ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)最新文献

{"title":"Spike Modeling for Interest Rate Derivatives with an Application to SOFR Caplets","authors":"Leif Andersen, Dominique R. A. Bang","doi":"10.2139/ssrn.3700446","DOIUrl":"https://doi.org/10.2139/ssrn.3700446","url":null,"abstract":"With the forthcoming introduction of SOFR benchmark rates in the US, market participants will need to adjust their interest rate option models to accommodate a variety of idiosyncrasies of the SOFR rate. The materiality of these changes for quoted options level is currently unknown, and will depend on market sentiment (as expressed in market risk premia, say), regulatory policies, and the rate fixing conventions ultimately available in the market. While we wait for liquidity in SOFR options to build, this paper pre-emptively considers two important characteristics of SOFR derivatives: the backward-looking settlement style of SOFR floating rate payments; and the “jagged” nature of SOFR evolution through time. The latter originates with liquidity conditions in the repo financing markets from which SOFR is constructed, where temporary demand-supply imbalances can result in the formation of short-term spikes of substantial magnitude. We construct a variety of mechanisms that allows us to build rich stochastic models for both “surprising” and anticipated (e.g., year-end) spikes, and demonstrate how to modify existing (smooth) term structure models to capture them. To accommodate high-efficiency pricing of vanilla derivatives in top-down models, we also develop several convenient numerical techniques that allow for effcient pricing of these structures. For instance, a novel scheme merges existing spike-free pricing formulas with a given spike characteristic function in a custom low-dimensional quadrature routine, enabling us to spike-enable standard valuation models (such as SABR) at minimal computational effort. Using SOFR-style caplets for illustration, we numerically demonstrate that the effect of spikes on implied caplet volatility levels and skews can be substantial, even at modest levels of risk premia in the spike model parameters. Besides being useful for the pricing of SOFR derivatives, our paper more broadly establishes a complete mathematical framework for rate spikes, applicable to pricing, scenario generation, and risk management in any rates market where spike phenomena exist.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130355256","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 Forecast Superiority Testing with an Application to Assessing Pools of Expert Forecasters","authors":"V. Corradi, Sainan Jin, Norman R. Swanson","doi":"10.2139/ssrn.3538905","DOIUrl":"https://doi.org/10.2139/ssrn.3538905","url":null,"abstract":"We develop a forecast superiority testing methodology which is robust to the choice of loss function. Following Jin, Corradi and Swanson (JCS: 2017), we rely on a mapping between generic loss forecast evaluation and stochastic dominance principles. However, unlike JCS tests, which are not uniformly valid, and have correct asymptotic size only under the least favorable case, our tests are uniformly asymptotically valid and non-conservative. These properties are derived by first establishing uniform convergence (over error support) of HAC variance estimators and of their bootstrap counterparts, and by extending the asymptotic validity of generalized moment selection tests to the case of non-vanishing recursive parameter estimation error. Monte Carlo experiments indicate good finite sample performance of the new tests, and an empirical illustration suggests that prior forecast accuracy matters in the Survey of Professional Forecasters. Namely, for our longest forecast horizons (4 quarters ahead), selecting pools of expert forecasters based on prior accuracy results in ensemble forecasts that are superior to those based on forming simple averages and medians from the entire panel of experts.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129184263","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":"An Explainable Machine Learning Framework for Cross-Sectional Forecast-Based Fund Selection","authors":"Giulio Trichilo","doi":"10.2139/ssrn.3707595","DOIUrl":"https://doi.org/10.2139/ssrn.3707595","url":null,"abstract":"Since the 1990’s the global hedge fund industry has seen a rapid expansion. Its growing presence in financial markets ranging from equity, fixed income and derivative markets has inextricably linked it to the broader financial industry, with larger funds effectively acting as a market makers and liquidity providers in many markets. For both academics and practitioners, the space has established itself as a key area of research given the vast heterogeneity of investment styles and the high mutability of the industry. In this thesis, a cross sectional fund selection approach which builds upon the paradigm of explainable machine learning is proposed in a fully systematic setting. Four fund performance metrics, Sharpe and Sortino ratios, fund alpha and its t-statistic, are used as the ranking and selection metric, at investable inter-regime forecast horizons of 24 and 36 months. We find that quintile portfolios constructed from machine learning and deep learning approaches outperform linear models and benchmark portfolios constructed exclusively based on historical realizations of the forecast metric, in terms of absolute and risk-adjusted performance. We find that the extreme quintile portfolios realize a high (resp. low) value of the performance metric employed as forecast metric in model training. We find forecasting on the Sortino ratio to yield the most consistent overall performance, and find particular benefit in employing machine learning methods for bottom quintile fund selection (consistent identification of under-performers) in the case of forecasting on fund alpha. Explainability, achieved via the use of SHAP values further serves the purpose of outlining feature importance both at the aggregate and the individual fund level. At the aggregate level, all methods agree on a subset of statistically consistent predictors across investment style and forecast horizon; with discernible relevance of predictors constructed from interactions of fund returns with nowcasters, and management quality indicators. This consistency enables a discretionary fund selection process to be complemented by model forecasts and SHAP value-based feature importance delineations. There is thus evidence that proposed approach may be valuable for a discretionary fund manager looking to incorporate machine learning based signals into their selection process.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134556695","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 Low-Dimension Shrinkage Approach to Choice-Based Conjoint Estimation","authors":"Yupeng Chen, R. Iyengar","doi":"10.2139/ssrn.3672517","DOIUrl":"https://doi.org/10.2139/ssrn.3672517","url":null,"abstract":"Estimating consumers' heterogeneous preferences using choice-based conjoint (CBC) data poses a considerable modeling challenge, as the amount of information elicited from each consumer is often limited. Given the lack of individual-level information, effective information pooling across consumers becomes critical for accurate CBC estimation. In this paper, we propose an innovative low-dimension shrinkage approach to pooling information and modeling preference heterogeneity, in which we learn a low-dimensional affine subspace approximation of the heterogeneity distribution and shrink the individual-level part-worth estimates toward this affine subspace. Drawing on recent modeling techniques for low-rank matrix recovery, we develop a computationally tractable machine learning model for implementing this low-dimension shrinkage and apply it to CBC estimation. We use an extensive simulation experiment and a field data set to demonstrate the superior performance of our low-dimension shrinkage approach as compared to alternative benchmark models.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124855785","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":"Discussing Copulas with Sergey Aivazian: A Memoir","authors":"Dean Fantazzini","doi":"10.2139/ssrn.3669763","DOIUrl":"https://doi.org/10.2139/ssrn.3669763","url":null,"abstract":"Sergey Aivazian was the head of my department at the Moscow School of Economics, but he was much more than that. He played an important role in my life, and he contributed to my studies devoted to copula modelling. This small memoir reports how this amazingly polite and smart scientist helped me to develop my academic skills and to further stimulate my interest in multivariate modelling and risk management. Some open questions related to multivariate discrete models that were among the last topics I discussed with Sergey are reported, hoping they can be of interest to young researchers for further studies.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132422784","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":"Ranking Distributions when only Means and Variances are Known","authors":"A. Müller, M. Scarsini, Ilia Tsetlin, R. L. Winkler","doi":"10.2139/ssrn.3440103","DOIUrl":"https://doi.org/10.2139/ssrn.3440103","url":null,"abstract":"In “Technical Note—Ranking Distributions When Only Means and Variances Are Known,” Müller, Scarsini, Tsetlin, and Winkler address the question of ranking distributions when only the first two moments—that is, means and variances—are known. This is important in decision making under uncertainty, with potential applications in economics, finance, statistics, and other areas. Previous results require some assumptions about the shape of the distributions, while this paper’s approach is to impose bounds on how much marginal utility can change, thus constraining risk preferences. Such a ranking is consistent with almost stochastic dominance and provides a new connection between the Sharpe and Omega ratios from finance.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133839574","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":"Understanding Large-Scale Dynamic Purchase Behavior","authors":"Bruno Jacobs, D. Fok, B. Donkers","doi":"10.2139/ssrn.3680678","DOIUrl":"https://doi.org/10.2139/ssrn.3680678","url":null,"abstract":"A scalable model-based approach to gain insights in dynamic purchase behavior for large product assortments and customer bases.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124855224","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":"Deep Learning Modeling of the Limit Order Book: A Comparative Perspective","authors":"Antonio Briola, J. Turiel, T. Aste","doi":"10.2139/ssrn.3714230","DOIUrl":"https://doi.org/10.2139/ssrn.3714230","url":null,"abstract":"The present work addresses theoretical and practical questions in the domain of Deep Learning for High Frequency Trading, with a thorough review and analysis of the literature and state-of-the-art models. Random models, Logistic Regressions, LSTMs, LSTMs equipped with an Attention mask, CNN-LSTMs and MLPs are compared on the same tasks, feature space, and dataset and clustered according to pairwise similarity and performance metrics. The underlying dimensions of the modeling techniques are hence investigated to understand whether these are intrinsic to the Limit Order Book's dynamics. It is possible to observe that the Multilayer Perceptron performs comparably to or better than state-of-the-art CNN-LSTM architectures indicating that dynamic spatial and temporal dimensions are a good approximation of the LOB's dynamics, but not necessarily the true underlying dimensions.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131432176","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":"Pricing American Drawdown Options under Markov Models","authors":"Xiang Zhang, Lingfei Li, Gongqiu Zhang","doi":"10.2139/ssrn.3648921","DOIUrl":"https://doi.org/10.2139/ssrn.3648921","url":null,"abstract":"Abstract The drawdown in the price of an asset shows how much the price falls relative to its historical maximum. This paper considers the pricing problem of perpetual American style drawdown call options, which allow the holder to optimally choose the time to receive a call payoff written on the drawdown. Our pricing framework includes classical Russian options and American lookback puts as special cases after a suitable equivalent measure change. We approximate the original asset price model by a continuous time Markov chain and develop two types of algorithms to solve the optimal stopping problem for the drawdown process. The first one is a transform based algorithm which is applicable to general exponential Levy models. The second approach solves the linear complementarity problem (LCP) associated with the variational inequalities for the value function and it applies to general Markov models. We propose an efficient Block-LCP (BLCP) method that reduces an LCP with big size to a sequence of sub-LCPs with mild size which can be solved by a variety of LCP solvers and we identify the best solver through numerical experiments. Convergence of Markov chain approximation is proved and various numerical examples are given to demonstrate their computational efficiency and convergence properties. An extension of the BLCP method to the finite maturity case is also provided.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124620146","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":"Multilevel Modeling for Economists: Why, When and How","authors":"A. Oshchepkov, A. Shirokanova","doi":"10.2139/ssrn.3637907","DOIUrl":"https://doi.org/10.2139/ssrn.3637907","url":null,"abstract":"Multilevel modeling (MLM, also known as hierarchical linear modeling, HLM) is a methodological framework widely used in the social sciences to analyze data with a hierarchical structure, where lower units of aggregation are ‘nested’ in higher units, including longitudinal data. In economics, however, MLM is used very rarely. Instead, economists use separate econometric techniques including cluster-robust standard errors and fixed effects models. In this paper, we review the methodological literature and contrast the econometric techniques typically used in economics with the analysis of hierarchical data using MLM. Our review suggests that economic techniques are generally less convenient, flexible, and efficient compared to MLM. The important limitation of MLM, however, is its inability to deal with the omitted variable problem at the lowest level of data, while standard economic techniques may be complemented by quasi-experimental methods mitigating this problem. It is unlikely, though, that this limitation can explain and justify the rare use of MLM in economics. Overall, we conclude that MLM has been unreasonably ignored in economics, and we encourage economists to apply this framework by providing ‘when and how’ guidelines","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126000959","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}