Query: buehler hedging

title: Deep Hedging
abstract: We present a framework for hedging a portfolio of derivatives in the presence of market frictions such as transaction costs, market impact, liquidity constraints or risk limits using modern deep reinforcement machine learning methods. We discuss how standard reinforcement learning methods can be applied to non linear reward structures, i.e. in our case convex risk measures. As a general contribution to the use of deep learning for stochastic processes, we also show that the set of constrained trading strategies used by our algorithm is large enough to \epsilon$ approximate any optimal solution. Our algorithm can be implemented efficiently even in high dimensional situations using modern machine learning tools. Its structure does not depend on specific market dynamics, and generalizes across hedging instruments including the use of liquid derivatives. Its computational performance is largely invariant in the size of the portfolio as it depends mainly on the number of hedging instruments available. We illustrate our approach by showing the effect on hedging under transaction costs in a synthetic market driven by the Heston model, where we outperform the standard "complete market" solution.

title: Deep Hedging: Hedging Derivatives Under Generic Market Frictions Using Reinforcement Learning
abstract: This article discusses a new application of reinforcement learning: to the problem of hedging a portfolio of "over the counter" derivatives under under market frictions such as trading costs and liquidity constraints. It is an extended version of our recent work https:/www.ssrn.com/abstract=3120710, here using notation more common in the machine learning literature. The objective is to maximize a non linear risk adjusted return function by trading in liquid hedging instruments such as equities or listed options. The approach presented here is the first efficient and model independent algorithm which can be used for such problems at scale.

title: Deep Hedging: Learning to Simulate Equity Option Markets
abstract: We construct realistic equity option market simulators based on generative adversarial networks (GANs) We consider recurrent and temporal convolutional architectures, and assess the impact of state compression. Option market simulators are highly relevant because they allow us to extend the limited real world data sets available for the training and evaluation of option trading strategies. We show that network based generators outperform classical methods on a range of benchmark metrics, and adversarial training achieves the best performance. Our work demonstrates for the first time that GANs can be successfully applied to the task of generating multivariate financial time series.

title: Neural Networks for Option Pricing and Hedging: A Literature Review
abstract: Neural networks have been used as a nonparametric method for option pricing and hedging since the early 1990s. Far over a hundred papers have been published on this topic. This note intends to provide a comprehensive review. Papers are compared in terms of input features, output variables, benchmark models, performance measures, data partition methods, and underlying assets. Furthermore, related work and regularisation techniques are discussed.

title: Dynamic Replication and Hedging: A Reinforcement Learning Approach
abstract: The authors of this article address the problem of how to optimally hedge an options book in a practical setting, where trading decisions are discrete and trading costs can be nonlinear and difficult to model. Based on reinforcement learning (RL) a well established machine learning technique, the authors propose a model that is flexible, accurate and very promising for real world applications. A key strength of the RL approach is that it does not make any assumptions about the form of trading cost. RL learns the minimum variance hedge subject to whatever transaction cost function one provides. All that it needs is a good simulator, in which transaction costs and options prices are simulated accurately.

title: No differences in ventral striatum responsivity between adolescents with a positive family history of alcoholism and controls
abstract: Individuals with alcohol dependent parents show an elevated risk of developing alcohol related problems themselves. Modulations of the mesolimbic reward circuit have been postulated as a pre existing marker of alcoholism. We tested whether a positive family history of alcoholism is correlated with ventral striatum functionality during a reward task. All participants performed a modified version of the monetary incentive delay task while their brain responses were measured with functional magnetic resonance imaging. We compared 206 healthy adolescents (aged 13 15) who had any first or second degree relative with alcoholism to 206 matched controls with no biological relative with alcoholism. Reward anticipation as well as feedback of win recruited the ventral striatum in all participants, but adolescents with a positive family history of alcoholism did not differ from their matched peers. Also we did not find any correlation between family history density and reward anticipation or feedback of win. This finding of no differences did not change when we analyzed a subsample of 77 adolescents with at least one parent with alcohol use disorder and their matched controls. Because this result is in line with another study reporting no differences between children with alcohol dependent parents and controls at young age, but contrasts with studies of older individuals, one might conclude that at younger age the effect of family history has not yet exerted its influence on the still developing mesolimbic reward circuit.

title: Bigger Not Better: Unpacking Future Expenses Inflates Spending Predictions
abstract: People often underestimate their future personal spending. Across four studies we examined an "unpacking" intervention to reduce this bias. Participants predicted spending for an upcoming week (Study 1) a weekend (Study 2a) a vacation (Study 2b) and for weeks versus self nominated events (Study 3) and subsequently reported actual spending. In each case, unpacking the details of expected expenses increased spending predictions. In contexts where predictions tended to be too low (Study 1, 3) unpacking eliminated underestimation bias. However, in contexts where predictions were already unbiased, unpacking introduced an overestimation bias (Study 2, 3) Unpacking appears to make predictions bigger, not necessarily better.

title: Effectiveness of Practices To Increase Timeliness of Providing Targeted Therapy for Inpatients with Bloodstream Infections: a Laboratory Medicine Best Practices Systematic Review and Meta analysis
abstract: Bloodstream infection (BSI) is a major cause of morbidity and mortality throughout the world. Rapid identification of bloodstream pathogens is a laboratory practice that supports strategies for rapid transition to direct targeted therapy by providing for timely and effective patient care. In fact, the more rapidly that appropriate antimicrobials are prescribed, the lower the mortality for patients with sepsis. Rapid identification methods may have multiple positive impacts on patient outcomes, including reductions in mortality, morbidity, hospital lengths of stay, and antibiotic use. In addition, the strategy can reduce the cost of care for patients with BSIs.  The purpose of this review is to evaluate the evidence for the effectiveness of three rapid diagnostic practices in decreasing the time to targeted therapy for hospitalized patients with BSIs. The review was performed by applying the Centers for Disease Control and Prevention's (CDC's) Laboratory Medicine Best Practices Initiative (LMBP) systematic review methods for quality improvement (QI) practices and translating the results into evidence based guidance (R. H. Christenson et al.  2011, http:/dx.doi.org/10.1373/clinchem.2010.157131) Search strategy. A comprehensive literature search was conducted to identify studies with measurable outcomes. A search of three electronic bibliographic databases (PubMed, Embase, and CINAHL) databases containing "gray" literature (unpublished academic, government, or industry evidence not governed by commercial publishing) (CIHI, NIHR, SIGN, and other databases) and the Cochrane database for English language articles published between 1990 and 2011 was conducted in July 2011. Dates of search. The dates of our search were from 1990 to July 2011. Selection criteria. Animal studies and non English publications were excluded. The search contained the following medical subject headings: bacteremia; bloodstream infection; time factors; health care costs; length of stay; morbidity; mortality; antimicrobial therapy; rapid molecular techniques, polymerase chain reaction (PCR) in situ hybridization, fluorescence; treatment outcome; drug therapy; patient care team; pharmacy service, hospital; hospital information systems; Gram stain; pharmacy service; and spectrometry, mass, matrix assisted laser desorption ionization. Phenotypic as well as the following key words were searched: targeted therapy; rapid identification; rapid; Gram positive; Gram negative; reduce(ed) cost(s) pneumoslide; PBP2; tube coagulase; matrix assisted laser desorption/ionization time of flight; MALDI TOF; blood culture; EMR; electronic reporting; call to provider; collaboration; pharmacy; laboratory; bacteria; yeast; ICU; and others. In addition to the electronic search being performed, a request for unpublished quality improvement data was made to the clinical laboratory community.  Rapid molecular testing with direct communication significantly improves timeliness compared to standard testing. Rapid phenotypic techniques with direct communication likely improve the timeliness of targeted therapy. Studies show a significant and homogeneous reduction in mortality associated with rapid molecular testing combined with direct communication.  No recommendation is made for or against the use of the three assessed practices of this review due to insufficient evidence. The overall strength of evidence is suggestive; the data suggest that each of these three practices has the potential to improve the time required to initiate targeted therapy and possibly improve other patient outcomes, such as mortality. The meta analysis results suggest that the implementation of any of the three practices may be more effective at increasing timeliness to targeted therapy than routine microbiology techniques for identification of the microorganisms causing BSIs. Based on the included studies, results for all three practices appear applicable across multiple microorganisms, including methicillin resistant Staphylococcus aureus (MRSA) methicillin sensitive S. aureus (MSSA) Candida species, and Enterococcus species.

title: Model Independent Estimation of Optimal Hedging Strategies with Deep Neural Networks
abstract: MODEL INDEPENDENT ESTIMATION OF OPTIMAL HEDGING STRATEGIES WITH DEEP NEURAL NETWORKS by Tobias Michael Furtwaengler The University of Wisconsin Milwaukee, 2019 Under the Supervision of Professor Chao Zhu Inspired by the recent paper Buehler et al. (2018) this thesis aims to investigate the optimal hedging and pricing of financial derivatives with neural networks. We utilize the concept of convex risk measures to define optimal hedging strategies without strong assumptions on the underlying market dynamics. Furthermore, the setting allows the incorporation of market frictions and thus the determination of optimal hedging strategies and prices even in incomplete markets. We then use the approximation capabilities of neural networks to find close to optimal estimates for these strategies. We will elaborate on the theoretical foundations of this approach and carry out implementations and a detailed analysis of the method with simulated market data. Our experiments show that the neural network based algorithm is a powerful tool for the model independent pricing of any financial derivative and the estimation of optimal hedging strategies for these instruments.

title: Non Transferable Non Hedgeable Executive Stock Option Pricing
abstract: To value non transferable non hedgeable (NTNH) contingent claims and price executive stock options (ESOs) we use a replication argument to translate portfolios with NTNH derivatives into portfolios of primary assets (only) with stochastic portfolio constraints. By identifying stochastic discount factors and finding subjective prices of NTNH European and American ESOs, for block and continuous partial exercise, we derive executives' optimal exercise policies, and use these to find objective prices/costs of ESOs to firms. Through numerical simulations, we obtain policy implications regarding ESOs' incentivizing efficiency. For the first time, we demonstrate that, unlike under block exercise, subjective prices under continuous partial exercise may be higher than objective ones. Moreover, volatility regimes and executives' "other wealth" are important in ESO pricing, and are thus essential to empirical executive compensation studies.