Successful entrepreneurs and executives often hold much of their wealth in a highly appreciated single stock and thereby face a difficult financial dilemma. On the one hand, the high idiosyncratic volatility of a concentrated single stock position can lead to significant risk of catastrophic losses; on the other hand, selling the stock can result in an immediate and punitive tax burden. This article develops an analytical framework for evaluating this choice and explains how it relates to classic betting strategies and economic theory. For many investors, a full and immediate liquidation of their appreciated single stock might be optimal from the perspective of long-run wealth growth and preservation. In fact, in the absence of diversification, most investors must expect catastrophic losses of wealth over reasonable investment horizons. For investors reluctant to incur an upfront tax burden, tax-efficient techniques for disposing of an appreciated single stock might strike the balance between the urgency to diversify concentrated risk and aversion to taxes. Whereas for median and mode cumulative wealth, the primary effect likely comes from diversification, be it tax efficient or not, for mean cumulative wealth, the tax efficiency of diversification can yield a tangible improvement.
{"title":"When Fortune Doesn’t Favor the Bold: Perils of Volatility for Wealth Growth and Preservation","authors":"Nathan Sosner","doi":"10.2139/ssrn.4104756","DOIUrl":"https://doi.org/10.2139/ssrn.4104756","url":null,"abstract":"Successful entrepreneurs and executives often hold much of their wealth in a highly appreciated single stock and thereby face a difficult financial dilemma. On the one hand, the high idiosyncratic volatility of a concentrated single stock position can lead to significant risk of catastrophic losses; on the other hand, selling the stock can result in an immediate and punitive tax burden. This article develops an analytical framework for evaluating this choice and explains how it relates to classic betting strategies and economic theory. For many investors, a full and immediate liquidation of their appreciated single stock might be optimal from the perspective of long-run wealth growth and preservation. In fact, in the absence of diversification, most investors must expect catastrophic losses of wealth over reasonable investment horizons. For investors reluctant to incur an upfront tax burden, tax-efficient techniques for disposing of an appreciated single stock might strike the balance between the urgency to diversify concentrated risk and aversion to taxes. Whereas for median and mode cumulative wealth, the primary effect likely comes from diversification, be it tax efficient or not, for mean cumulative wealth, the tax efficiency of diversification can yield a tangible improvement.","PeriodicalId":74863,"journal":{"name":"SSRN","volume":"25 1","pages":"10 - 36"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45592248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The long-run impact and implications of an endowment’s spending policy and asset-allocation decisions are examined. Using a dynamic model, the authors explore how different endowment spending rules (SRs) influence the dynamics of an endowment’s size and future spending. They find that different parameters within each SR have significant long-term impact on wealth accumulation and spending capacity. Using Merton’s (1993) endowment model and compiled asset-allocation data, they estimate the intertemporal preferences and risk aversion of several major endowments and find significant variation across endowments in their propensity to increase portfolio risk in response to increased spending needs.
{"title":"The Effects of Spending Rules and Asset Allocation on Nonprofit Endowments","authors":"Z. Halem, A. Lo, Egor Matveyev, Sarah Quraishi","doi":"10.2139/ssrn.4065795","DOIUrl":"https://doi.org/10.2139/ssrn.4065795","url":null,"abstract":"The long-run impact and implications of an endowment’s spending policy and asset-allocation decisions are examined. Using a dynamic model, the authors explore how different endowment spending rules (SRs) influence the dynamics of an endowment’s size and future spending. They find that different parameters within each SR have significant long-term impact on wealth accumulation and spending capacity. Using Merton’s (1993) endowment model and compiled asset-allocation data, they estimate the intertemporal preferences and risk aversion of several major endowments and find significant variation across endowments in their propensity to increase portfolio risk in response to increased spending needs.","PeriodicalId":74863,"journal":{"name":"SSRN","volume":"49 1","pages":"81 - 106"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48265737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Harvey, Tarek Abou Zeid, Teun Draaisma, Martin Luk, Henry Neville, Andre Rzym, Otto van Hemert
The authors provide practical insights for investors seeking exposure to the growing cryptocurrency space. Today, crypto is much more than just bitcoin, which historically dominated the space but accounted for just a 31% share of total crypto trading volume in June 2022. The authors discuss a wide variety of tokens, highlighting both their functionality and their investment properties. The authors critically compare popular valuation methods, and they contrast buy-and-hold investing with more active styles. The authors only deem return data from 2017 representative, but the use of intraday data boosts statistical power. Underlying crypto performance has been notoriously volatile, but volatility-targeting methods are effective at controlling risk, and trend-following strategies have performed well. Crypto assets display a low correlation with traditional risky assets in normal times, but the correlation also rises in the left tail of these risky assets. Finally, the authors detail important custody and regulatory considerations for institutional investors.
{"title":"An Investor’s Guide to Crypto","authors":"C. Harvey, Tarek Abou Zeid, Teun Draaisma, Martin Luk, Henry Neville, Andre Rzym, Otto van Hemert","doi":"10.2139/ssrn.4124576","DOIUrl":"https://doi.org/10.2139/ssrn.4124576","url":null,"abstract":"The authors provide practical insights for investors seeking exposure to the growing cryptocurrency space. Today, crypto is much more than just bitcoin, which historically dominated the space but accounted for just a 31% share of total crypto trading volume in June 2022. The authors discuss a wide variety of tokens, highlighting both their functionality and their investment properties. The authors critically compare popular valuation methods, and they contrast buy-and-hold investing with more active styles. The authors only deem return data from 2017 representative, but the use of intraday data boosts statistical power. Underlying crypto performance has been notoriously volatile, but volatility-targeting methods are effective at controlling risk, and trend-following strategies have performed well. Crypto assets display a low correlation with traditional risky assets in normal times, but the correlation also rises in the left tail of these risky assets. Finally, the authors detail important custody and regulatory considerations for institutional investors.","PeriodicalId":74863,"journal":{"name":"SSRN","volume":"49 1","pages":"146 - 171"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46895184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Herding is human nature. There is ample evidence of it in the management of public pension funds in the United States, where their effective equity exposure clusters around an average of approximately 70%. Extreme diversification is universal. These two aspects of herd behavior have proven benign. Where herding has had a detrimental effect is the funds’ pouring more than a trillion dollars into alternative investments after alts ceased adding value to institutional portfolios more than 10 years ago. One might say two out of three ain’t bad. And yet, the heavy use of active management—and alts, in particular—has cost the funds dearly. Public fund managers need to understand that their strength is not active money management. Rather, it is their potential to become the lowest-cost producers of investment returns on the planet. This article argues in favor of a one-size-fits-all approach to managing public pension investments—namely, embracing a Universal Investment Portfolio.
{"title":"richard ennis’s insights A Universal Investment Portfolio for Public Pension Funds: Making the Most of Our Herding Ways","authors":"Richard M. Ennis","doi":"10.2139/ssrn.4138750","DOIUrl":"https://doi.org/10.2139/ssrn.4138750","url":null,"abstract":"Herding is human nature. There is ample evidence of it in the management of public pension funds in the United States, where their effective equity exposure clusters around an average of approximately 70%. Extreme diversification is universal. These two aspects of herd behavior have proven benign. Where herding has had a detrimental effect is the funds’ pouring more than a trillion dollars into alternative investments after alts ceased adding value to institutional portfolios more than 10 years ago. One might say two out of three ain’t bad. And yet, the heavy use of active management—and alts, in particular—has cost the funds dearly. Public fund managers need to understand that their strength is not active money management. Rather, it is their potential to become the lowest-cost producers of investment returns on the planet. This article argues in favor of a one-size-fits-all approach to managing public pension investments—namely, embracing a Universal Investment Portfolio.","PeriodicalId":74863,"journal":{"name":"SSRN","volume":"32 1","pages":"7 - 20"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43180631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. Heurtebize, Frederic Chen, François Soupé, Raul Leote de Carvalho
The authors propose a model based on statistical learning techniques to predict unreported corporate greenhouse gas emissions that generates considerably better results than existing approaches. The model uses one linear learner and one nonlinear learner only, which reduces its complexity to the minimum required. An iterative approach to detecting and correcting data significantly improves the model predictions. Unlike mainstream approaches, which tend to construct one model for each industry, we construct one single global model that uses industry as a factor. This addresses the problem of lack of breadth or lack of reported data in some sectors and generates practical results even for industries where other approaches have failed. We show results for Scope 1 and Scope 2 corporate carbon emissions. Adapting the framework to Scope 3 will be the focus of a future article.
{"title":"Corporate Carbon Footprint: A Machine Learning Predictive Model for Unreported Data","authors":"T. Heurtebize, Frederic Chen, François Soupé, Raul Leote de Carvalho","doi":"10.2139/ssrn.4038436","DOIUrl":"https://doi.org/10.2139/ssrn.4038436","url":null,"abstract":"The authors propose a model based on statistical learning techniques to predict unreported corporate greenhouse gas emissions that generates considerably better results than existing approaches. The model uses one linear learner and one nonlinear learner only, which reduces its complexity to the minimum required. An iterative approach to detecting and correcting data significantly improves the model predictions. Unlike mainstream approaches, which tend to construct one model for each industry, we construct one single global model that uses industry as a factor. This addresses the problem of lack of breadth or lack of reported data in some sectors and generates practical results even for industries where other approaches have failed. We show results for Scope 1 and Scope 2 corporate carbon emissions. Adapting the framework to Scope 3 will be the focus of a future article.","PeriodicalId":74863,"journal":{"name":"SSRN","volume":"3 1","pages":"36 - 54"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42606747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Models of option returns neglect the distribution of expected asset volatility, unfortunately for not only derivatives traders but also investors who monitor options as fear gauges. Six common GARCH (generalized autoregressive conditional heteroskedasticity) models afford estimates of the physical, rather than the risk-neutral, distribution of anticipated—instead of historical—volatility, as well as of volatility disagreement. This value specification covering nine global equity indexes and five expiries from 1 to 12 months fits implied volatilities closely, with sizeable and robust error-correction speeds out of sample, all else equal. Exploratory backtests of delta-neutral trading rules produce high Sharpe ratios and alphas, with modest drawdowns and skew.
{"title":"Value for Equity Index Options: Expected—Not Realized—Volatility and the Distribution of Forecasts","authors":"J. Durham","doi":"10.2139/ssrn.4067236","DOIUrl":"https://doi.org/10.2139/ssrn.4067236","url":null,"abstract":"Models of option returns neglect the distribution of expected asset volatility, unfortunately for not only derivatives traders but also investors who monitor options as fear gauges. Six common GARCH (generalized autoregressive conditional heteroskedasticity) models afford estimates of the physical, rather than the risk-neutral, distribution of anticipated—instead of historical—volatility, as well as of volatility disagreement. This value specification covering nine global equity indexes and five expiries from 1 to 12 months fits implied volatilities closely, with sizeable and robust error-correction speeds out of sample, all else equal. Exploratory backtests of delta-neutral trading rules produce high Sharpe ratios and alphas, with modest drawdowns and skew.","PeriodicalId":74863,"journal":{"name":"SSRN","volume":"49 1","pages":"213 - 251"},"PeriodicalIF":0.0,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43922149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The article develops an analytical framework that enables investors who use gain- and loss-based performance measures to evaluate and compare investment strategies or managers and to do so in a more precise manner that accounts for statistical uncertainty and sampling error. In particular, the article develops tests for detection of timing skill and sizing skill for individual strategies as well as tests to compare similarity across competing strategies or between different periods (e.g., backtest and live). Some of these tests are exact and therefore relevant for small samples or track records. The article illustrates the methodology throughout by applying these results to a suite of systematic strategy indexes.
{"title":"Gains and Losses Revisited: Skill Detection and Similarity Assessment","authors":"S. Browne","doi":"10.2139/ssrn.4077305","DOIUrl":"https://doi.org/10.2139/ssrn.4077305","url":null,"abstract":"The article develops an analytical framework that enables investors who use gain- and loss-based performance measures to evaluate and compare investment strategies or managers and to do so in a more precise manner that accounts for statistical uncertainty and sampling error. In particular, the article develops tests for detection of timing skill and sizing skill for individual strategies as well as tests to compare similarity across competing strategies or between different periods (e.g., backtest and live). Some of these tests are exact and therefore relevant for small samples or track records. The article illustrates the methodology throughout by applying these results to a suite of systematic strategy indexes.","PeriodicalId":74863,"journal":{"name":"SSRN","volume":"4 1","pages":"39 - 71"},"PeriodicalIF":0.0,"publicationDate":"2022-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47276699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The author suggests an alternative environmental, social, and governance (ESG) integration framework for portfolio optimization to reflect that systematic ESG risk can account for joint movement in security prices. The author’s framework consists of the double-index model, the two-layer grouping, and the extended-criteria decision rule for optimal portfolio selection. The author’s approach clearly shows how institutional investors can manage systematic ESG risk, rather than individual ESG risk, during portfolio optimization. The framework also provides a simple decision rule, a practical complement for complicated nonlinear programming algorithms, and clearly shows the security characteristics that make it desirable. Applying the framework to US equity mutual funds indicates that the approach can help investors understand how systematic ESG risk is relevant to future risks or returns, strategically manage systematic ESG risk, and improve the portfolio’s risk-adjusted return. Thus, the author’s framework can provide a tractable empirical method compatible with recent theoretical analyses on ESG factor investing.
{"title":"Systematic ESG Risk and Decision Criteria for Optimal Portfolio Selection","authors":"Ick Jin","doi":"10.2139/ssrn.3962574","DOIUrl":"https://doi.org/10.2139/ssrn.3962574","url":null,"abstract":"The author suggests an alternative environmental, social, and governance (ESG) integration framework for portfolio optimization to reflect that systematic ESG risk can account for joint movement in security prices. The author’s framework consists of the double-index model, the two-layer grouping, and the extended-criteria decision rule for optimal portfolio selection. The author’s approach clearly shows how institutional investors can manage systematic ESG risk, rather than individual ESG risk, during portfolio optimization. The framework also provides a simple decision rule, a practical complement for complicated nonlinear programming algorithms, and clearly shows the security characteristics that make it desirable. Applying the framework to US equity mutual funds indicates that the approach can help investors understand how systematic ESG risk is relevant to future risks or returns, strategically manage systematic ESG risk, and improve the portfolio’s risk-adjusted return. Thus, the author’s framework can provide a tractable empirical method compatible with recent theoretical analyses on ESG factor investing.","PeriodicalId":74863,"journal":{"name":"SSRN","volume":"48 1","pages":"206 - 225"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48916264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We solve the quadratic hedging problem by deep learning in discrete time. We consider three deep learning algorithms corresponding to three architectures of neural network approximation: approximating controls of different periods by different feedforward neural networks (FNNs) as proposed by Han and Weinan (2016), using a single FNN with decision time as an input to approximate controls of different periods, and using a recursive neural network (RNN) to utilize historical information. We evaluate these algorithms under the discrete-time Black-Scholes model and the DCC-GARCH model for hedging basket options on portfolios of up to 100 assets with time to maturity up to one year. We compare them in terms of their hedging error on the test data, extent of overlearning, learned hedging strategy, training speed, and scalability. Our results favor the single FNN and RNN approximations overall; the multiple FNN approximation can fail for a large portfolio and a long maturity. We also evaluate the performance of the single FNN and RNN algorithms in a data-driven framework, where data is generated by resampling without assuming any parametric model.
{"title":"Evaluation of Deep Learning Algorithms for Quadratic Hedging","authors":"Zhiwen Dai, Lingfei Li, Gongqiu Zhang","doi":"10.2139/ssrn.4062101","DOIUrl":"https://doi.org/10.2139/ssrn.4062101","url":null,"abstract":"We solve the quadratic hedging problem by deep learning in discrete time. We consider three deep learning algorithms corresponding to three architectures of neural network approximation: approximating controls of different periods by different feedforward neural networks (FNNs) as proposed by Han and Weinan (2016), using a single FNN with decision time as an input to approximate controls of different periods, and using a recursive neural network (RNN) to utilize historical information. We evaluate these algorithms under the discrete-time Black-Scholes model and the DCC-GARCH model for hedging basket options on portfolios of up to 100 assets with time to maturity up to one year. We compare them in terms of their hedging error on the test data, extent of overlearning, learned hedging strategy, training speed, and scalability. Our results favor the single FNN and RNN approximations overall; the multiple FNN approximation can fail for a large portfolio and a long maturity. We also evaluate the performance of the single FNN and RNN algorithms in a data-driven framework, where data is generated by resampling without assuming any parametric model.","PeriodicalId":74863,"journal":{"name":"SSRN","volume":"30 1","pages":"32 - 57"},"PeriodicalIF":0.0,"publicationDate":"2022-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43218999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bilateral gamma processes generalize the variance gamma process and allow one to capture, more precisely, the differences between upward and downward moves of financial returns, notably in terms of jump speed, frequency, and size. Like in most other pure jump models, option pricing under bilateral gamma processes relies heavily on numerical evaluation of Fourier integrals. In this article, we combine the Mellin transform and residue calculus to establish closed-form pricing formulas for several vanilla and exotic European options. These formulas take the form of series whose terms are straightforward to evaluate in practice and achieve an arbitrary degree of precision, without requiring sophisticated numerical tools; moreover, the convergence of the series is particularly accelerated for short maturity options, which are the most challenging to price for competing Fourier methods. Accuracy of the formulas is assessed thanks to several comparisons with state-of-the-art Fourier methods, with reference prices provided for future research.
{"title":"Robust and Nearly Exact Option Pricing with Bilateral Gamma Processes","authors":"Jean-Philippe Aguilar, J. Kirkby","doi":"10.2139/ssrn.4074124","DOIUrl":"https://doi.org/10.2139/ssrn.4074124","url":null,"abstract":"Bilateral gamma processes generalize the variance gamma process and allow one to capture, more precisely, the differences between upward and downward moves of financial returns, notably in terms of jump speed, frequency, and size. Like in most other pure jump models, option pricing under bilateral gamma processes relies heavily on numerical evaluation of Fourier integrals. In this article, we combine the Mellin transform and residue calculus to establish closed-form pricing formulas for several vanilla and exotic European options. These formulas take the form of series whose terms are straightforward to evaluate in practice and achieve an arbitrary degree of precision, without requiring sophisticated numerical tools; moreover, the convergence of the series is particularly accelerated for short maturity options, which are the most challenging to price for competing Fourier methods. Accuracy of the formulas is assessed thanks to several comparisons with state-of-the-art Fourier methods, with reference prices provided for future research.","PeriodicalId":74863,"journal":{"name":"SSRN","volume":"30 1","pages":"8 - 31"},"PeriodicalIF":0.0,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49327482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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