D. Cvijanović, Stanimira Milcheva, Alex M. van de Minne
Private real estate markets have experienced signi ficant in inflows of institutional capital over the last couple of decades. In this paper we seek to understand what are the implications of this recent development. Employing a generalized Hamiltonian Monte Carlo Bayesian procedure we find novel empirical evidence that market entry by large institutional investors predicts higher uncertainty and greater noise in real estate prices in the short and medium run, and lower longitudinal risk in the long run. Our findings point to a signi ficant eff ect of institutional capital, which serves as a catalyst for structural changes in real estate market risk.
{"title":"Impact of Institutional Investors on Real Estate Risk","authors":"D. Cvijanović, Stanimira Milcheva, Alex M. van de Minne","doi":"10.2139/ssrn.3942539","DOIUrl":"https://doi.org/10.2139/ssrn.3942539","url":null,"abstract":"Private real estate markets have experienced signi ficant in inflows of institutional capital over the last couple of decades. In this paper we seek to understand what are the implications of this recent development. Employing a generalized Hamiltonian Monte Carlo Bayesian procedure we find novel empirical evidence that market entry by large institutional investors predicts higher uncertainty and greater noise in real estate prices in the short and medium run, and lower longitudinal risk in the long run. Our findings point to a signi ficant eff ect of institutional capital, which serves as a catalyst for structural changes in real estate market risk.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124812226","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}
This article develops an analytical methodology for decomposing non-linear portfolio risk not only by instrument, but also by fund managers or sub-portfolios for one single manager. Furthermore the approach may be used by quantitative portfolio managers for risk decomposition by factors under a factor investing strategy. We refer to this approach as ``Delta-Gamma Component Value-at-Risk'' (DG CVaR) as it decomposes VaR using an analytic approximation. The approach is well suited to funds holding any asset class or instrument type together with options. This decomposition approach is additive under non-linear portfolio returns, fully captures the correlations between instrument returns, and thus is well suited for decomposing risk by instrument, manager, sub-portfolio, or factor, modulo the limitations of VaR. We provide an example from a representative CTA portfolio that demonstrates superiority of the decomposition approach over other common practices for risk decomposition. The core methodology is implemented in R and made available to readers. The source can be found at https://github.com/mfrdixon/RiskDecomposition.
{"title":"Delta-Gamma Component VaR: Non-Linear Risk Decomposition for any Type of Funds","authors":"M. Dixon","doi":"10.2139/ssrn.2610188","DOIUrl":"https://doi.org/10.2139/ssrn.2610188","url":null,"abstract":"This article develops an analytical methodology for decomposing non-linear portfolio risk not only by instrument, but also by fund managers or sub-portfolios for one single manager. Furthermore the approach may be used by quantitative portfolio managers for risk decomposition by factors under a factor investing strategy. We refer to this approach as ``Delta-Gamma Component Value-at-Risk'' (DG CVaR) as it decomposes VaR using an analytic approximation. The approach is well suited to funds holding any asset class or instrument type together with options. This decomposition approach is additive under non-linear portfolio returns, fully captures the correlations between instrument returns, and thus is well suited for decomposing risk by instrument, manager, sub-portfolio, or factor, modulo the limitations of VaR. We provide an example from a representative CTA portfolio that demonstrates superiority of the decomposition approach over other common practices for risk decomposition. The core methodology is implemented in R and made available to readers. The source can be found at https://github.com/mfrdixon/RiskDecomposition.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126981158","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 confront prominent asset pricing models with the classical out-of-sample cross-sectional test of Fama and MacBeth (1973). For all models, we uncover three main findings: (i) the intercept coefficients are economically large and highly statistically significant; (ii) the cross-sectional factor risk premium estimates are far below the average factor excess returns; and (iii) they are generally not statistically significant. Thus, our findings show that the models do not only fail the equilibrium condition of the time-series test, but are also inconsistent with the weaker no-arbitrage condition. Overall, all new factor models cannot accurately explain the cross-section of stock returns.
{"title":"Testing Factor Models in the Cross-Section","authors":"Fabian Hollstein, Marcel Prokopczuk","doi":"10.2139/ssrn.3924777","DOIUrl":"https://doi.org/10.2139/ssrn.3924777","url":null,"abstract":"We confront prominent asset pricing models with the classical out-of-sample cross-sectional test of Fama and MacBeth (1973). For all models, we uncover three main findings: (i) the intercept coefficients are economically large and highly statistically significant; (ii) the cross-sectional factor risk premium estimates are far below the average factor excess returns; and (iii) they are generally not statistically significant. Thus, our findings show that the models do not only fail the equilibrium condition of the time-series test, but are also inconsistent with the weaker no-arbitrage condition. Overall, all new factor models cannot accurately explain the cross-section of stock returns.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122687831","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}
Quantum circuit learning is applied to computing option prices and their sensitivities. The advantage of this method is that a suitable choice of quantum circuit architecture makes it possible to compute the sensitivities analytically by applying parameter-shift rules. We expect our numerical result to pave the way for using quantum machine learning for option pricing.
{"title":"Quantum Circuit Learning to Compute Option Prices and Their Sensitivities","authors":"T. Sakuma","doi":"10.2139/ssrn.3922040","DOIUrl":"https://doi.org/10.2139/ssrn.3922040","url":null,"abstract":"Quantum circuit learning is applied to computing option prices and their sensitivities. The advantage of this method is that a suitable choice of quantum circuit architecture makes it possible to compute the sensitivities analytically by applying parameter-shift rules. We expect our numerical result to pave the way for using quantum machine learning for option pricing.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131839332","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}
This paper defines an asset’s risk as the likelihood that the asset can deliver at least a specific rate of return. Every asset which provides uncertain payoff has a corresponding put-call parity. The paper uses put option to construct the p-index to measure risk levels (likelihoods) of asset’s providing various rates of return, i.e., risk structure of asset. It shows that in the binomial case with up move and down move, (1) assets having lower down move have higher p-index, i.e., higher risk; (2) all call options have the same p-index, i.e., the same risk level, and all put options have the same p-index; and (3) underlying asset may be riskier than its put option and may have the same risk level as its call option. The trinomial example shows that the ranking of risk levels of assets’ providing different rates of returns could reverse. In the Black-Scholes-Merton model, assets having higher volatility have higher risk.
{"title":"Put Option and Risk Level of Asset","authors":"Kuo-Ping Chang","doi":"10.2139/ssrn.3920521","DOIUrl":"https://doi.org/10.2139/ssrn.3920521","url":null,"abstract":"This paper defines an asset’s risk as the likelihood that the asset can deliver at least a specific rate of return. Every asset which provides uncertain payoff has a corresponding put-call parity. The paper uses put option to construct the p-index to measure risk levels (likelihoods) of asset’s providing various rates of return, i.e., risk structure of asset. It shows that in the binomial case with up move and down move, (1) assets having lower down move have higher p-index, i.e., higher risk; (2) all call options have the same p-index, i.e., the same risk level, and all put options have the same p-index; and (3) underlying asset may be riskier than its put option and may have the same risk level as its call option. The trinomial example shows that the ranking of risk levels of assets’ providing different rates of returns could reverse. In the Black-Scholes-Merton model, assets having higher volatility have higher risk.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116793810","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}
A method is developed to determine the portfolio that maximizes the expected utility of an agent that trades the difference between a perceived future price distribution of an asset and the associated market implied risk neutral density. Exact results to construct and price such a portfolio are presented under the assumption that the underlying asset price evolves according to a geometric Brownian motion. Integer programming optimization techniques are applied to the general case where one first calibrates the asset price risk neutral density directly from option market data using Gatheral’s SVI parameterization. Several numerical examples approximating the optimal payoff function with liquid securities are given.
{"title":"On the Utility Maximization of the Discrepancy between a Perceived and Market Implied Risk Neutral Distribution","authors":"R. Navratil, S. Taylor, J. Vecer","doi":"10.2139/ssrn.3918087","DOIUrl":"https://doi.org/10.2139/ssrn.3918087","url":null,"abstract":"A method is developed to determine the portfolio that maximizes the expected utility of an agent that trades the difference between a perceived future price distribution of an asset and the associated market implied risk neutral density. Exact results to construct and price such a portfolio are presented under the assumption that the underlying asset price evolves according to a geometric Brownian motion. Integer programming optimization techniques are applied to the general case where one first calibrates the asset price risk neutral density directly from option market data using Gatheral’s SVI parameterization. Several numerical examples approximating the optimal payoff function with liquid securities are given.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122097379","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 construct measures of uncertainty and its dispersion exploiting the heterogeneity of a large set of model predictions. The approach is forward-looking, can be computed in real-time, and can be applied at any frequency. We illustrate the methodology with expected shortfall predictions of worldwide equity indices generated from 71 risk models. We use the new measures in asset pricing, risk forecasting, and for explaining the aggregate trading volume of S&P 500 firms.
{"title":"Measuring uncertainty and uncertainty dispersion from a large set of model predictions","authors":"David Ardia, A. Dufays","doi":"10.2139/ssrn.3917085","DOIUrl":"https://doi.org/10.2139/ssrn.3917085","url":null,"abstract":"We construct measures of uncertainty and its dispersion exploiting the heterogeneity of a large set of model predictions. The approach is forward-looking, can be computed in real-time, and can be applied at any frequency. We illustrate the methodology with expected shortfall predictions of worldwide equity indices generated from 71 risk models. We use the new measures in asset pricing, risk forecasting, and for explaining the aggregate trading volume of S&P 500 firms.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126115628","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}
This paper formally implements time-varying risk price models for currency returns. Focusing upon time variation in risk prices, the paper explores four currency risk factors. In addition to dollar and carry factors, we employ momentum and value factors which are widely used by currency investors. We find time variation in risk prices for the dollar factor is associated with the U.S. business cycle, with notable increases at the end of economic downturns. Constant beta models moreover have smaller pricing errors across all currency portfolios, which is in contrast to the stock and bond markets.
{"title":"The Time-Varying Risk Price of Currency Portfolios","authors":"Joseph P. Byrne, B. M. Ibrahim, Ryuta Sakemoto","doi":"10.2139/ssrn.3926927","DOIUrl":"https://doi.org/10.2139/ssrn.3926927","url":null,"abstract":"This paper formally implements time-varying risk price models for currency returns. Focusing upon time variation in risk prices, the paper explores four currency risk factors. In addition to dollar and carry factors, we employ momentum and value factors which are widely used by currency investors. We find time variation in risk prices for the dollar factor is associated with the U.S. business cycle, with notable increases at the end of economic downturns. Constant beta models moreover have smaller pricing errors across all currency portfolios, which is in contrast to the stock and bond markets.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114324557","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}
This online appendix (OA) contains proofs and additional results to the paper Ebert and Karehnke (2021) “Skewness Preferences in Choice under Risk.” Online Appendix OA.1 shows the proofs of the results in the main text. Online Appendix OA.2 studies behavioral implications of the orders of skewness-seeking and how they determine the trade-off of skewness against mean or variance. Online Appendix OA.3 characterizes skewness preferences in leading theories of choice under risk. It completes the analysis of expected utility (EU), rank-dependent utility (RDU), and cumulative prospect theory (CPT) in main text, and further covers moment-based, context-dependent, and belief-based theories of choice under risk. Online Appendix OA.4 provides an extensive analysis of the skewness preferences induced by probability weighting, as in RDU and CPT. Online Appendix OA.5 proposes two new utility functions that yield second-order skewness-seeking EU. Online Appendix OA.6 clarifies the relationship between the orders of skewness preference, the skewness risk premium, and the willingness to pay for skewness. Online Appendix OA.7 illustrates and discusses some technical aspects of this paper that concern the use and computation of one-sided derivatives. Online Appendix OA.8 presents the proofs to all results in the online appendix.
{"title":"Online Appendix: Skewness Preferences in Choice under Risk","authors":"S. Ebert, Paul Karehnke","doi":"10.2139/ssrn.3903202","DOIUrl":"https://doi.org/10.2139/ssrn.3903202","url":null,"abstract":"This online appendix (OA) contains proofs and additional results to the paper Ebert and Karehnke (2021) “Skewness Preferences in Choice under Risk.” Online Appendix OA.1 shows the proofs of the results in the main text. Online Appendix OA.2 studies behavioral implications of the orders of skewness-seeking and how they determine the trade-off of skewness against mean or variance. Online Appendix OA.3 characterizes skewness preferences in leading theories of choice under risk. It completes the analysis of expected utility (EU), rank-dependent utility (RDU), and cumulative prospect theory (CPT) in main text, and further covers moment-based, context-dependent, and belief-based theories of choice under risk. Online Appendix OA.4 provides an extensive analysis of the skewness preferences induced by probability weighting, as in RDU and CPT. Online Appendix OA.5 proposes two new utility functions that yield second-order skewness-seeking EU. Online Appendix OA.6 clarifies the relationship between the orders of skewness preference, the skewness risk premium, and the willingness to pay for skewness. Online Appendix OA.7 illustrates and discusses some technical aspects of this paper that concern the use and computation of one-sided derivatives. Online Appendix OA.8 presents the proofs to all results in the online appendix.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131782464","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 develop a model of deposit insurer choices for pricing deposit insurance, determining the target insurance fund, resolving bank failures and managing insurer investments. The academic literature and law treat these four areas as separate processes. Deposit insurers’ experience, however, shows there are trade-offs between these operations. We use a risk aggregation model (copula) to combine multiple insurer revenue and expense streams. We apply ruin theory, common to insurance literature but not previously used for deposit insurers, to these streams to study target fund estimates and insurer insolvency risk. Our results suggest a target fund for the FDIC (as an example) of $98 billion as of year-end 2019 based on a 99.97 percent confidence level for fund solvency; the official FDIC target fund is $150 billion as of year-end 2019. Next, we test alternative scenarios for achieving a target fund and show changes in probability of ruin and fund capital under various funding strategies a deposit insurer could employ.
{"title":"Bridging the Gap between the Deposit Insurance Fund Target Level and the Current Fund Level","authors":"Charles Kusaya, J. O'Keefe, Alex Ufier","doi":"10.2139/ssrn.3605487","DOIUrl":"https://doi.org/10.2139/ssrn.3605487","url":null,"abstract":"We develop a model of deposit insurer choices for pricing deposit insurance, determining the target insurance fund, resolving bank failures and managing insurer investments. The academic literature and law treat these four areas as separate processes. Deposit insurers’ experience, however, shows there are trade-offs between these operations. We use a risk aggregation model (copula) to combine multiple insurer revenue and expense streams. We apply ruin theory, common to insurance literature but not previously used for deposit insurers, to these streams to study target fund estimates and insurer insolvency risk. Our results suggest a target fund for the FDIC (as an example) of $98 billion as of year-end 2019 based on a 99.97 percent confidence level for fund solvency; the official FDIC target fund is $150 billion as of year-end 2019. Next, we test alternative scenarios for achieving a target fund and show changes in probability of ruin and fund capital under various funding strategies a deposit insurer could employ.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127058180","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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