Research articles for the 2020-03-20
Bitcoin in a Time of Financial Crisis
SSRN
This is the article we had prepared around 1-2 weeks ago (data sample starts in October 2014 and ends on 4th of March 2020). But then coronavirus hit our country (Slovak Republic), and we were doing a lot of crisis management tasks and therefore were not able to publish it on time. Now, after the Bitcoin’s negative performance between 5th of March and current date, the conclusion of our short article seems logical. But we still feel that it makes sense to publish it in a way it was prepared.
SSRN
This is the article we had prepared around 1-2 weeks ago (data sample starts in October 2014 and ends on 4th of March 2020). But then coronavirus hit our country (Slovak Republic), and we were doing a lot of crisis management tasks and therefore were not able to publish it on time. Now, after the Bitcoin’s negative performance between 5th of March and current date, the conclusion of our short article seems logical. But we still feel that it makes sense to publish it in a way it was prepared.
Credit Risk in European Banks: The Bright Side of the Internal Ratings Based Approach
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This paper investigates the accuracy of internal rating based (IRB) models in measuring credit risk. We contribute to the growing debate on the current prudential regulatory framework by investigating the use of validated IRB models in promoting efficient risk management practises. Our empirical analysis is based on a novel panel data set of 177 Western European banks observed from 2008 to 2015, in the aftermath of the financial and economic crisis. We find that IRB banks were able to curb the increase in credit risk driven by the macroeconomic slowdown better than banks under the standardized approach. This suggests that the introduction of the internal ratings based approach by Basel II has promoted the adoption of stronger risk management practices among banks, as meant by the regulators.
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This paper investigates the accuracy of internal rating based (IRB) models in measuring credit risk. We contribute to the growing debate on the current prudential regulatory framework by investigating the use of validated IRB models in promoting efficient risk management practises. Our empirical analysis is based on a novel panel data set of 177 Western European banks observed from 2008 to 2015, in the aftermath of the financial and economic crisis. We find that IRB banks were able to curb the increase in credit risk driven by the macroeconomic slowdown better than banks under the standardized approach. This suggests that the introduction of the internal ratings based approach by Basel II has promoted the adoption of stronger risk management practices among banks, as meant by the regulators.
Financial Policy in an Exuberant World
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This paper studies optimal financial policy in a world where the financial sector can become excessively optimistic. I decompose the welfare effects of bank capital regulation to demonstrate the effects of exuberance and its interaction with incentive problems in banking. The optimal policy depends not only on the extent, but also on the type of optimism. For example, it is markedly different when the exuberance of banks focuses on neglected downside risk, as opposed to overstated upside opportunities. A central normative conclusion is that “leaning against the windâ€, by tightening capital requirements in exuberant times, can be counterproductive. I show that two natural metrics, describing the distortion in perceived upside and downside risk, are sufficient statistics for the policy implications of exuberance. My results shed light on the diverse empirical evidence on the relationship between bank capital and risk-taking. Finally, I investigate the sensitivity of these insights under different assumptions about government rationality and paternalism.
SSRN
This paper studies optimal financial policy in a world where the financial sector can become excessively optimistic. I decompose the welfare effects of bank capital regulation to demonstrate the effects of exuberance and its interaction with incentive problems in banking. The optimal policy depends not only on the extent, but also on the type of optimism. For example, it is markedly different when the exuberance of banks focuses on neglected downside risk, as opposed to overstated upside opportunities. A central normative conclusion is that “leaning against the windâ€, by tightening capital requirements in exuberant times, can be counterproductive. I show that two natural metrics, describing the distortion in perceived upside and downside risk, are sufficient statistics for the policy implications of exuberance. My results shed light on the diverse empirical evidence on the relationship between bank capital and risk-taking. Finally, I investigate the sensitivity of these insights under different assumptions about government rationality and paternalism.
G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning
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We present a reinforcement learning approach to goal based wealth management problems such as optimization of retirement plans or target dated funds. In such problems, an investor seeks to achieve a financial goal by making periodic investments in the portfolio while being employed, and periodically draws from the account when in retirement, in addition to the ability to re-balance the portfolio by selling and buying different assets (e.g. stocks). Instead of relying on a utility of consumption, we present G-Learner: a reinforcement learning algorithm that operates with explicitly defined one-step rewards, does not assume a data generation process, and is suitable for noisy data. Our approach is based on G-learning (Fox et al., 2015) - a probabilistic extension of the Q-learning method of reinforcement learning. In this paper, we demonstrate how G-learning, when applied to a quadratic reward and Gaussian reference policy, gives an entropy-regulated Linear Quadratic Regulator (LQR). This critical insight provides a novel and computationally tractable tool for wealth management tasks which scales to high dimensional portfolios. In addition to the solution of the direct problem of G-learning, we also present a new algorithm, GIRL, that extends our goal-based G-learning approach to the setting of Inverse Reinforcement Learning (IRL) where rewards collected by the agent are not observed, and should instead be inferred. We demonstrate that GIRL can successfully learn the reward parameters of a G-Learner agent and thus imitate its behavior. Finally, we discuss potential applications of the G-Learner and GIRL algorithms for wealth management and robo-advising.
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We present a reinforcement learning approach to goal based wealth management problems such as optimization of retirement plans or target dated funds. In such problems, an investor seeks to achieve a financial goal by making periodic investments in the portfolio while being employed, and periodically draws from the account when in retirement, in addition to the ability to re-balance the portfolio by selling and buying different assets (e.g. stocks). Instead of relying on a utility of consumption, we present G-Learner: a reinforcement learning algorithm that operates with explicitly defined one-step rewards, does not assume a data generation process, and is suitable for noisy data. Our approach is based on G-learning (Fox et al., 2015) - a probabilistic extension of the Q-learning method of reinforcement learning. In this paper, we demonstrate how G-learning, when applied to a quadratic reward and Gaussian reference policy, gives an entropy-regulated Linear Quadratic Regulator (LQR). This critical insight provides a novel and computationally tractable tool for wealth management tasks which scales to high dimensional portfolios. In addition to the solution of the direct problem of G-learning, we also present a new algorithm, GIRL, that extends our goal-based G-learning approach to the setting of Inverse Reinforcement Learning (IRL) where rewards collected by the agent are not observed, and should instead be inferred. We demonstrate that GIRL can successfully learn the reward parameters of a G-Learner agent and thus imitate its behavior. Finally, we discuss potential applications of the G-Learner and GIRL algorithms for wealth management and robo-advising.
Is there a Boutique Asset Management Premium?
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There exists evidence in the performance evaluation literature that mutual funds that are manufactured by large asset management groups with large “fund families†benefit from economies of scale in terms of marketing, distribution and resourcing that accrue from the larger organisation. In this paper we examine the performance of funds that are managed by “boutique†asset managers that tend to be small and which tend to offer a more focused fund range. Using European mutual fund data, we find evidence to suggest the existence of a boutique asset management premium. This premium is particularly pronounced in the European Mid/Small Cap and the Global Emerging market fund sectors, where we find it to be both economically and statistically significant; a finding that is robust to the factor model used to calculate alphas. These results suggest in particular, that if an investor is looking to invest in a European Mid/Small Cap or Emerging Market equity fund, then they should give serious consideration to investing with a Boutique fund manager.
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There exists evidence in the performance evaluation literature that mutual funds that are manufactured by large asset management groups with large “fund families†benefit from economies of scale in terms of marketing, distribution and resourcing that accrue from the larger organisation. In this paper we examine the performance of funds that are managed by “boutique†asset managers that tend to be small and which tend to offer a more focused fund range. Using European mutual fund data, we find evidence to suggest the existence of a boutique asset management premium. This premium is particularly pronounced in the European Mid/Small Cap and the Global Emerging market fund sectors, where we find it to be both economically and statistically significant; a finding that is robust to the factor model used to calculate alphas. These results suggest in particular, that if an investor is looking to invest in a European Mid/Small Cap or Emerging Market equity fund, then they should give serious consideration to investing with a Boutique fund manager.
On the Comprehensive Balance Sheet Stress Testing and Net Interest Income P/L Attribution
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The joint stress testing of net interest income interest rate risk and profit and loss from behavioral risks on a multi-horizon scenario path poses great challenges in enterprise stress testing and earnings risk attributions. We propose a framework for granular level stressed net interest income calculation and P/L attribution. The proposed framework can accommodate net interest income impact from interest rate risk, profit and losses from behavioral risks e.g. prepayments and credit defaults as well as facility and deposit contingent drawdowns at each horizon on a given scenario path. Our net interest income framework uses the matched maturity asset and liability concept to synthetically separate the loan origination risks and the Treasury strategic funding risks. It considerably simplifies the joint market and credit risk stress testing by focusing on the asset versus maturity matched liability net interest income on the asset side and the maturity matched asset versus actual liabilities cash flows on the Treasury side. In this framework there is no need for exact cash flow generation on the asset side. The focus is on the loan net interest income process which requires only the outstanding balance. The loan net interest income process is a stochastic process with behavioral events. At the time of the events, the net interest income process regime-switches to capture the event impact e.g., credit loss or prepayment with associated interest rate impact. The event based net interest income process is suitable for loan-level behavioral state path simulation models with competing risk. We also consider the corresponding net interest income process with conditional default and prepayment rate models. The obtained net interest income process can be applied to both loan-level models as well as pools of homogenous loans. We illustrate the framework with competing hazards and state transition models.
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The joint stress testing of net interest income interest rate risk and profit and loss from behavioral risks on a multi-horizon scenario path poses great challenges in enterprise stress testing and earnings risk attributions. We propose a framework for granular level stressed net interest income calculation and P/L attribution. The proposed framework can accommodate net interest income impact from interest rate risk, profit and losses from behavioral risks e.g. prepayments and credit defaults as well as facility and deposit contingent drawdowns at each horizon on a given scenario path. Our net interest income framework uses the matched maturity asset and liability concept to synthetically separate the loan origination risks and the Treasury strategic funding risks. It considerably simplifies the joint market and credit risk stress testing by focusing on the asset versus maturity matched liability net interest income on the asset side and the maturity matched asset versus actual liabilities cash flows on the Treasury side. In this framework there is no need for exact cash flow generation on the asset side. The focus is on the loan net interest income process which requires only the outstanding balance. The loan net interest income process is a stochastic process with behavioral events. At the time of the events, the net interest income process regime-switches to capture the event impact e.g., credit loss or prepayment with associated interest rate impact. The event based net interest income process is suitable for loan-level behavioral state path simulation models with competing risk. We also consider the corresponding net interest income process with conditional default and prepayment rate models. The obtained net interest income process can be applied to both loan-level models as well as pools of homogenous loans. We illustrate the framework with competing hazards and state transition models.
Policy Uncertainty in the Scandinavian Countries
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Globalization drives the need to properly understand policy uncertainty and how it affects both the economy in general and business conditions. To systematically investigate the effect of policy uncertainty on small, open economies, we develop a policy uncertainty index based on newspaper content for each of the three Scandinavian countries; Norway, Denmark and Sweden. We show how these indices capture important historical events, both local events such as referendums and certain general elections, but also global events such as financial crises. Our narrative validation provides evidence that the three indices are good measures of policy uncertainty. Further, we compare historical policy uncertainty in the Scandinavian countries to a similar index for the US, before analysing the effect of both local and US policy uncertainty on the Scandinavian economies. Our findings indicate that increased policy uncertainty both at home and in the US leads to economic contraction, a significant decline in stock markets and a long-lasting reduction in the Scandinavian countries’ Purchasing Managers’ Index. These results can be highly relevant for anyone seeking to predict economic indicators in Scandinavia, or other small, open economies. Similarly, our findings can help to better understand how companies react to changes in policy uncertainty.
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Globalization drives the need to properly understand policy uncertainty and how it affects both the economy in general and business conditions. To systematically investigate the effect of policy uncertainty on small, open economies, we develop a policy uncertainty index based on newspaper content for each of the three Scandinavian countries; Norway, Denmark and Sweden. We show how these indices capture important historical events, both local events such as referendums and certain general elections, but also global events such as financial crises. Our narrative validation provides evidence that the three indices are good measures of policy uncertainty. Further, we compare historical policy uncertainty in the Scandinavian countries to a similar index for the US, before analysing the effect of both local and US policy uncertainty on the Scandinavian economies. Our findings indicate that increased policy uncertainty both at home and in the US leads to economic contraction, a significant decline in stock markets and a long-lasting reduction in the Scandinavian countries’ Purchasing Managers’ Index. These results can be highly relevant for anyone seeking to predict economic indicators in Scandinavia, or other small, open economies. Similarly, our findings can help to better understand how companies react to changes in policy uncertainty.
Social Media, Financial Reporting Opacity, and Return Co-movement: Evidence From Seeking Alpha
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In this study, we develop a model to analyze the interplay between the coverage of a firm on social media, financial reporting opacity, and stock return co-movement. Our model predicts a negative association between social media coverage and co-movement as social media facilitates the incorporation of firm-specific information into stock price. It also predicts that the effect of social media coverage on co-movement is more pronounced among firms with higher financial reporting opacity. Using data from Seeking Alpha, the largest crowd-sourced social media platform that provides “third-party generated†financial analysis in US, we find results consistent with the model’s predictions.
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In this study, we develop a model to analyze the interplay between the coverage of a firm on social media, financial reporting opacity, and stock return co-movement. Our model predicts a negative association between social media coverage and co-movement as social media facilitates the incorporation of firm-specific information into stock price. It also predicts that the effect of social media coverage on co-movement is more pronounced among firms with higher financial reporting opacity. Using data from Seeking Alpha, the largest crowd-sourced social media platform that provides “third-party generated†financial analysis in US, we find results consistent with the model’s predictions.
Tail Risk Measurement in Crypto-Asset Markets
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The paper examines the relationships among market assets during stressful times, using two recently proposed econometric modeling techniques for tail risk measurement: the extreme downside hedge (EDH) and the extreme downside correlation (EDC). We extend both measures taking into account the sensitivity of asset's return to innovations not only from the overall market index, but also from its components, by means of network modelling. Applying our proposal to the cryptocurrencies market, we find that crypto-assets can be clustered in two groups: speculative assets, such as Bitcoin, which are mainly "givers" of tail contagion; and technical assets, such as Ethereum, which are mainly "receivers" of contagion.
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The paper examines the relationships among market assets during stressful times, using two recently proposed econometric modeling techniques for tail risk measurement: the extreme downside hedge (EDH) and the extreme downside correlation (EDC). We extend both measures taking into account the sensitivity of asset's return to innovations not only from the overall market index, but also from its components, by means of network modelling. Applying our proposal to the cryptocurrencies market, we find that crypto-assets can be clustered in two groups: speculative assets, such as Bitcoin, which are mainly "givers" of tail contagion; and technical assets, such as Ethereum, which are mainly "receivers" of contagion.
What is your Desire? Retail Investor Preferences in Structured Products
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We conduct a choice-based conjoint analysis to evaluate the preferences of private investors with regard to the investment in structured products, especially discount certificates. Investors consider the costs and the product structure to be most important, whereas the product’s issuer and information on risk are of less interest. Their preferences depend on their (self-evaluated) expertise: while inexperienced retail investors concentrate on costs, experienced investors pay more attention to the product structure. Furthermore, buyers requesting further information on discount certificates before investing leads to an increase in the relative importance of product structure as a factor in their choice.
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We conduct a choice-based conjoint analysis to evaluate the preferences of private investors with regard to the investment in structured products, especially discount certificates. Investors consider the costs and the product structure to be most important, whereas the product’s issuer and information on risk are of less interest. Their preferences depend on their (self-evaluated) expertise: while inexperienced retail investors concentrate on costs, experienced investors pay more attention to the product structure. Furthermore, buyers requesting further information on discount certificates before investing leads to an increase in the relative importance of product structure as a factor in their choice.
When the Blockchain Does Not Block: On Hackings and Uncertainty in the Cryptocurrency Market
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A total of 1.1 million bitcoin were stolen in the 2013â€"2017 period. Noting that the average price for Bitcoin in 2018 was USD 7,572 the corresponding monetary equivalent of losses is USD 8.9 billion which strongly shows the societal impact of this criminal activity. Investigating the response of the uncertainty of Bitcoin when hacking incidents occur, the results of this study point toward a delayed response in volatility. The volatility increases significantly only at day Ï„+ 5. Incidents of hacking that occur in the Bitcoin market affect uncertainty for another cryptocurrency Ethereum too. Again, the evidence suggests a delayed response. However, Bitcoin and Ethereum do not exhibit asymmetric responses to negative innovations.
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A total of 1.1 million bitcoin were stolen in the 2013â€"2017 period. Noting that the average price for Bitcoin in 2018 was USD 7,572 the corresponding monetary equivalent of losses is USD 8.9 billion which strongly shows the societal impact of this criminal activity. Investigating the response of the uncertainty of Bitcoin when hacking incidents occur, the results of this study point toward a delayed response in volatility. The volatility increases significantly only at day Ï„+ 5. Incidents of hacking that occur in the Bitcoin market affect uncertainty for another cryptocurrency Ethereum too. Again, the evidence suggests a delayed response. However, Bitcoin and Ethereum do not exhibit asymmetric responses to negative innovations.
