Research articles for the 2019-03-21
A Model for Indirect Losses of Negatives Shocks in Production and Finance
SSRN
The Japanese economy has been frequently affected by natural disasters and domestic and overseas financial crises. These events cause production disruption and several other economic losses including the negative impacts on the banking system. The understanding of the transmission mechanism that causes various negative second-order effects post-catastrophe is crucial for policymakers in order to define more efficient strategies for recovery. We introduce in this work the credit-based adaptive regional input-output (ARIO) model to analyze the effects of disaster and crisis on the supply chain and the bank-firm credit networks. Using the exogenous shocks of the 2008 Lehman Brothers bankruptcy and the Great East Japan Earthquake (March 11, 2011), this paper has three research objectives. First, investigate how Japanese listed firms' strategies differ between financial crisis and natural disaster. Second, show how exogenous shocks related to those catastrophic events are transferred to the Japanese banks through the bank-firm network. Third, investigate the most vulnerable Japanese industrial sectors and Japanese geographic locations when hit by a financial crisis and natural disaster, respectively. The model is initialized based on the real supplier-customer network of Japanese listed firms, the real borrowing-lending network of Japanese banks and Japanese listed firms, and the financial statements of these firms. The credit-based ARIO model is calibrated using the Latin hypercube sampling and the design of experiments to reproduce the short-term (one year) dynamics of the index of industrial production of Japan post the 2008 Lehman Brothers bankruptcy and the 2011 Great earthquake in Japan. We reproduced by simulation the Japanese economic dynamics post these two disastrous events. We show how the disruption of production caused by financial crisis or natural disaster is transferred to the banking system by generating additional non-performing loans and liquidity pressure. Then, by simulation experiments we identify the most vulnerable Japanese industrial sectors as the chemical and petroleum manufacturing, and the transport, since those sectors are damaged by a crisis similar to the 2008 financial crisis. The impact on the global economy is measured and the systemic risk of industrial sectors is explained by their position in the supply chain network. Finally, the scenario of the 2011 Great earthquake is simulated on Japanese prefectures to understand differences between regions in terms of global engendered indirect economic losses. Tokyo and Osaka prefectures are the vulnerable geographic locations because of the higher concentration of those vulnerable industrial sectors.
SSRN
The Japanese economy has been frequently affected by natural disasters and domestic and overseas financial crises. These events cause production disruption and several other economic losses including the negative impacts on the banking system. The understanding of the transmission mechanism that causes various negative second-order effects post-catastrophe is crucial for policymakers in order to define more efficient strategies for recovery. We introduce in this work the credit-based adaptive regional input-output (ARIO) model to analyze the effects of disaster and crisis on the supply chain and the bank-firm credit networks. Using the exogenous shocks of the 2008 Lehman Brothers bankruptcy and the Great East Japan Earthquake (March 11, 2011), this paper has three research objectives. First, investigate how Japanese listed firms' strategies differ between financial crisis and natural disaster. Second, show how exogenous shocks related to those catastrophic events are transferred to the Japanese banks through the bank-firm network. Third, investigate the most vulnerable Japanese industrial sectors and Japanese geographic locations when hit by a financial crisis and natural disaster, respectively. The model is initialized based on the real supplier-customer network of Japanese listed firms, the real borrowing-lending network of Japanese banks and Japanese listed firms, and the financial statements of these firms. The credit-based ARIO model is calibrated using the Latin hypercube sampling and the design of experiments to reproduce the short-term (one year) dynamics of the index of industrial production of Japan post the 2008 Lehman Brothers bankruptcy and the 2011 Great earthquake in Japan. We reproduced by simulation the Japanese economic dynamics post these two disastrous events. We show how the disruption of production caused by financial crisis or natural disaster is transferred to the banking system by generating additional non-performing loans and liquidity pressure. Then, by simulation experiments we identify the most vulnerable Japanese industrial sectors as the chemical and petroleum manufacturing, and the transport, since those sectors are damaged by a crisis similar to the 2008 financial crisis. The impact on the global economy is measured and the systemic risk of industrial sectors is explained by their position in the supply chain network. Finally, the scenario of the 2011 Great earthquake is simulated on Japanese prefectures to understand differences between regions in terms of global engendered indirect economic losses. Tokyo and Osaka prefectures are the vulnerable geographic locations because of the higher concentration of those vulnerable industrial sectors.
A Quantitative Analysis of Countercyclical Capital Buffers
SSRN
This paper analyzes the effects of countercyclical capital buffers (CCyB) in a nonlinear DSGE model with a financial sector that is subject to occasional panics. The model is combined with data to estimate sequences of structural shocks and study policy counterfactuals. First, I show that lowering capital buffers during a crisis can moderate the intensity of the crisis. Second, I show that raising capital buffers during leverage expansions can reduce the frequency of crises by more than half. A quantitative application to the 2008 financial crisis shows that CCyB in the ±2% range (as in the Federal Reserve’s current framework) could have greatly mitigated the financial panic in 2007Q4-2008Q4. These findings suggest that CCyB are a useful policy tool both ex-ante and ex-post.
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This paper analyzes the effects of countercyclical capital buffers (CCyB) in a nonlinear DSGE model with a financial sector that is subject to occasional panics. The model is combined with data to estimate sequences of structural shocks and study policy counterfactuals. First, I show that lowering capital buffers during a crisis can moderate the intensity of the crisis. Second, I show that raising capital buffers during leverage expansions can reduce the frequency of crises by more than half. A quantitative application to the 2008 financial crisis shows that CCyB in the ±2% range (as in the Federal Reserve’s current framework) could have greatly mitigated the financial panic in 2007Q4-2008Q4. These findings suggest that CCyB are a useful policy tool both ex-ante and ex-post.
Common-Ownership and Portfolio Rebalancing
SSRN
The empirical literature on the potential collusive effects of common-ownership relies heavily on financial institution mergers to make causal inferences. I find that more than 85% of newly-formed common-ownership relationships due to such financial institution mergers are no longer commonly-held by the acquiring institution during the post-merger period (with most being liquidated in the first quarter following the merger). Firms that are no longer commonly-held by the merged institution drive the anti-competitive results found in previous studies. The fact that portfolio firms are so quickly rebalanced casts doubt on the utility of financial institution mergers as a natural experiment. I also find evidence that portfolio rebalancing post-merger is driven by other factors, such as portfolio diversification or index tracking. Further, I find no significant positive risk-adjusted returns for a common-ownership based portfolio strategy, suggesting that investors do not make a profit from commonly-held stocks. Taken together, these findings suggest that empirical basis for claiming collusive effects of common-ownership is weaker than it appears and there is no strong evidence that provides a basis for policy concerns about institutional common-ownership.
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The empirical literature on the potential collusive effects of common-ownership relies heavily on financial institution mergers to make causal inferences. I find that more than 85% of newly-formed common-ownership relationships due to such financial institution mergers are no longer commonly-held by the acquiring institution during the post-merger period (with most being liquidated in the first quarter following the merger). Firms that are no longer commonly-held by the merged institution drive the anti-competitive results found in previous studies. The fact that portfolio firms are so quickly rebalanced casts doubt on the utility of financial institution mergers as a natural experiment. I also find evidence that portfolio rebalancing post-merger is driven by other factors, such as portfolio diversification or index tracking. Further, I find no significant positive risk-adjusted returns for a common-ownership based portfolio strategy, suggesting that investors do not make a profit from commonly-held stocks. Taken together, these findings suggest that empirical basis for claiming collusive effects of common-ownership is weaker than it appears and there is no strong evidence that provides a basis for policy concerns about institutional common-ownership.
ESG: Fact vs. Belief
SSRN
The paper examines the investment proposition of a scoring-based ESG investing in the context of portfolio allocation. We assess the theme on the basic idea of investing, viz: return enhancement and/or diversification. At the same time, the paper also argues on some of the subjective aspects which merit an in-depth discussion and debate. This is a returns-based analysis which compares an off-the-shelf ESG component to the broad market and also examines its impact on its inclusion in a typical balanced portfolio. The analyses show that ESG has not been able to add any returns in absolute terms as well as on a risk-adjusted basis. We also evaluated the ability of ESG to protect on the downside given its claims of providing higher quality tilt. Historical data do not suggest any such benefits and ESG demonstrates similar drawdowns as traditional equities during risk off periods. We, therefore, do not see much merit in following a ratings-based mechanism for ESG investing. Rather, emphasis should be laid on how to engage with and improve a firm come up the ESG spectrum over time and potentially participate in its upside as investors.
SSRN
The paper examines the investment proposition of a scoring-based ESG investing in the context of portfolio allocation. We assess the theme on the basic idea of investing, viz: return enhancement and/or diversification. At the same time, the paper also argues on some of the subjective aspects which merit an in-depth discussion and debate. This is a returns-based analysis which compares an off-the-shelf ESG component to the broad market and also examines its impact on its inclusion in a typical balanced portfolio. The analyses show that ESG has not been able to add any returns in absolute terms as well as on a risk-adjusted basis. We also evaluated the ability of ESG to protect on the downside given its claims of providing higher quality tilt. Historical data do not suggest any such benefits and ESG demonstrates similar drawdowns as traditional equities during risk off periods. We, therefore, do not see much merit in following a ratings-based mechanism for ESG investing. Rather, emphasis should be laid on how to engage with and improve a firm come up the ESG spectrum over time and potentially participate in its upside as investors.
Epstein-Zin Utility Maximization on Random Horizons
arXiv
arXiv
This paper solves the consumption-investment problem with Epstein-Zin utility on a random horizon. In an incomplete market, we take the random horizon to be a stopping time adapted to the market filtration, generated by all observable, but not necessarily tradable, state processes. Contrary to prior studies, we do not impose any fixed upper bound for the random horizon, allowing for truly unbounded ones. Focusing on the empirically relevant case where the risk aversion and the elasticity of intertemporal substitution are both larger than one, we characterize optimal consumption and investment strategies through backward stochastic differential equations (BSDEs). Compared with classical results on a fixed horizon, our characterization involves an additional stochastic process to account for the uncertainty of the horizon. As demonstrated in a Markovian setting, this added uncertainty drastically alters optimal strategies from the fixed-horizon case. The main results are obtained through developing new techniques for BSDEs with superlinear growth on an unbounded random horizon.
Expectations During the U.S. Housing Boom: Inferring Beliefs from Actions
SSRN
We assess the role of price expectations in forming the U.S. housing boom in the mid-2000s by studying the dynamics of vacant properties. When agents anticipate price increases, they amass excess capacity. Thus, housing vacancy discriminates between price movements related to shocks to demand for housing services (low vacancy) and expectation shocks (high vacancy). We implement this idea using a structural vector autoregression with sign restrictions. In the aggregate, expectation shocks are the most important factor explaining the boom, immediately followed by mortgage rate shocks. In the cross-section, expectation shocks are the major factor explaining price movements in the Sand States, which experienced unprecedented booms.
SSRN
We assess the role of price expectations in forming the U.S. housing boom in the mid-2000s by studying the dynamics of vacant properties. When agents anticipate price increases, they amass excess capacity. Thus, housing vacancy discriminates between price movements related to shocks to demand for housing services (low vacancy) and expectation shocks (high vacancy). We implement this idea using a structural vector autoregression with sign restrictions. In the aggregate, expectation shocks are the most important factor explaining the boom, immediately followed by mortgage rate shocks. In the cross-section, expectation shocks are the major factor explaining price movements in the Sand States, which experienced unprecedented booms.
Expected exponential utility maximization of insurers with a general diffusion factor model : The complete market case
arXiv
arXiv
In this paper, we consider the problem of optimal investment by an insurer. The insurer invests in a market consisting of a bank account and $m$ risky assets. The mean returns and volatilities of the risky assets depend nonlinearly on economic factors that are formulated as the solutions of general stochastic differential equations. The wealth of the insurer is described by a Cram\'er--Lundberg process, and the insurer preferences are exponential. Adapting a dynamic programming approach, we derive Hamilton--Jacobi--Bellman (HJB) equation. And, we prove the unique solvability of HJB equation. In addition, the optimal strategy is also obtained using the coupled forward and backward stochastic differential equations (FBSDEs). Finally, proving the verification theorem, we construct the optimal strategy.
IRS Announces First Wave of Opportunity Zone Designations
SSRN
In this article, Wallwork discusses Treasury’s first round of designations of qualified opportunity zones.
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In this article, Wallwork discusses Treasury’s first round of designations of qualified opportunity zones.
Nonperforming Loans in Asia: Determinants and Macrofinancial Linkages
SSRN
The recent rise of nonperforming loans (NPLs) in some Asian economies calls for close analysis of the determinants, the potential macrofinancial feedback effects, and the implications for financial stability in the region. Using a dynamic panel model, we assess the determinants of the evolution of bank-specific NPLs in Asia and find that macroeconomic conditions and bank-specific factors such as rapid credit growth and excessive bank lendingâ€"contribute to the buildup of NPLs. Further, a panel vector autoregression analysis of macrofinancial implications of NPLs in emerging Asia offers significant evidence for the feedback effects of NPLs on the real economy and financial variables. Impulse response functions demonstrate that a rising NPL ratio decreases gross domestic product growth and credit supply and increases unemployment rate. Our findings underline the importance of considering policy options to swiftly and effectively manage and respond to a buildup of NPLs. The national and regional mechanisms underlying NPL resolution are important for safeguarding financial stability in an increasingly interconnected global financial system.
SSRN
The recent rise of nonperforming loans (NPLs) in some Asian economies calls for close analysis of the determinants, the potential macrofinancial feedback effects, and the implications for financial stability in the region. Using a dynamic panel model, we assess the determinants of the evolution of bank-specific NPLs in Asia and find that macroeconomic conditions and bank-specific factors such as rapid credit growth and excessive bank lendingâ€"contribute to the buildup of NPLs. Further, a panel vector autoregression analysis of macrofinancial implications of NPLs in emerging Asia offers significant evidence for the feedback effects of NPLs on the real economy and financial variables. Impulse response functions demonstrate that a rising NPL ratio decreases gross domestic product growth and credit supply and increases unemployment rate. Our findings underline the importance of considering policy options to swiftly and effectively manage and respond to a buildup of NPLs. The national and regional mechanisms underlying NPL resolution are important for safeguarding financial stability in an increasingly interconnected global financial system.
On the Preferences of Coco Bond Buyers and Sellers: A Logistic Regression Analysis
SSRN
This paper estimates the preference scores of CoCo bond buyers and sellers by running logistic regressions taking into account both bond and issuing bank's characteristics, and also considers the role of country−specific CoCo bond market competitiveness. Buyers are found to be characterised by stronger preference responses to CoCo bond coupons and credit ratings, while sellers are more sensitive to CoCo bond issue size and financial characteristics including return on common equity, price−to−book ratio and total regulatory capital to risk−weighted asset ratio. Further, sizeable responses to CoCo bond and issuing bank's characteristics are found in most European countries, Brazil, Mexico and China, the strongest responses being estimated in the case of the UK and China.
SSRN
This paper estimates the preference scores of CoCo bond buyers and sellers by running logistic regressions taking into account both bond and issuing bank's characteristics, and also considers the role of country−specific CoCo bond market competitiveness. Buyers are found to be characterised by stronger preference responses to CoCo bond coupons and credit ratings, while sellers are more sensitive to CoCo bond issue size and financial characteristics including return on common equity, price−to−book ratio and total regulatory capital to risk−weighted asset ratio. Further, sizeable responses to CoCo bond and issuing bank's characteristics are found in most European countries, Brazil, Mexico and China, the strongest responses being estimated in the case of the UK and China.
Stress Testing Made Easy: No More US Banks Stumbling and Facing Public Embarrassment Due to the Federal Reserve’s Qualitative Objection
SSRN
The worst two financial crises in human history were in some ways attributable to the US Federal Reserve’s misguided monetary policies. Many economists share the view that the Fed’s tight-money policy in the late 1920s caused a significant drop in the money stock (i.e. severe contraction) which triggered the 1929 stock market crash and the subsequent Great Depression â€" the worst financial catastrophe of the 20th century. Close to a century later, the Federal Reserve was on the scene again, this time economists argue that the Fed’s expansive monetary policy since the late 1990s created an easy-credit environment (global dollar glut) which induced banks to expand credit into sub-prime segment turning ordinary folks into avid buyers (i.e. asset-price boom). Consequently, the Fed’s policy errors along with unfolding contributing and driving forces led to the 2006 mortgage debacle in the U.S., subsequently an ordinary looking recession was turned into the inevitable 2008 global financial crisis (GFC) â€" the worst financial catastrophe of the 21st century. The Trump administration wants to reverse the bank regulation rules that were put in place following the GFC; last time, President Clinton repealed the Glass-Steagall Act of 1933 by signing into law the 1999 Gramm-Leach Bliley Act, which fostered the boom-and-bust of the dot.com and housing bubbles, and the eventual GFC. The crises in the new millennium have cost investors over $30 trillion and pushed millions of people into poverty. Stress testing, a simulation technique used by individual banks, supervisory community, and central banks to safeguard financial stability, has evolved to become a crisis-management tool. Stress testing is neither a standalone tool nor an early-warning mechanism, but it is indispensable in the macroeconomic toolkit when used as a complement not a supplement to other tools such as VaR. To ease up the rules of regulation that was put in place after a near financial meltdown in the U.S. would be a fatal mistake by the Trump administration, even if the goal is to favor US’ largest banks.
SSRN
The worst two financial crises in human history were in some ways attributable to the US Federal Reserve’s misguided monetary policies. Many economists share the view that the Fed’s tight-money policy in the late 1920s caused a significant drop in the money stock (i.e. severe contraction) which triggered the 1929 stock market crash and the subsequent Great Depression â€" the worst financial catastrophe of the 20th century. Close to a century later, the Federal Reserve was on the scene again, this time economists argue that the Fed’s expansive monetary policy since the late 1990s created an easy-credit environment (global dollar glut) which induced banks to expand credit into sub-prime segment turning ordinary folks into avid buyers (i.e. asset-price boom). Consequently, the Fed’s policy errors along with unfolding contributing and driving forces led to the 2006 mortgage debacle in the U.S., subsequently an ordinary looking recession was turned into the inevitable 2008 global financial crisis (GFC) â€" the worst financial catastrophe of the 21st century. The Trump administration wants to reverse the bank regulation rules that were put in place following the GFC; last time, President Clinton repealed the Glass-Steagall Act of 1933 by signing into law the 1999 Gramm-Leach Bliley Act, which fostered the boom-and-bust of the dot.com and housing bubbles, and the eventual GFC. The crises in the new millennium have cost investors over $30 trillion and pushed millions of people into poverty. Stress testing, a simulation technique used by individual banks, supervisory community, and central banks to safeguard financial stability, has evolved to become a crisis-management tool. Stress testing is neither a standalone tool nor an early-warning mechanism, but it is indispensable in the macroeconomic toolkit when used as a complement not a supplement to other tools such as VaR. To ease up the rules of regulation that was put in place after a near financial meltdown in the U.S. would be a fatal mistake by the Trump administration, even if the goal is to favor US’ largest banks.
Stylized Facts on Price Formation on Corporate Bonds and Best Execution Analysis
arXiv
arXiv
The goal of this paper is to establish a benchmark for transaction cost analysis in bond trading for retail investors. Investors can use this benchmark to improve decisions when requesting quotes from dealers on electronic platforms. This benchmark is constructed in two steps. The first step is to identify abnormal trades by approximating the market liquidity using different statistical regression methods: OLS, two-step LASSO and Elastic Net. The second step is to estimate the amplitude and the decay pattern of price impact using non-parametric methods. A key discovery is the price impact asymmetry between customer-buy orders and costumer sell orders: customer buy orders have a larger price impact than customer sell orders. We show that this asymmetry is statistically significant.
The China Syndrome Affects Banks: The Credit Supply Channel of Foreign Import Competition
SSRN
We study the effect of rising Chinese import competition in the early 2000s on banks’ credit supply policies. Using bank-firm-level data on the universe of Spanish corporate loans, we exploit heterogeneity across banks in the exposure of their loan portfolios towards firms competing with Chinese imports. Exposed banks rebalanced their loan portfolios by cutting the supply of credit to firms affected by Chinese competition, while raising their lending towards non-exposed sectors. This portfolio reallocation depressed further the economic activity of firms competing with Chinese imports.
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We study the effect of rising Chinese import competition in the early 2000s on banks’ credit supply policies. Using bank-firm-level data on the universe of Spanish corporate loans, we exploit heterogeneity across banks in the exposure of their loan portfolios towards firms competing with Chinese imports. Exposed banks rebalanced their loan portfolios by cutting the supply of credit to firms affected by Chinese competition, while raising their lending towards non-exposed sectors. This portfolio reallocation depressed further the economic activity of firms competing with Chinese imports.
The Effect of Spatial Competition on Rural Banks Stability: Contagion Effect
SSRN
This paper examines the effect of competition on bank stability particularly the contagion effect in rural banking market using spatial panel econometrics methodology. We obtain quarterly data from 2014 to 2017 focusing in a single country, Indonesia, as it has the largest number of rural banks in Asia and divides the analysis into 33 provinces. The efficiency-adjusted Lerner index is used to measure the market power of the banks and this index shows that banks have a mark-up price. In terms of control variables, we use bank-specific characteristics to proxy size, capital, and liquidity. Provincial macroeconomic indicators such as the growth of GDP, inflation and foreign exchange rate are also used in the present study. We construct the spatial variables as inverse distance and area of rural bank market per provinces. The contagion effect is measured by the indirect effect of two different channels: geographical spillover and rural banks zone based on FSA. The results reveal that there is a negative relationship between bank stability and market power which supports the competition-fragility hypothesis. The contagion effect does exist in a market that has the same zone categories (local area). In conclusion, we argue that the rural bank stability depends on the level of spatial competition.
SSRN
This paper examines the effect of competition on bank stability particularly the contagion effect in rural banking market using spatial panel econometrics methodology. We obtain quarterly data from 2014 to 2017 focusing in a single country, Indonesia, as it has the largest number of rural banks in Asia and divides the analysis into 33 provinces. The efficiency-adjusted Lerner index is used to measure the market power of the banks and this index shows that banks have a mark-up price. In terms of control variables, we use bank-specific characteristics to proxy size, capital, and liquidity. Provincial macroeconomic indicators such as the growth of GDP, inflation and foreign exchange rate are also used in the present study. We construct the spatial variables as inverse distance and area of rural bank market per provinces. The contagion effect is measured by the indirect effect of two different channels: geographical spillover and rural banks zone based on FSA. The results reveal that there is a negative relationship between bank stability and market power which supports the competition-fragility hypothesis. The contagion effect does exist in a market that has the same zone categories (local area). In conclusion, we argue that the rural bank stability depends on the level of spatial competition.
The Impact of Lending Standards on Default Rates of Residential Real-Estate Loans
SSRN
This paper analyses the impact of lending standards for residential real estate (RRE) loans on default rates, using a novel loan-level dataset from the European DataWarehouse (EDW) that covers eight euro area countries. To the best of the authors’ knowledge, this paper is the first to use, for this purpose, a consistent set of loan-level data on loans originated in multiple euro area countries. Previous literature has used either national loan-level data, which does not allow for cross-country comparisons, or aggregate cross-country data. The dataset is first explored through an extensive descriptive analysis and this is followed by static probit regressions. The findings confirm the key influence of lending standards â€" in particular, loan-to-value and loan-to-income ratios at origination, original loan maturity and borrower employment status â€" on loan default rates. The impact of other variables, such as interest rate fixation and payment type, varies depending on the country of loan origination. These results are particularly relevant for microprudential supervisors in their ongoing assessment of banks’ credit policies. The highlighted country specificities should be taken into account in macroprudential policymaking.
SSRN
This paper analyses the impact of lending standards for residential real estate (RRE) loans on default rates, using a novel loan-level dataset from the European DataWarehouse (EDW) that covers eight euro area countries. To the best of the authors’ knowledge, this paper is the first to use, for this purpose, a consistent set of loan-level data on loans originated in multiple euro area countries. Previous literature has used either national loan-level data, which does not allow for cross-country comparisons, or aggregate cross-country data. The dataset is first explored through an extensive descriptive analysis and this is followed by static probit regressions. The findings confirm the key influence of lending standards â€" in particular, loan-to-value and loan-to-income ratios at origination, original loan maturity and borrower employment status â€" on loan default rates. The impact of other variables, such as interest rate fixation and payment type, varies depending on the country of loan origination. These results are particularly relevant for microprudential supervisors in their ongoing assessment of banks’ credit policies. The highlighted country specificities should be taken into account in macroprudential policymaking.
The Impact of Regulation and Transparency in the Cryptocurrency Market
SSRN
Cryptocurrencies are mostly issued and traded outside traditional regulated financial markets. Financial regulators and governments have started advocating for regulation of cryptocurrency markets, but regulatory efforts so far lack a comprehensive approach due to the ambiguous nature of cryptocurrencies. We empirically examine if cryptocurrency investors perceive regulation as net beneficial or costly, and whether cross-sectional differences in cryptocurrency characteristics and transparency affect this reaction. Using a sample of around 1,300 cryptocurrencies and 148 regulation news events, we find that on average, investors react negatively to events that increase the likelihood of regulation. We find that the negative reaction is mainly driven by news about securities regulation of ICOs and cryptocurrency exchanges. However, the negative reaction is less pronounced for cryptocurrencies with higher expert ratings for transparency, management competence and the underlying business idea, and for those cryptocurrencies that engage more with followers on social media. We conclude that cryptocurrency investors expect regulation to be costly on average, but less so for cryptocurrencies with a better information environment or that are better positioned to deal with changes in regulatory requirements.
SSRN
Cryptocurrencies are mostly issued and traded outside traditional regulated financial markets. Financial regulators and governments have started advocating for regulation of cryptocurrency markets, but regulatory efforts so far lack a comprehensive approach due to the ambiguous nature of cryptocurrencies. We empirically examine if cryptocurrency investors perceive regulation as net beneficial or costly, and whether cross-sectional differences in cryptocurrency characteristics and transparency affect this reaction. Using a sample of around 1,300 cryptocurrencies and 148 regulation news events, we find that on average, investors react negatively to events that increase the likelihood of regulation. We find that the negative reaction is mainly driven by news about securities regulation of ICOs and cryptocurrency exchanges. However, the negative reaction is less pronounced for cryptocurrencies with higher expert ratings for transparency, management competence and the underlying business idea, and for those cryptocurrencies that engage more with followers on social media. We conclude that cryptocurrency investors expect regulation to be costly on average, but less so for cryptocurrencies with a better information environment or that are better positioned to deal with changes in regulatory requirements.
Three Different Ways Synchronization Can Cause Contagion in Financial Markets
SSRN
We introduce tools to capture the dynamics of three different pathways, in which the synchronization of human decision-making could lead to turbulent periods and contagion phenomena in financial markets. The first pathway is caused when stock market indices, seen as a set of coupled integrate-and-fire oscillators, synchronize in frequency. The integrate-and-fire dynamics happens due to “change blindnessâ€, a trait in human decision-making where people have the tendency to ignore small changes, but take action when a large change happens. The second pathway happens due to feedback mechanisms between market performance and the use of certain (decoupled) trading strategies. The third pathway occurs through the effects of communication and its impact on human decision-making. A model is introduced in which financial market performance has an impact on decision-making through communication between people. Conversely, the sentiment created via communication has an impact on financial market performance. The methodologies used are: agent based modeling, models of integrate-and-fire oscillators, and communication models of human decision-making.
SSRN
We introduce tools to capture the dynamics of three different pathways, in which the synchronization of human decision-making could lead to turbulent periods and contagion phenomena in financial markets. The first pathway is caused when stock market indices, seen as a set of coupled integrate-and-fire oscillators, synchronize in frequency. The integrate-and-fire dynamics happens due to “change blindnessâ€, a trait in human decision-making where people have the tendency to ignore small changes, but take action when a large change happens. The second pathway happens due to feedback mechanisms between market performance and the use of certain (decoupled) trading strategies. The third pathway occurs through the effects of communication and its impact on human decision-making. A model is introduced in which financial market performance has an impact on decision-making through communication between people. Conversely, the sentiment created via communication has an impact on financial market performance. The methodologies used are: agent based modeling, models of integrate-and-fire oscillators, and communication models of human decision-making.
What Went Wrong?: The Puerto Rican Debt Crisis, the
SSRN
What went wrong? Why did seemingly rational bond investors continue to purchase Puerto Rican debt with only a modest risk premium, even though the macroeconomic fundamentals were dismal? Why did financial markets fail to exercise market discipline and restrict capital flows to Puerto Rico? Given gloomy macroeconomic fundamentals and relatively low risk premia, investors were either stunningly myopic/misinformed, or Puerto Rican debt was implicitly insured by the U.S. government. This paper examines the latter hypothesis, which we label the "Treasury Put." The expectation of a federal bailout was perfectly reasonable given past behavior by the federal government, starting with the prior bailout of the city of New York. Evaluating the Treasury Put hypothesis with a minimal set of assumptions is possible given three unique features – the dire fiscal and economic conditions in Puerto Rico, a fortunate characteristic of Puerto Rican bond issuance, and a "seismic shock." Regarding the second feature, Puerto Rico issued both uninsured and insured general obligation bonds on the same day and, in many cases, with the exact same maturity. The associated bond price data allow for an accurate computation of the risk premia on Puerto Rican bonds. The third feature is the non-bailout of the city of Detroit in 2013 that effectively extinguished the Treasury Put. Puerto Rican risk premia were stable before the Detroit bankruptcy and bracketed by the risk premia on Corporate Aaa and Baa bonds. However, after the Detroit bankruptcy, risk premia rose dramatically, thus identifying a sizeable Treasury Put of at least 300 basis points and a significant misallocation of capital to Puerto Rico. In effect, the Treasury Put was a form of regulatory forbearance. Institutional reforms that would eliminate the Treasury Put are considered, but none are found satisfactory.
SSRN
What went wrong? Why did seemingly rational bond investors continue to purchase Puerto Rican debt with only a modest risk premium, even though the macroeconomic fundamentals were dismal? Why did financial markets fail to exercise market discipline and restrict capital flows to Puerto Rico? Given gloomy macroeconomic fundamentals and relatively low risk premia, investors were either stunningly myopic/misinformed, or Puerto Rican debt was implicitly insured by the U.S. government. This paper examines the latter hypothesis, which we label the "Treasury Put." The expectation of a federal bailout was perfectly reasonable given past behavior by the federal government, starting with the prior bailout of the city of New York. Evaluating the Treasury Put hypothesis with a minimal set of assumptions is possible given three unique features – the dire fiscal and economic conditions in Puerto Rico, a fortunate characteristic of Puerto Rican bond issuance, and a "seismic shock." Regarding the second feature, Puerto Rico issued both uninsured and insured general obligation bonds on the same day and, in many cases, with the exact same maturity. The associated bond price data allow for an accurate computation of the risk premia on Puerto Rican bonds. The third feature is the non-bailout of the city of Detroit in 2013 that effectively extinguished the Treasury Put. Puerto Rican risk premia were stable before the Detroit bankruptcy and bracketed by the risk premia on Corporate Aaa and Baa bonds. However, after the Detroit bankruptcy, risk premia rose dramatically, thus identifying a sizeable Treasury Put of at least 300 basis points and a significant misallocation of capital to Puerto Rico. In effect, the Treasury Put was a form of regulatory forbearance. Institutional reforms that would eliminate the Treasury Put are considered, but none are found satisfactory.
