# Research articles for the 2020-11-10

A Multidisciplinary Literature Review of Academic Research on the Green Bond Market
SSRN
Climate finance is the mobilization of public and private capital toward climate mitigation and adaptation. Green bonds are one of a growing number of financial products used to facilitate climate finance investments. The green bond market has grown rapidly since the European Investment Bankâ€™s inaugural issue in 2007. In November 2018, the total outstanding volume of green bond issues crossed the $500 billion threshold, with an additional$148 billion in green bonds issued since the beginning of 2019. As the bridge between scientists, policymakers, and the private sector, the field encompassing green bonds and other financial instruments could be critical to meeting the targets of the Paris Agreement under the United Nationâ€™s Framework Convention on Climate Change (UNFCCC). And as that happens, it will become increasingly clear that this field will require a vast array of expertise and perspectives. This paper adopts an interdisciplinary approach to map the burgeoning field of literature on green bonds and climate finance more broadly. We situate the green bond market within the development of climate finance by outlining the role that scientific research plays in developing green bond guidelines and standards. We examine this trend from an anthropological and economic-history approach, before delving into the policy research that is emerging in the climate finance and green bond field. This provides the context for an analysis of the rapidly growing body of legal research on the green bond market, including a reflection on the legal ramifications of a pricing difference between vanilla and green bonds. Finally, we propose areas for further research in each of our respective disciplines of anthropology, policy, and law.

A Note on Utility Indifference Pricing with Delayed Information
Peter Bank,Yan Dolinsky
arXiv

We consider the Bachelier model with information delay where investment decisions can be based only on observations from $H>0$ time units before. Utility indifference prices are studied for vanilla options and we compute their non-trivial scaling limit for vanishing delay when risk aversion is scaled liked $A/H$ for some constant $A$. Using techniques from [7], we develop discrete-time duality for this setting and show how the relaxed form of martingale property introduced by [9] results in the scaling limit taking the form of a volatility control problem with quadratic penalty.

Banks, Non banks, and Lending Standards
Darst, Matthew,Refayet, Ehraz,Vardoulakis, Alexandros
SSRN
We study how competition between banks and non-banks affects lending standards. Banks have private information about some borrowers and are subject to capital requirements to mitigate risk-taking incentives from deposit insurance. Non-banks are uninformed and market forces determine their capital structure. We show that lending standards monotonically increase in bank capital requirements. Intuitively, higher capital requirements raise banks' skin in the game and screening out bad projects assures positive expected lending returns. Non-banks enter the market when capital requirements are sufficiently high, but do not cause a deterioration in lending standards. Optimal capital requirements trade-off inefficient lending to bad projects under loose standards with inefficient collateral liquidation under tight standards.

Cutting Edge Economic Tools
Whelan, Chris
SSRN
The words â€œcutting edgeâ€ suggest something new and on the frontier of our understanding. Although, when applied to economic tools, the word â€œnewâ€ has to be understood within the context of the development of economic theory, for which change tends to take place over years or decades rather than months. â€œCutting edgeâ€ also suggests something sophisticated, but some of the most powerful economic insights are also the most simple.Cutting edge economics tools can be broken down into cutting edge theoretical and empirical tools. For theoretical tools, this paper focuses on vertical merger theories of harm, which have risen to prominence over the last few years. For cutting edge empirical tools, the paper considers machine learning techniques applied to big data, which have started to gain mainstream application. The paper finishes with a cautionary example of how apparently cutting edge econometric analysis can go wrong based on the Westpac bank bill swap rate (BBSW) manipulation case in Australia.

Defining Climate-Aligned Investment: An Analysis of Sustainable Finance Taxonomy Development
Tripathy, Aneil,Mok, Lionel,House, Katie
SSRN
The green bond market has grown rapidly since its inception in 2007. Climate-aligned standards provide investors with the confidence that their investments deliver a measurable climate benefit. Serving as a benchmark, these standards demonstrate alignment with the Paris Agreement, against which green bond issuers can then report compliance. This paper draws on the authorsâ€™ experiences as practitioners and researchers helping to develop the Climate Bonds Standard and the European Unionâ€™s Sustainable Finance Taxonomy to analyze the methodological considerations that were vital to the development of both taxonomy systems. The first section positions the role of standards development within the context of the green bond market and is followed by an analysis of the factors that affect the Climate Bonds Standard criteria development process. This paper concludes with key takeaways and suggestions for areas of future research on climate-aligned standards development.

Distressed Firm Restructurings and Hedge Funds with Expertise: Saviors or Vultures?
Baranchuk, Nina,Rebello, Michael J.
SSRN
We model restructuring when hedge funds with expertise in navigating distress intervene. Whether hedge funds help distressed firms or act like vultures are two sides of the same coin. Interventions help when firm prospects are bright and assets are not easily redeploy-able. Interventions are vulture like when bankruptcy is costly and fire sale conditions prevail in the market for distressed assets. Positive outcomes are more likely when funds intervene by acquiring equity though acquiring debt is more likely. These effects are the result of systematic changes in expectations and strategies of firms' other claimants in response to hedge fund intervention.

Energy Transition, Asset Price Fluctuations, and Dynamic Portfolio Decisions
Semmler, Willi
SSRN
This paper analyzes the implications of short-termism on portfolio decisions of investors, and its potential consequences on green investments. We study a dynamic portfolio choice problem that contains two assets, one asset with fluctuating returns and another asset with a constant risk-free return. Fluctuating returns can arise from fossil or from clean energy-related assets. Short-termism is seen to be driven by discount rates (exponential and hyperbolic) and the decision horizon of investors. We also explore the impact of the fluctuating assets returns on the fate of the portfolio, for both a deterministic and stochastic model variant, and in cases where innovation efforts are spent for fossil fuel or clean energy sources. Detailing dynamic portfolio decisions in such a way may allow us for better pathways to empirical tests.

Factor Investing, Learning from Prices, and Endogenous Uncertainty in Asset Markets
Dim, Chukwuma,Sangiorgi, Francesco,Vilkov, Grigory
SSRN
We use an endogenous information model with correlated assets to study learning and uncertainty under the factor investing paradigm. As investors shift attention away from firms toward a systematic risk factor, firmsâ€™ stock prices become less informative about it. This loss of price information raises systematic uncertainty, increasing incentives to learn about systematic risk. Such a learning complementarity leads to multiple regimes in systematic uncertainty and attention allocation. Empirically, we specify and estimate a model-based, forward-looking measure for investor attention to systematic versus firm-level information. Consistent with the model, the measure follows a regime-switching process. The high-level regime is linked to lower stock price sensitivity to firm-specific information and higher systematic risk concentration.

Liquidity in Resolution: Estimating Possible Liquidity Gaps for Specific Banks in Resolution and in a Systemic Crisis
Parisi, Laura ,Chalamandaris, Dimitrios,Amamou, Raschid ,Torstensson, PÃ¤r
SSRN
This paper contributes to the debate on liquidity in resolution by providing a quantitative assessment of liquidity gaps of banks in resolution in the euro area. It estimates possible ranges of liquidity gaps for significant banks under different assumptions and scenarios. The findings suggest that, while the average liquidity gaps in resolution are limited, the averages hide significant outliers. The paper thus shows that, under adverse circumstances, the instruments currently available to provide liquidity support to financial institutions in the euro area would be insufficient

Malliavin-Mancino estimators implemented with non-uniform fast Fourier transforms
Patrick Chang,Etienne Pienaar,Tim Gebbie
arXiv

We implement and test kernel averaging Non-Uniform Fast Fourier Transform (NUFFT) methods to enhance the performance of correlation and covariance estimation on asynchronously sampled event-data using the Malliavin-Mancino Fourier estimator. The methods are benchmarked for Dirichlet and Fej\'{e}r Fourier basis kernels. We consider test cases formed from Geometric Brownian motions to replicate synchronous and asynchronous data for benchmarking purposes. We consider three standard averaging kernels to convolve the event-data for synchronisation via over-sampling for use with the Fast Fourier Transform (FFT): the Gaussian kernel, the Kaiser-Bessel kernel, and the exponential of semi-circle kernel. First, this allows us to demonstrate the performance of the estimator with different combinations of basis kernels and averaging kernels. Second, we investigate and compare the impact of the averaging scales explicit in each averaging kernel and its relationship between the time-scale averaging implicit in the Malliavin-Mancino estimator. Third, we demonstrate the relationship between time-scale averaging based on the number of Fourier coefficients used in the estimator to a theoretical model of the Epps effect. We briefly demonstrate the methods on Trade-and-Quote (TAQ) data from the Johannesburg Stock Exchange to make an initial visualisation of the correlation dynamics for various time-scales under market microstructure.

Modeling and analysis of the effect of COVID-19 on the stock price: V and L-shape recovery
Ajit Mahata,Anish rai,Om Prakash,Md Nurujjaman
arXiv

The emergence of the COVID-19 pandemic, a new and novel risk factor, leads to the stock price crash due to the investors' rapid and synchronous sell-off. However, within a short period, the quality sectors start recovering from the bottom. A stock price model has been developed during such crises based on the net-fund-flow ($\Psi_t$) due to institutional investors, and financial antifragility ($\phi$) of a company. We assume that during the crash, the stock price fall is independent of the $\phi$. We study the effects of shock lengths and $\phi$ on the stock price during the crises period using the $\Psi_t$ obtained from synthetic and real fund flow data. We observed that the possibility of recovery of stock with $\phi>0$, termed as quality stock, decreases with an increase in shock-length beyond a specific period. A quality stock with higher $\phi$ shows V-shape recovery and outperform others. The shock length and recovery period of quality stock are almost equal that is seen in the Indian market. Financially stressed stocks, i.e., the stocks with $\phi<0$, show L-shape recovery during the pandemic. The stock data and model analysis shows that the investors, in uncertainty like COVID-19, invest in quality stocks to restructure their portfolio to reduce the risk. The study may help the investors to make the right investment decision during a crisis.

Mutual Fund Hedging Demands and the Cross-Section of Variance Risk Premiums
Li, Shuaiqi
SSRN
This paper examines the pricing implication of mutual fund activities in equity option markets. I present a robust new finding that the mutual fund ownership concentration of a firm's equity shares, measured as the Herfindahl-Hirschman Index (HHI), negatively predicts the cross-sectional variance risk premiums (VRP). HHI can be interpreted as a proxy for the variance risk hedging demand of mutual funds. An increase in mutual fund ownership concentration in the underlying stock drives up the hedging demand for equity options. To absorb the increased order imbalances, dealers charge a higher premium, leading to a more negative VRP. Using the actual option holdings of U.S. equity funds, I find a positive relation between the firm HHI and the market share held by mutual funds in the firm's option market. After decomposing firm VRP into systematic and idiosyncratic components, I find that HHI is negatively related with both components.

Nonparametric Adaptive Bayesian Stochastic Control Under Model Uncertainty
Tao Chen,Jiyoun Myung
arXiv

In this paper we propose a new methodology for solving a discrete time stochastic Markovian control problem under model uncertainty. By utilizing the Dirichlet process, we model the unknown distribution of the underlying stochastic process as a random probability measure and achieve online learning in a Bayesian manner. Our approach integrates optimizing and dynamic learning. When dealing with model uncertainty, the nonparametric framework allows us to avoid model misspecification that usually occurs in other classical control methods. Then, we develop a numerical algorithm to handle the infinitely dimensional state space in this setup and utilizes Gaussian process surrogates to obtain a functional representation of the value function in the Bellman recursion. We also build separate surrogates for optimal control to eliminate repeated optimizations on out-of-sample paths and bring computational speed-ups. Finally, we demonstrate the financial advantages of the nonparametric Bayesian framework compared to parametric approaches such as strong robust and time consistent adaptive.

Off Track Monetary Policy and Housing
SSRN
We find significant evidence of model mis-specification, in the form of neglected serial correlation, in the econometric model of the U.S. housing market used by Taylor (2007) in his critique of monetary policy following the 2001 recession. When we model that serial correlation, his model fails to replicate the historical paths of housing starts and house price inflation. Further modifications in the model allow us to capture both the housing boom and the bust. Our analysis suggests that a counterfactual monetary policy proposed by Taylor (2007) would not have averted the pre-financial crisis collapse in the housing market.

Only Gamble in Town: Stock Market Gambling Around the World and Market Efficiency
Kumar, Alok,Nguyen, Thanh Huong,PutniÅ†Å¡, TÄlis J.
SSRN
This paper examines how gambling-motivated trading affects aggregate financial market outcomes. Using a unique global gambling data set covering 39 countries, we show that the dollar volume of stock market gambling is at least 3.5 times the combined volume of â€œtraditionalâ€ gambling outlets such as casinos and lotteries. The two forms of gambling are positively related as they have common drivers, including wealth, culture, and economic environment. Restrictions on traditional forms of gambling generate spillover effects into stock markets. Exploiting casino regulation as an instrument, we find that stock market gambling increases liquidity and consequently improves the informational efficiency of prices.

Optimizing distortion riskmetrics with distributional uncertainty
Silvana Pesenti,Qiuqi Wang,Ruodu Wang
arXiv

Optimization of distortion riskmetrics with distributional uncertainty has wide applications in finance and operations research. Distortion riskmetrics include many commonly applied risk measures and deviation measures, which are not necessarily monotone or convex. One of our central findings is a unifying result that allows us to convert an optimization of a non-convex distortion riskmetric with distributional uncertainty to a convex one, leading to great tractability. The key to the unifying equivalence result is the novel notion of closedness under concentration of sets of distributions. Our results include many special cases that are well studied in the optimization literature, including but not limited to optimizing probabilities, Value-at-Risk, Expected Shortfall, and Yaari's dual utility under various forms of distributional uncertainty. We illustrate our theoretical results via applications to portfolio optimization, optimization under moment constraints, and preference robust optimization.

Pattern recognition in trading behaviors before stock price jumps: new method based on multivariate time series classification
Ao Kong,Robert Azencott,Hongliang Zhu
arXiv

This paper extends the work of Boudt and Pertitjean(2014) and investigates the trading patterns before price jumps in the stock market based on a new multivariate time classification technique. Different from Boudt and Pertitjean(2014), our analyzing scheme can explore the "time-series information" embedded in the trading-related attributes and provides a set of jump indicators for abnormal pattern recognition. In addition to the commonly used liquidity measures, our analysis also involves a set of technical indicators to describe the micro-trading behaviors. An empirical study is conducted on the level-2 data of the constituent stocks of China Security Index 300. It is found that among all the candidate attributes, several volume and volatility-related attributes exhibit the most significant abnormality before price jumps. Though some of the abnormalities start just shortly before the occurrence of the jumps, some start much earlier. We also find that most of our attributes have low mutual dependencies with each other from the perspective of time-series analysis, which allows various perspectives to study the market trading behaviors. To this end, our experiment provides a set of jump indicators that can effectively detect the stocks with extremely abnormal trading behaviors before price jumps. More importantly, our study offers a new framework and potential useful directions for trading-related pattern recognition problem using the time series classification techniques.

Predictive Regressions: A Machine Learning Perspective
Coqueret, Guillaume,Deguest, Romain
SSRN
In this article, we quantify the forecasting efficiency of the OLS estimator in uni-variate predictive regressions. We link the prediction accuracy to three key quantities: the persistence of the underlying series, the forecasting horizon, and the sample size. We find that high auto-correlation in the dependent variable is required to reach reasonably low levels of mean squared errors. In this case, we identify two configurations which generate positive out-of-sample R-squared: short term forecasting with small samples and long horizon predictions with very deep samples. Two examples of such configurations can easily be found in financial economics: the short term volatility and the long term equity premium. We confirm our results via an empirical study on the SP 500 with a series of 15 popular predictors used in the literature.

Recent Evidence on International Stock Marketâ€™s Overreaction
Pereira Alves, Paulo F.,Carvalho, LuÃ­s
SSRN
This paper examines the overreaction hypothesis on market indices for three- and five-year investment periods using end-of-month data from 49 Morgan Stanley Capital International indices from December 1970 to December 2018. The returns were computed as holding-period returns, instead of cumulative average returns, to avoid an upward bias. We found economically and statistically significant return reversals for both the three-year and five-year investment periods. When implemented in developed markets only, there is evidence that supports the overreaction hypothesis, although the excess returns are smaller than those observed in the whole sample. Not only did the losers outperform the winners, but the former were also less risky. Notwithstanding these results, the overreaction strategy is sensitive to the periods considered, thus highlighting the possibility that its success is not time stationary.

Reversal Interest Rate and Macroprudential Policy
Darracq Paries, Matthieu,Kok, Christoffer
SSRN
Could a monetary policy loosening entail the opposite effect than the intended expansionary impact in a low interest rate environment? We demonstrate that the risk of hitting the rate at which the effect reverses depends on the capitalization of the banking sector by using a non-linear macroeconomic model calibrated to the euro area economy. The framework suggests that the reversal interest rate is located in negative territory of around âˆ'1% per annum. The possibility of the reversal interest rate creates a novel motive for macroprudential policy. We show that macroprudential policy in the form of a countercyclical capital buffer, which prescribes the build-up of buffers in good times, can mitigate substantially the probability of encountering the reversal rate, improves welfare and reduces economic fluctuations. This new motive emphasizes also the strategic complementarities between monetary policy and macroprudential policy.

Spontaneous symmetry breaking in Quantum Finance
Ivan Arraut,Alan Au,Alan Ching-biu Tse
arXiv

We analyze the phenomena of spontaneous symmetry breaking in Quantum Finance by using as a starting point the Black-Scholes (BS) and the Merton-Garman (MG) equations expressed in the Hamiltonian form. In this scenario the martingale condition (state) corresponds to the vacuum state which becomes degenerate when the symmetry of the system is spontaneously broken. We then analyze the broken symmetries of the system and we interpret from the perspective of Financial markets the possible appearance of the Nambu-Goldstone bosons.

Startup & Unicorn Growth Valuation
Andreas A. Aigner,Walter Schrabmair
arXiv

How do you value companies which have IPOed recently? How do you compare them amongst their peers? Valuing companies using a linear extrapolation of their revenues and profits leads to an ingenious method to benchmark stocks against each other. Here we present such a method, dubbed the growth average U1.

Supervisory Enforcement Actions against Banks and Systemic Risk
Berger, Allen N.,Cai, Jin,Roman, Raluca A.,Sedunov, John
SSRN
Prudential bank supervision is designed to enhance financial stability, but we are unaware of research linking this supervision to financial system risk. In particular, there are no prior findings on how supervisory enforcement actions (EAs) â€" major tools of supervisors â€" affect systemic risk. Theory is ambiguous, leaving important unanswered research and policy questions regarding the effectiveness of these tools. We empirically investigate relations between EAs and banksâ€™ contributions to systemic risk. Using instrumental variables, we find significantly smaller bank contributions to systemic risk after EAs than before them, suggesting that EAs enhance financial stability. The data also suggest that the primary channel behind this relation is reduced leverage, but lower portfolio risk also plays a role. We also find that the magnitude of our findings is greater during financial crises than normal times, and that EAs against banks are more effective in systemic risk reduction than those against individual bank managers.

Tracking change-points in multivariate extremes
Miguel de Carvalho,Manuele Leonelli,Alex Rossi
arXiv

In this paper we devise a statistical method for tracking and modeling change-points on the dependence structure of multivariate extremes. The methods are motivated by and illustrated on a case study on crypto-assets.