# Research articles for the 2020-11-28

CEO International Background and Cross-Border M&As

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We investigate whether having a CEO with international background affects U.S. firmsâ€™ cross-border merger and acquisition (M&A) activities. By defining international background as having: a non-U.S. nationality, overseas education, or overseas work-experience, we find that when a CEO has these characteristics, the firm is more likely to acquire international targets and these deals are more likely to be value-enhancing. Moreover, our results indicate that having multiple dimensions of international background (as opposed to one), for example, having both overseas education and work experience, increases the likelihood, and announcement returns of cross-border deals. The observed gains are largely from greater due diligence, lower acquisition premiums, and greater likelihood of financing the deal with stocks. We also find that firms typically undertake their first cross-border acquisition within five years of hiring a CEO with international background, suggesting that this may be a strategic decision of U.S. firms when they plan to expand their operations overseas.

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We investigate whether having a CEO with international background affects U.S. firmsâ€™ cross-border merger and acquisition (M&A) activities. By defining international background as having: a non-U.S. nationality, overseas education, or overseas work-experience, we find that when a CEO has these characteristics, the firm is more likely to acquire international targets and these deals are more likely to be value-enhancing. Moreover, our results indicate that having multiple dimensions of international background (as opposed to one), for example, having both overseas education and work experience, increases the likelihood, and announcement returns of cross-border deals. The observed gains are largely from greater due diligence, lower acquisition premiums, and greater likelihood of financing the deal with stocks. We also find that firms typically undertake their first cross-border acquisition within five years of hiring a CEO with international background, suggesting that this may be a strategic decision of U.S. firms when they plan to expand their operations overseas.

ContagionGraphs a Tool for Network-Based Models of Financial Contagion

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Financial contagion is explored in a large and growing number of network-based models with a high variety of assumptions on network geometry, bank balance sheets, loan settlement, etc. In this paper, we present â€œContagionGraphsâ€, a tool for researches to explore the seemingly endless possibilities in network-based models of financial contagion. We showcase its abilities by reproducing and extending on hallmark papers in the field such as Allen and Gale (2000), Nier et al. (2007) and Acemoglu et al. (2015). On top of the predominant analysis of average default ratios in average node degrees, we show that the net credit position of the shocked bank is a strong predictor of network stability and that equity and liquidity are substitutes for interbank network stability.

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Financial contagion is explored in a large and growing number of network-based models with a high variety of assumptions on network geometry, bank balance sheets, loan settlement, etc. In this paper, we present â€œContagionGraphsâ€, a tool for researches to explore the seemingly endless possibilities in network-based models of financial contagion. We showcase its abilities by reproducing and extending on hallmark papers in the field such as Allen and Gale (2000), Nier et al. (2007) and Acemoglu et al. (2015). On top of the predominant analysis of average default ratios in average node degrees, we show that the net credit position of the shocked bank is a strong predictor of network stability and that equity and liquidity are substitutes for interbank network stability.

Contrasting Incentives for Earnings Management: Board Activity and Board Remuneration in Spanish Firms

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We analyze the effect board activity and board remuneration has on earnings management (EM). Our results show that more active boards are inefficient in preventing earnings manipulation. Regarding board compensation we find a U-shaped relation indicating that excessive remuneration will lead to more earnings management. Policy recommendations are derived from the findings.

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We analyze the effect board activity and board remuneration has on earnings management (EM). Our results show that more active boards are inefficient in preventing earnings manipulation. Regarding board compensation we find a U-shaped relation indicating that excessive remuneration will lead to more earnings management. Policy recommendations are derived from the findings.

Credit Rating, Banks' Capital Structure and Speed of Adjustment: A Cross-Country Analysis

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Recent studies examining the effects of a credit rating on firmsâ€™ capital structure and adjustment of capital structure to target have focused predominantly on non-financial firms, with virtually no attention given to financial institutions. Using an international sample of 391 rated banks from 76 countries, this study examines the effects of credit ratings on the capital structure of banks. We find that, on average, banks near a credit rating upgrade have a higher capital to assets ratio compared to banks not near a rating upgrade. Most systematically important â€œtoo-big-to-failâ€ banks near a credit rating upgrade tend to have lower capital relative to assets than the rest of the banks in our sample. Furthermore, banks downgraded from an investment-grade rating to a speculative-grade rating, on average, hold 1 (3) percentage points less capital relative to assets in the short (long) run. Contrary to studies based on non-financial firms, our results show that credit ratings have relatively little economic effect on the speed at which banksâ€™ capital is adjusted. Our results suggest that while rating agencies exert influences on banksâ€™ capital structure, they are fewer in number and tend to be weaker, compared to those documented in non-financial firms.

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Recent studies examining the effects of a credit rating on firmsâ€™ capital structure and adjustment of capital structure to target have focused predominantly on non-financial firms, with virtually no attention given to financial institutions. Using an international sample of 391 rated banks from 76 countries, this study examines the effects of credit ratings on the capital structure of banks. We find that, on average, banks near a credit rating upgrade have a higher capital to assets ratio compared to banks not near a rating upgrade. Most systematically important â€œtoo-big-to-failâ€ banks near a credit rating upgrade tend to have lower capital relative to assets than the rest of the banks in our sample. Furthermore, banks downgraded from an investment-grade rating to a speculative-grade rating, on average, hold 1 (3) percentage points less capital relative to assets in the short (long) run. Contrary to studies based on non-financial firms, our results show that credit ratings have relatively little economic effect on the speed at which banksâ€™ capital is adjusted. Our results suggest that while rating agencies exert influences on banksâ€™ capital structure, they are fewer in number and tend to be weaker, compared to those documented in non-financial firms.

Crowded Ratings: Clientele Effects in the Corporate Bond Market

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Consistent with a simple model of market segmentation, we document rating-based clientele effects in the corporate bond market. Net capital flows that arise due to idiosyncratic firm upgrades and downgrades cause significant price movements for the other bonds in the effected rating bucket. A one-standard-deviation flow into a rating bucket generates a 5 bp bond price reduction, equivalent to 4.1% of the monthly price variation driven by macro variables. This effect is highly persistent, with an approximate half-life of five months. Guided by the model, we also document a significant decaying spillover pattern to bond prices in adjacent buckets.

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Consistent with a simple model of market segmentation, we document rating-based clientele effects in the corporate bond market. Net capital flows that arise due to idiosyncratic firm upgrades and downgrades cause significant price movements for the other bonds in the effected rating bucket. A one-standard-deviation flow into a rating bucket generates a 5 bp bond price reduction, equivalent to 4.1% of the monthly price variation driven by macro variables. This effect is highly persistent, with an approximate half-life of five months. Guided by the model, we also document a significant decaying spillover pattern to bond prices in adjacent buckets.

Intraday Volume-Return Nexus in Cryptocurrency Markets: A Novel Evidence From Cryptocurrency Classification

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This paper analyses the volume-return relationships across top 30 most traded cryptocurrencies from the April 2013 to June 2019 using a high-frequency intraday data. We use a novel approach for the classification of cryptocurrencies with respect to multiple qualitative factors, such as geographical location of headquarters, founder and founderâ€™s origin, platform on which cryptocurrency built on, consensus algorithm, to name but a few. We identified significant bidirectional causalities between trading volume and returns at high-frequency intervals, however, those linkages are disappearing with increased frequencies of data. The findings confirm the leading position of the Bitcoin trading volume in the cryptocurrency price formation. This evidence will help investors to design effective trading strategies in cryptocurrency market providing useful insights from cryptocurrency categorisation.

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This paper analyses the volume-return relationships across top 30 most traded cryptocurrencies from the April 2013 to June 2019 using a high-frequency intraday data. We use a novel approach for the classification of cryptocurrencies with respect to multiple qualitative factors, such as geographical location of headquarters, founder and founderâ€™s origin, platform on which cryptocurrency built on, consensus algorithm, to name but a few. We identified significant bidirectional causalities between trading volume and returns at high-frequency intervals, however, those linkages are disappearing with increased frequencies of data. The findings confirm the leading position of the Bitcoin trading volume in the cryptocurrency price formation. This evidence will help investors to design effective trading strategies in cryptocurrency market providing useful insights from cryptocurrency categorisation.

Loss Given Default Distributions in Different Countries: The Modality Defines the Estimation Method

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Estimating the credit risk parameter loss given default (LGD) is important for banks from an internal risk management and a regulatory perspective. Several estimation approaches are common in the literature and in practice. However, it remains unclear which approach leads to the highest estimation accuracy. In this regard, existing comparative studies in the literature come to different conclusions. The differences can be attributed to the specific choice of loan portfolio and, thus, to the specific choice of the LGD distribution. Against this background, we examine the estimation accuracy of various LGD estimation methods, including traditional regression and advanced machine learning. Our analysis is based on international loan portfolios of 16 European countries, with a total of 26, 227 defaulted loans of small and medium enterprises. Using a cluster analysis, we assign country-specific loan portfolios to three relevant modality types of LGD distributions. For each of these three types, we empirically determine the estimation method with the highest estimation accuracy.

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Estimating the credit risk parameter loss given default (LGD) is important for banks from an internal risk management and a regulatory perspective. Several estimation approaches are common in the literature and in practice. However, it remains unclear which approach leads to the highest estimation accuracy. In this regard, existing comparative studies in the literature come to different conclusions. The differences can be attributed to the specific choice of loan portfolio and, thus, to the specific choice of the LGD distribution. Against this background, we examine the estimation accuracy of various LGD estimation methods, including traditional regression and advanced machine learning. Our analysis is based on international loan portfolios of 16 European countries, with a total of 26, 227 defaulted loans of small and medium enterprises. Using a cluster analysis, we assign country-specific loan portfolios to three relevant modality types of LGD distributions. For each of these three types, we empirically determine the estimation method with the highest estimation accuracy.

Mutual Funds' Fire Sales and the Real Economy: Evidence from Hurricanes

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This paper contributes to the recent debate on whether nonfundamental price dislocations affect real economic activities, using a novel and economically-grounded approach. Hurricanes create liquidity demand from investors living in disaster zones. This translates into additional outflows for mutual funds in the areas affected by hurricanes of about $2.5 billions. Such outflows cause fire sales, which are followed by temporary price dislocations in stocks unrelated to the natural disaster (-7% reverted within 10 months). The nonfundamental price drop induces firms to re- duce investments by 4%. These results indicate that when the source of outflows is identified ex-ante and stems from investorsâ€™ liquidity needs unrelated to fund perfor- mance, the resulting nonfundamental price dislocations actually distort firmsâ€™ real decisions.

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This paper contributes to the recent debate on whether nonfundamental price dislocations affect real economic activities, using a novel and economically-grounded approach. Hurricanes create liquidity demand from investors living in disaster zones. This translates into additional outflows for mutual funds in the areas affected by hurricanes of about $2.5 billions. Such outflows cause fire sales, which are followed by temporary price dislocations in stocks unrelated to the natural disaster (-7% reverted within 10 months). The nonfundamental price drop induces firms to re- duce investments by 4%. These results indicate that when the source of outflows is identified ex-ante and stems from investorsâ€™ liquidity needs unrelated to fund perfor- mance, the resulting nonfundamental price dislocations actually distort firmsâ€™ real decisions.

Optimal Insurance under Maxmin Expected Utility

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We examine a problem of demand for insurance indemnification, when the insured is sensitive to ambiguity and behaves according to the Maxmin-Expected Utility model of Gilboa and Schmeidler (1989), whereas the insurer is a (risk-averse or risk-neutral) Expected-Utility maximizer. We characterize optimal indemnity functions both with and without the customary ex ante no-sabotage requirement on feasible indemnities, and for both concave and linear utility functions for the two agents. This allows us to provide a unifying framework in which we examine the effects of the no-sabotage condition, marginal utility of wealth, belief heterogeneity, as well as ambiguity (multiplicity of priors) on the structure of optimal indemnity functions. In particular, we show how the singularity in beliefs leads to an optimal indemnity function that involves full insurance on an event to which the insurer assigns zero probability, while the decision maker assigns a positive probability. We examine several illustrative examples, and we provide numerical studies for the case of a Wasserstein and a Renyi ambiguity set.

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We examine a problem of demand for insurance indemnification, when the insured is sensitive to ambiguity and behaves according to the Maxmin-Expected Utility model of Gilboa and Schmeidler (1989), whereas the insurer is a (risk-averse or risk-neutral) Expected-Utility maximizer. We characterize optimal indemnity functions both with and without the customary ex ante no-sabotage requirement on feasible indemnities, and for both concave and linear utility functions for the two agents. This allows us to provide a unifying framework in which we examine the effects of the no-sabotage condition, marginal utility of wealth, belief heterogeneity, as well as ambiguity (multiplicity of priors) on the structure of optimal indemnity functions. In particular, we show how the singularity in beliefs leads to an optimal indemnity function that involves full insurance on an event to which the insurer assigns zero probability, while the decision maker assigns a positive probability. We examine several illustrative examples, and we provide numerical studies for the case of a Wasserstein and a Renyi ambiguity set.

Option Pricing with Random Risk Aversion

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Based on a standard general equilibrium economy, we develop a framework for pricing European options where the risk aversion parameter is state dependent and aggregate wealth and the underlying asset have a bi-variate transformed-normal distribution. Our results show that the pricing kernel may become non-monotonic for high levels of volatility and low levels of skewness of the risk aversion parameter. Also, as the volatility of the risk aversion parameter increases, the (Black and Scholes) implied volatility shifts upwards but its shape remains the same, which implies that the volatility of the risk aversion parameter does not change the shape of the risk neutral distribution. Finally, an empirical example shows that the estimated volatility of the risk aversion parameter tends to be low in periods of high market volatility and vice-versa.

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Based on a standard general equilibrium economy, we develop a framework for pricing European options where the risk aversion parameter is state dependent and aggregate wealth and the underlying asset have a bi-variate transformed-normal distribution. Our results show that the pricing kernel may become non-monotonic for high levels of volatility and low levels of skewness of the risk aversion parameter. Also, as the volatility of the risk aversion parameter increases, the (Black and Scholes) implied volatility shifts upwards but its shape remains the same, which implies that the volatility of the risk aversion parameter does not change the shape of the risk neutral distribution. Finally, an empirical example shows that the estimated volatility of the risk aversion parameter tends to be low in periods of high market volatility and vice-versa.

Outlier Blindness: A Neurobiological Foundation for Neglect of Financial Risk

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How do people record information about the outcomes they observe in their environment? Building on a well-established neuroscientific framework, we propose a model in which people are hampered in their perception of outcomes that they expect to seldom encounter. We provide experimental evidence for such â€˜outlier blindnessâ€™ and discuss how it provides a microfoundation for neglected tail risk by investors in financial markets.

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How do people record information about the outcomes they observe in their environment? Building on a well-established neuroscientific framework, we propose a model in which people are hampered in their perception of outcomes that they expect to seldom encounter. We provide experimental evidence for such â€˜outlier blindnessâ€™ and discuss how it provides a microfoundation for neglected tail risk by investors in financial markets.

Portfolio Value-at-Risk and Expected-Shortfall Using an Efficient Simulation Approach Based on Gaussian Mixture Model

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Monte Carlo Approaches for calculating Value-at-Risk (VaR) are powerful tools widely used by financial risk managers across the globe. However, they are time consuming and sometimes inaccurate. In this paper, a fast and accurate Monte Carlo algorithm for calculating VaR and ES based on Gaussian Mixture Models is introduced. Gaussian Mixture Models are able to cluster input data with respect to market's conditions and therefore no correlation matrices are needed for risk computation. Sampling from each cluster with respect to their weights and then calculating the volatility-adjusted stock returns leads to possible scenarios for prices of assets. Our results on a sample of US stocks show that the Gmm-based VaR model is computationally efficient and accurate. From a managerial perspective, our model can efficiently mimic the turbulent behavior of the market. As a result, our VaR measures before, during and after crisis periods realistically reflect the highly non-normal behavior and non-linear correlation structure of the market.

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Monte Carlo Approaches for calculating Value-at-Risk (VaR) are powerful tools widely used by financial risk managers across the globe. However, they are time consuming and sometimes inaccurate. In this paper, a fast and accurate Monte Carlo algorithm for calculating VaR and ES based on Gaussian Mixture Models is introduced. Gaussian Mixture Models are able to cluster input data with respect to market's conditions and therefore no correlation matrices are needed for risk computation. Sampling from each cluster with respect to their weights and then calculating the volatility-adjusted stock returns leads to possible scenarios for prices of assets. Our results on a sample of US stocks show that the Gmm-based VaR model is computationally efficient and accurate. From a managerial perspective, our model can efficiently mimic the turbulent behavior of the market. As a result, our VaR measures before, during and after crisis periods realistically reflect the highly non-normal behavior and non-linear correlation structure of the market.

Public Debt and

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As interest rate-growth differentials (r-g) turned negative in many countries, governments consider pursuing fiscal expansion and the potential risks involved. Using a large sample of advanced and emerging economies, our analysis suggests that high public debts can lead to adverse future r-g dynamics. Specifically, countries with higher initial public debt experience (i) a shorter duration of negative r-g episodes and a higher probability of reversal, (ii) higher average r-g, and (iii) a more right-skewed r-g distribution, that implies higher downside risks. Furthermore, high-debt countries experience larger increases in interest rates in response to (iv) an unexpected decline in domestic output and (v) an increase of global volatility. Results are stronger when public debts are denominated in foreign currencies.

*r - g**at Risk*SSRN

As interest rate-growth differentials (r-g) turned negative in many countries, governments consider pursuing fiscal expansion and the potential risks involved. Using a large sample of advanced and emerging economies, our analysis suggests that high public debts can lead to adverse future r-g dynamics. Specifically, countries with higher initial public debt experience (i) a shorter duration of negative r-g episodes and a higher probability of reversal, (ii) higher average r-g, and (iii) a more right-skewed r-g distribution, that implies higher downside risks. Furthermore, high-debt countries experience larger increases in interest rates in response to (iv) an unexpected decline in domestic output and (v) an increase of global volatility. Results are stronger when public debts are denominated in foreign currencies.

Rise of the Central Bank Digital Currencies: Drivers, Approaches and Technologies

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Central bank digital currencies (CBDCs) are receiving more attention than ever before. Yet the motivations for issuance vary across countries, as do the policy approaches and technical designs. We investigate the economic and institutional drivers of CBDC development and take stock of design efforts. We set out a comprehensive database of technical approaches and policy stances on issuance, relying on central bank speeches and technical reports. Most projects are found in digitised economies with a high capacity for innovation. Work on retail CBDCs is more advanced where the informal economy is larger. We next take stock of the technical design options. More and more central banks are considering retail CBDC architectures in which the CBDC is a direct cash-like claim on the central bank, but where the private sector handles all customer-facing activity. We conclude with an in-depth description of three distinct CBDC approaches by the central banks of China, Sweden and Canada.

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Central bank digital currencies (CBDCs) are receiving more attention than ever before. Yet the motivations for issuance vary across countries, as do the policy approaches and technical designs. We investigate the economic and institutional drivers of CBDC development and take stock of design efforts. We set out a comprehensive database of technical approaches and policy stances on issuance, relying on central bank speeches and technical reports. Most projects are found in digitised economies with a high capacity for innovation. Work on retail CBDCs is more advanced where the informal economy is larger. We next take stock of the technical design options. More and more central banks are considering retail CBDC architectures in which the CBDC is a direct cash-like claim on the central bank, but where the private sector handles all customer-facing activity. We conclude with an in-depth description of three distinct CBDC approaches by the central banks of China, Sweden and Canada.

Robust Portfolio Selection Using Vine Copulas

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Portfolio optimization problems involving Conditional Value-at-Risk (CVaR) are often computationally intractable and require complete information about the distribution of returns, which is rarely available in practice. These difficulties are compounded when the portfolio contains a lot of assets. In this paper, we consider the worst-case CVaR (WCVaR) under mixture of vine copulas distribution uncertainty, which can capture complex and hidden dependence patterns in multivariate data.We compare the out-of-sample performance of the robust strategies based on the mixture of R-vine copulas, mixture of C-vine copulas, mixture of D-vine copulas and nominal CVaR method. The experimental study shows that the robust models based on mixture of R-Vine copulas and mixture of C-Vine copulas perform the best in terms of average returns, Sharpe ratio and cumulative returns. The performance of robust mixture of D-Vine copulas model might be advantageous when the correlation between assets is low.

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Portfolio optimization problems involving Conditional Value-at-Risk (CVaR) are often computationally intractable and require complete information about the distribution of returns, which is rarely available in practice. These difficulties are compounded when the portfolio contains a lot of assets. In this paper, we consider the worst-case CVaR (WCVaR) under mixture of vine copulas distribution uncertainty, which can capture complex and hidden dependence patterns in multivariate data.We compare the out-of-sample performance of the robust strategies based on the mixture of R-vine copulas, mixture of C-vine copulas, mixture of D-vine copulas and nominal CVaR method. The experimental study shows that the robust models based on mixture of R-Vine copulas and mixture of C-Vine copulas perform the best in terms of average returns, Sharpe ratio and cumulative returns. The performance of robust mixture of D-Vine copulas model might be advantageous when the correlation between assets is low.

Spot Asset Carry Cost Rates and Futures Hedge Ratios

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Since the 1970s, futures hedge ratios have traditionally been calculated ex-post via economically structure-less statistical analyses. This paper proposes an ex-ante, more efficient, computationally simpler, general â€œcarry cost rateâ€ based hedge ratio. Though the proposed hedge ratio is biased, its bias is stationary and mitigable via a one-time calculation. Thus, unlike the traditional hedge ratio, the proposed unadjusted and bias-adjusted â€œcarry cost rateâ€ hedge ratios are trivial to update. Finally, the paper shows that the hedge ratioâ€™s biasadjusted version has hedge-effectiveness higher than that for either the â€œtraditionalâ€ or â€œnaÃ¯veâ€ futures benchmark hedge ratios in diverse real and simulated markets.

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Since the 1970s, futures hedge ratios have traditionally been calculated ex-post via economically structure-less statistical analyses. This paper proposes an ex-ante, more efficient, computationally simpler, general â€œcarry cost rateâ€ based hedge ratio. Though the proposed hedge ratio is biased, its bias is stationary and mitigable via a one-time calculation. Thus, unlike the traditional hedge ratio, the proposed unadjusted and bias-adjusted â€œcarry cost rateâ€ hedge ratios are trivial to update. Finally, the paper shows that the hedge ratioâ€™s biasadjusted version has hedge-effectiveness higher than that for either the â€œtraditionalâ€ or â€œnaÃ¯veâ€ futures benchmark hedge ratios in diverse real and simulated markets.

The Benefits of Issuing Green Bonds: Evidence From China Green Bonds Market

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In 2019, China has become the largest issuance source in the global green bonds market. This paper studies the benefits of green bonds issuance to issuers and investors in China green bond market, including announcement effect, green premium, and the changes of firm financial performance and institutional ownership. First, green bonds issuance has a negative but insignificant announcement effect on the companyâ€™s stock return. Then we find that green premium is nearly zero demonstrating that investors are not willing to sacrifice profits for environmentally friendly projects. Thirdly, after green bonds issuance, financial performance of issuers does not have a significant improvement, but institutional ownership ratio has increased notably. In short, there is no significant difference between green bonds and conventional bonds in terms of issuance. Green bonds issuance may not have a direct pecuniary benefit to firms and investors but can be added into portfolios of institutional investors and improve their ESG score.

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In 2019, China has become the largest issuance source in the global green bonds market. This paper studies the benefits of green bonds issuance to issuers and investors in China green bond market, including announcement effect, green premium, and the changes of firm financial performance and institutional ownership. First, green bonds issuance has a negative but insignificant announcement effect on the companyâ€™s stock return. Then we find that green premium is nearly zero demonstrating that investors are not willing to sacrifice profits for environmentally friendly projects. Thirdly, after green bonds issuance, financial performance of issuers does not have a significant improvement, but institutional ownership ratio has increased notably. In short, there is no significant difference between green bonds and conventional bonds in terms of issuance. Green bonds issuance may not have a direct pecuniary benefit to firms and investors but can be added into portfolios of institutional investors and improve their ESG score.

The Effect of Institutional Ownership on the Timing of Earnings Announcements: Evidence from the Russell Index Inclusion

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Using the annual Russell 1000/2000 index reconstitution as an exogenous shock to institutional ownership (IO), I examine the effect of IO on firmsâ€™ decisions regarding the time of day to announce earnings. I argue that firms with high IO strategically choose to announce earnings after hours to facilitate post-announcement price discovery and reduce volatility because the after-hours period is largely dominated by sophisticated institutional investors. I find that firms with greater IO are more likely to announce earnings during after-market sessions (i.e., after hours after the market closes), but not during premarket sessions (i.e., after hours before the market opens). My analysis further shows that transient IO has a stronger influence on the likelihood of after-market announcements relative to quasi-indexer or dedicated institutional holdings and that firms with high IO are even more likely to announce during after-market sessions when firms have bad earnings news or when earnings include large transitory items. Lastly, I find that announcing earnings during after-market sessions indeed facilitates post-announcement price discovery and reduces volatility for firms with high IO. Collectively, my findings suggest that IO is a significant factor in firmsâ€™ disclosure timing decisions and that the timing of disclosures affects price discovery and volatility.

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Using the annual Russell 1000/2000 index reconstitution as an exogenous shock to institutional ownership (IO), I examine the effect of IO on firmsâ€™ decisions regarding the time of day to announce earnings. I argue that firms with high IO strategically choose to announce earnings after hours to facilitate post-announcement price discovery and reduce volatility because the after-hours period is largely dominated by sophisticated institutional investors. I find that firms with greater IO are more likely to announce earnings during after-market sessions (i.e., after hours after the market closes), but not during premarket sessions (i.e., after hours before the market opens). My analysis further shows that transient IO has a stronger influence on the likelihood of after-market announcements relative to quasi-indexer or dedicated institutional holdings and that firms with high IO are even more likely to announce during after-market sessions when firms have bad earnings news or when earnings include large transitory items. Lastly, I find that announcing earnings during after-market sessions indeed facilitates post-announcement price discovery and reduces volatility for firms with high IO. Collectively, my findings suggest that IO is a significant factor in firmsâ€™ disclosure timing decisions and that the timing of disclosures affects price discovery and volatility.

Time to Build and Bond Risk Premia

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This paper studies the impact of time to build on the term structure of interest rates in an otherwise standard Cox, Ingersoll and Ross (1985a, 1985b, CIR) production economy. Due to time to build, production depends not only on the current business condition as in the original CIR, but also on past conditions over the production period. This causes equilibrium quantities, including the short rate, forward rates, and bond returns, to depend on the historical path of the production opportunities. Production delay that accumulates uncertainty over the time to build generates significant time variations in bond risk premia. Bond returns can be predicted by current forward rates, as well as their lagged values, since current market states not only affect the current short rate but also the short rate in a distant future. Due to the path dependence, risk premia cannot be fully spanned by current yields. We show that time to build improves the ability of the CIR in generating empirical facts.

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This paper studies the impact of time to build on the term structure of interest rates in an otherwise standard Cox, Ingersoll and Ross (1985a, 1985b, CIR) production economy. Due to time to build, production depends not only on the current business condition as in the original CIR, but also on past conditions over the production period. This causes equilibrium quantities, including the short rate, forward rates, and bond returns, to depend on the historical path of the production opportunities. Production delay that accumulates uncertainty over the time to build generates significant time variations in bond risk premia. Bond returns can be predicted by current forward rates, as well as their lagged values, since current market states not only affect the current short rate but also the short rate in a distant future. Due to the path dependence, risk premia cannot be fully spanned by current yields. We show that time to build improves the ability of the CIR in generating empirical facts.

Value at Risk and Diversification

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A more diversified portfolio could increase the failure probability of a bank.

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A more diversified portfolio could increase the failure probability of a bank.

What Could Possibly Go Wrong? Predictable Misallocation in Simple Debt Repayment Experiments

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How do people repay debt? In a simple debt repayment experiment we provide subjects with two credit cards with different interest rates and levels of debt that are to be repaid. From a rational choice perspective, this is arguably one of the simplest financial decisions. Nevertheless, we observe severe deviations from optimal, i.e. debt minimizing, repayment decisions with one particularly persistent type of misallocation that has not been found before. In consecutive experiments we show that this and further fallacies are predictable so that behavior can be steered towards more efficient repayment decisions.

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How do people repay debt? In a simple debt repayment experiment we provide subjects with two credit cards with different interest rates and levels of debt that are to be repaid. From a rational choice perspective, this is arguably one of the simplest financial decisions. Nevertheless, we observe severe deviations from optimal, i.e. debt minimizing, repayment decisions with one particularly persistent type of misallocation that has not been found before. In consecutive experiments we show that this and further fallacies are predictable so that behavior can be steered towards more efficient repayment decisions.

Î -CAPM: An Asset Pricing Model with Probability Weighting

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We study the asset pricing implications of probability weightingâ€"the idea that investors overweight rare, high impact eventsâ€"by generalizing the mean-variance framework to allow for tail overweighting. Our model allows for a unique and homogenous pricing equilibrium with multiple investors and several correlated assets. We find that even a symmetric probability distortion has asymmetric pricing implications. For example, while the price of a left-skewed asset increases in skewness and decreases in variance, the price of a right-skewed asset may not. We also obtain and empirically validate several novel implications on the option-implied premiums on variance, skewness, and correlation.

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We study the asset pricing implications of probability weightingâ€"the idea that investors overweight rare, high impact eventsâ€"by generalizing the mean-variance framework to allow for tail overweighting. Our model allows for a unique and homogenous pricing equilibrium with multiple investors and several correlated assets. We find that even a symmetric probability distortion has asymmetric pricing implications. For example, while the price of a left-skewed asset increases in skewness and decreases in variance, the price of a right-skewed asset may not. We also obtain and empirically validate several novel implications on the option-implied premiums on variance, skewness, and correlation.