{"title":"Data-Driven Optimization of Reward-Risk Ratio Measures","authors":"Ran Ji, M. Lejeune","doi":"10.2139/ssrn.2707122","DOIUrl":"https://doi.org/10.2139/ssrn.2707122","url":null,"abstract":"We investigate a class of fractional distributionally robust optimization problems with uncertain probabilities. They consist in the maximization of ambiguous fractional functions representing reward-risk ratios and have a semi-infinite programming epigraphic formulation. We derive a new fully parameterized closed-form to compute a new bound on the size of the Wasserstein ambiguity ball. We design a data-driven reformulation and solution framework. The reformulation phase involves the derivation of the support function of the ambiguity set and the concave conjugate of the ratio function. We design modular bisection algorithms which enjoy the finite convergence property. This class of problems has wide applicability in finance, and we specify new ambiguous portfolio optimization models for the Sharpe and Omega ratios. The computational study shows the applicability and scalability of the framework to solve quickly large, industry-relevant-size problems, which cannot be solved in one day with state-of-the-art mixed-integer nonlinear programming (MINLP) solvers.","PeriodicalId":344568,"journal":{"name":"Advanced Risk & Portfolio Management® Research Paper Series","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126800844","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}
{"title":"A Short Sharpe Course","authors":"Steven E. Pav","doi":"10.2139/SSRN.3036276","DOIUrl":"https://doi.org/10.2139/SSRN.3036276","url":null,"abstract":"A fully contained short course on the statistics of the Sharpe ratio.","PeriodicalId":344568,"journal":{"name":"Advanced Risk & Portfolio Management® Research Paper Series","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116973123","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}
{"title":"What You See Is Not What You Get: The Costs of Trading Market Anomalies","authors":"Andrew J. Patton, Brian M. Weller","doi":"10.2139/ssrn.3034796","DOIUrl":"https://doi.org/10.2139/ssrn.3034796","url":null,"abstract":"Abstract Is there a gap between the profitability of a trading strategy on paper and that which is achieved in practice? We answer this question by developing a general technique to measure the real-world implementation costs of financial market anomalies. Our method extends Fama-MacBeth regressions to compare the on-paper returns to factor exposures with those achieved by mutual funds. Unlike existing approaches, ours delivers estimates of all-in implementation costs without relying on parametric microstructure models or explicitly specified factor trading strategies. After accounting for implementation costs, typical mutual funds earn low returns to value and no returns to momentum.","PeriodicalId":344568,"journal":{"name":"Advanced Risk & Portfolio Management® Research Paper Series","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134100036","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}
{"title":"Learning Curve Dynamics with Artificial Neural Networks","authors":"A. Kondratyev","doi":"10.2139/ssrn.3041232","DOIUrl":"https://doi.org/10.2139/ssrn.3041232","url":null,"abstract":"Our objective is to learn the natural curve shapes with the help of Artificial Neural Networks (ANN). Our research aims to improve curve dynamics generated by the parametric models and the PCA. By running the ANN on the dataset of historically observed term structures of forward commodity prices we learn how the curves evolve over time. This allows us to determine stable natural shapes of the curves as well as to detect statistically significant deviations from the expected, predicted behaviour.","PeriodicalId":344568,"journal":{"name":"Advanced Risk & Portfolio Management® Research Paper Series","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125119295","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}
{"title":"A Risk-Neutral Approach for the Evaluation of NPLs","authors":"D. Tilloca","doi":"10.2139/ssrn.2996565","DOIUrl":"https://doi.org/10.2139/ssrn.2996565","url":null,"abstract":"This paper describes a new methodology which allows banks to evaluate non-performing loans (NPL) using a risk-neutral approach. In more detail, it illustrates the methodological framework behind the definition of the risk-neutral expected loss used to estimate the loans fair value. The risk-neutral expected loss is calculated by extending the Merton framework for modeling corporate liabilities. The proposed risk-neutral approach is suitable at producing estimates, in a fair value computation context, that are as close as possible to the exit price as mandated by IFRS-13 and is not biased by the stressed market conditions often observed on defaulted debt. In this context the pricing model is able to provide good estimates especially when applied to large portfolios (where the diosyncratic risk is low) and is requiring few parameters to work: the expected recovery, the recovery time, the market risk-premium and the default vintage. A model that can help the banks to estimate a “fair” price for this kind of asset can help in reducing the difference between the asked price (for a bank often is the book value) and the offered market price, facilitating the development of a market for NPLs.","PeriodicalId":344568,"journal":{"name":"Advanced Risk & Portfolio Management® Research Paper Series","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115531825","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}
{"title":"Exploring Mispricing in the Term Structure of CDS Spreads","authors":"R. Jarrow, Haitao Li, Xiaoxia Ye, May Hu","doi":"10.1093/rof/rfy014","DOIUrl":"https://doi.org/10.1093/rof/rfy014","url":null,"abstract":"Based on a reduced-form model of credit risk, we explore mispricing in the credit default swaps (CDS) spreads of North American companies and its economic content. Specifically, we develop a trading strategy using the model to trade out of sample market-neutral portfolios across the term structure of CDS contracts. Our empirical results show that the trading strategy exhibits abnormally large returns, confirming the existence and persistence of a mispricing. The aggregate returns of the trading strategy are positively related to the square of market-wide credit and liquidity risks, indicating that the mispricing is more pronounced when the market is more volatile. When implemented on the Markit data, the strategy shows significant economic value even after controlling for realistic transaction costs.","PeriodicalId":344568,"journal":{"name":"Advanced Risk & Portfolio Management® Research Paper Series","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128126107","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}
{"title":"Alpha Momentum and Price Momentum","authors":"H. Hühn, H. Scholz","doi":"10.2139/SSRN.2287848","DOIUrl":"https://doi.org/10.2139/SSRN.2287848","url":null,"abstract":"We analyze a novel alpha momentum strategy that invests in stocks based on three-factor alphas which we estimate using daily returns. The empirical analysis for the U.S. and for Europe shows that (i) past alpha has power in predicting the cross-section of stock returns; (ii) alpha momentum exhibits less dynamic factor exposures than price momentum and (iii) alpha momentum dominates price momentum only in the U.S. Connecting both strategies to behavioral explanations, alpha momentum is more related to an underreaction to firm-specific news while price momentum is primarily driven by price overshooting due to momentum trading.","PeriodicalId":344568,"journal":{"name":"Advanced Risk & Portfolio Management® Research Paper Series","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122647437","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}
{"title":"Risk Control of Mean-Reversion Time in Statistical Arbitrage","authors":"Joongyeub Yeo, G. Papanicolaou","doi":"10.2139/ssrn.2868525","DOIUrl":"https://doi.org/10.2139/ssrn.2868525","url":null,"abstract":"This paper deals with the risk of mean-reversions in statistical arbitrage. The basic concept of statistical arbitrage is to exploit short-term deviations in returns from a long-term equilibrium across several assets. This kind of strategy heavily relies on the assumption of mean-reversion of idiosyncratic returns - reverting to a long-term mean after a certain amount of time, but literature on the assessment of risk on this belief is rare. In this paper, we propose a simple scheme that controls the risk on mean-reversions, via portfolio selections and screenings. Realizing that each residual exhibits different mean-reversion time, the residuals that have fast mean-reverting speed are selected to form portfolios. In addition, further control is imposed by allowing the trading activity only when the goodness-of-fit of the estimation for trading signals is sufficiently high. Furthermore, we design the dynamic asset allocation with the market- and dollar-neutrality conditions as a constrained optimization problem, which can be solved numerically. The improved reliability and robustness of the strategy from our method is demonstrated through back-testing results. It has been found that the performance of this investment framework is robust to market fluctuations. We further provide an answer to a puzzled relation among the number of factors, lengths of estimation window, and transaction costs, which are crucial parameters that have direct impacts on the strategy.","PeriodicalId":344568,"journal":{"name":"Advanced Risk & Portfolio Management® Research Paper Series","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128500969","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}
{"title":"The Case for Bitcoin for Institutional Investors: Bubble Investing or Fundamentally Sound?","authors":"J. Liew, Levar Hewlett","doi":"10.2139/ssrn.3082808","DOIUrl":"https://doi.org/10.2139/ssrn.3082808","url":null,"abstract":"In this work we assume the institutional investor role and analyze a possible investment in bitcoin (BTC). We document several salient features of BTC employing monthly returns over the period from August 2010 to October 2017. First, it provides unique diversification benefits for traditional institutional portfolios. This diversification benefit appears to be stable over our sample period. Second, bitcoin is very volatile, indeed, but the historical return to risk ratio appears attractive with a Sharpe Ratio of 1.176. Finally, using standard portfolio optimization tools we find that the optimal allocation to BTC is 1.3% over the sample we examined. We provide controversial evidence that institutional investors are under allocated to BTC.","PeriodicalId":344568,"journal":{"name":"Advanced Risk & Portfolio Management® Research Paper Series","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134118794","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}
{"title":"Robust Portfolio Optimization with Multivariate Copulas: A Worst-Case CVaR Approach","authors":"Fernando B. Sabino da Silva, Flávio Ziegelman","doi":"10.2139/ssrn.3076283","DOIUrl":"https://doi.org/10.2139/ssrn.3076283","url":null,"abstract":"Using data from the S&P 500 stocks from 1990 to 2015, we address the uncertainty of distribution of assets’ returns in Conditional Value-at-Risk (CVaR) minimization model by applying multidimensional mixed Archimedean copula function and obtaining its robust counterpart. We implement a dynamic investing viable strategy where the portfolios are optimized using three different length of rolling calibration windows. The out-of-sample performance is evaluated and compared against two benchmarks: a multidimensional Gaussian copula model and a constant mix portfolio. Our empirical analysis shows that the Mixed Copula-CVaR approach generates portfolios with better downside risk statistics for any rebalancing period and it is more profitable than the Gaussian Copula-CVaR and the 1/N portfolios for daily and weekly rebalancing. To cope with the dimensionality problem we select a set of assets that are the most diversified, in some sense, to the S&P 500 index in the constituent set. The accuracy of the VaR forecasts is determined by how well they minimize a capital requirement loss function. In order to mitigate data-snooping problems, we apply a test for superior predictive ability to determine which model significantly minimizes this expected loss function. We find that the minimum average loss of the mixed Copula-CVaR approach is smaller than the average performance of the Gaussian Copula-CVaR.","PeriodicalId":344568,"journal":{"name":"Advanced Risk & Portfolio Management® Research Paper Series","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133807479","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}