estimate conditional quantile curves of implied volatilities using machine learning. We find that a zero-cost trading strategy that is long (short) in the portfolio with low (high) implied volatility -- conditional on the options' moneyness and realized volatility -- produces an economically and statistically significant average monthly return. Using conditional quantile curves not only helps in distinguishing volatility risk premia from other effects, most notably realized volatility, it also leads to returns that are higher than those reported in previous work on similar volatility strategies.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122705620","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}
We propose a novel approach in the assessment of a random risk variable X by introducing magnitude-propensity risk measures (mX, pX). This bivariate measure intends to account for the dual aspect of risk, where the magnitudes x of X tell how high are the losses incurred, whereas the probabilities P(X = x) reveal how often one has to expect to suffer such losses. The basic idea is to simultaneously quantify both the severity mX and the propensity pX of the real-valued risk X. This is to be contrasted with traditional univariate risk measures, like VaR or Expected shortfall, which typically conflate both effects.
In its simplest form, (mX, p X) is obtained by mass transportation in Wasserstein metric of the law PX of X to a two-points {0,mX} discrete distribution with mass pX at mX. The approach can also be formulated as a constrained optimal quantization problem.
This allows for an informative comparison of risks on both the magnitude and propensity scales. Several examples illustrate the proposed approach.
{"title":"Risk Quantization by Magnitude and Propensity","authors":"O. Faugeras, G. Pagès","doi":"10.2139/ssrn.3854467","DOIUrl":"https://doi.org/10.2139/ssrn.3854467","url":null,"abstract":"We propose a novel approach in the assessment of a random risk variable <i>X</i> by introducing magnitude-propensity risk measures (<i>m<sub>X</sub>, p<sub>X</sub></i>). This bivariate measure intends to account for the dual aspect of risk, where the magnitudes <i>x</i> of <i>X</i> tell how high are the losses incurred, whereas the probabilities <i>P(X = x)</i> reveal how often one has to expect to suffer such losses. The basic idea is to simultaneously quantify both the severity <i>mX</i> and the propensity <i>pX</i> of the real-valued risk <i>X</i>. This is to be contrasted with traditional univariate risk measures, like VaR or Expected shortfall, which typically conflate both effects.<br><br>In its simplest form, <i>(m<sub>X</sub>, p <sub>X</sub>)</i> is obtained by mass transportation in Wasserstein metric of the law <i>P<sup>X</sup></i> of <i>X</i> to a two-points {0,<i>m<sub>X</sub></i>} discrete distribution with mass <i>p<sub>X</sub></i> at <i>m<sub>X</sub></i>. The approach can also be formulated as a constrained optimal quantization problem. <br><br>This allows for an informative comparison of risks on both the magnitude and propensity scales. Several examples illustrate the proposed approach.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127588735","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}
There are mixed results on whether macro risks are spanned by the yield curve. This paper reviews the major arguments and takes a global perspective to obtain comprehensive evidence. We study a large cross-section of 22 countries, including both developed and emerging markets. Our regression evidence confirms that macro information provides explanatory power for bond excess returns on top of yield factors. This finding is particularly strong in emerging markets. However, from a mechanical perspective, discriminating between spanned and unspanned models when considering in-sample fit and term premium predictions makes no difference.
{"title":"Global Evidence on Unspanned Macro Risks in Dynamic Term Structure Models","authors":"Michel van der Wel, Yaoyuan Zhang","doi":"10.2139/ssrn.3853648","DOIUrl":"https://doi.org/10.2139/ssrn.3853648","url":null,"abstract":"There are mixed results on whether macro risks are spanned by the yield curve. This paper reviews the major arguments and takes a global perspective to obtain comprehensive evidence. We study a large cross-section of 22 countries, including both developed and emerging markets. Our regression evidence confirms that macro information provides explanatory power for bond excess returns on top of yield factors. This finding is particularly strong in emerging markets. However, from a mechanical perspective, discriminating between spanned and unspanned models when considering in-sample fit and term premium predictions makes no difference.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123587979","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}
We investigate how different components in firm characteristics affect expected returns and comovements in international stock markets. We decompose characteristics into country, industry, and adjusted components. Then, we use these components to capture time-series and cross-sectional variations in stock-level alphas and factor exposures. We show that decomposing characteristics is crucial to model jointly expected returns and comovements: (i) country (adjusted) components capture systematic risk exposures (alphas), (ii) component-based models outperform benchmark models, and (iii) alphas in international markets are significant, contrary to the U.S. market. However, trading on predicted alphas does not generate significant out-of-sample net performances, indicating that they are related to limits to arbitrage.
{"title":"What Matters in a Characteristic?","authors":"Hugues Langlois","doi":"10.2139/ssrn.3848587","DOIUrl":"https://doi.org/10.2139/ssrn.3848587","url":null,"abstract":"We investigate how different components in firm characteristics affect expected returns and comovements in international stock markets. We decompose characteristics into country, industry, and adjusted components. Then, we use these components to capture time-series and cross-sectional variations in stock-level alphas and factor exposures. We show that decomposing characteristics is crucial to model jointly expected returns and comovements: (i) country (adjusted) components capture systematic risk exposures (alphas), (ii) component-based models outperform benchmark models, and (iii) alphas in international markets are significant, contrary to the U.S. market. However, trading on predicted alphas does not generate significant out-of-sample net performances, indicating that they are related to limits to arbitrage.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130586806","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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