Jiang Pu

@article{chataigner_1620,

title = {Nowcasting networks},

author = {Marc Chataigner and Stéphane Crépey and Jiang Pu},

url = {https://www.risk.net/journal-of-computational-finance/7806476/nowcasting-networks},

year  = {2020},

date = {2020-12-01},

journal = {Journal Of Computational Finance},

volume = {24},

number = {3},

pages = {1-39},

abstract = {We devise a neural network-based compression/completion methodology for financial nowcasting. The latter is meant in a broad sense, encompassing completion of gridded values, interpolation and outlier detection, in the context of financial time series of curves or surfaces. (It is also applicable in higher dimensions, at least in theory.) In particular, we introduce an original architecture amenable to the treatment of data defined at variable grid nodes (by far the most common situation in financial nowcasting applications, where principal component analysis (PCA) and classical autoencoder methods are not applicable). This is illustrated by three case studies on real data sets. First, we introduce our approach on repurchase agreement curves data (with a moving time-to maturity as calendar time passes). Second, we show that our approach outperforms elementary interpolation benchmarks on an equity derivative surfaces data set (again, with a moving time-to-maturity). We also obtain a satisfying performance for outlier detection and surface completion. Third, we benchmark our approach against PCA on at-the-money swaption surfaces redefined at constant expiry/tenor grid nodes. Our approach is then shown to perform as well as (even if not obviously better than) the PCA (which, however, is not applicable to the native, raw data defined on a moving time-to-expiry grid).},

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pubstate = {published},

tppubtype = {article}

}

@article{gueant_1619,

title = {Accelerated share repurchase and other buyback programs: what neural networks can bring},

author = {Olivier Guéant and Iiulia Manziuk and Jiang Pu},

url = {https://www.tandfonline.com/doi/abs/10.1080/14697688.2020.1729397},

year  = {2020},

date = {2020-04-01},

journal = {Quantitative Finance},

volume = {20},

number = {8},

pages = {1389-1404},

abstract = {When firms want to buy back their own shares, they have a choice between several alternatives. If they often carry out open market repurchase, they also increasingly rely on banks through complex buyback contracts involving option components, e.g. accelerated share repurchase contracts, VWAP-minus profit-sharing contracts, etc. The entanglement between the execution problem and the option hedging problem makes the management of these contracts a difficult task that should not boil down to simple Greek-based risk hedging, contrary to what happens with classical books of options. In this paper, we propose a machine learning method to optimally manage several types of buyback contract. In particular, we recover strategies similar to those obtained in the literature with partial differential equation and recombinant tree methods and show that our new method, which does not suffer from the curse of dimensionality, enables to address types of contract that could not be addressed with grid or tree methods.},

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pubstate = {published},

tppubtype = {article}

}

@article{gueant_1617,

title = {Mid-price estimation for European corporate bonds: a particle filtering approach},

author = {Olivier Guéant and Jiang Pu},

url = {https://www.worldscientific.com/doi/abs/10.1142/S2382626619500059},

year  = {2019},

date = {2019-08-01},

journal = {Market Microstructure and Liquidity},

volume = {4},

number = {no 01n02},

pages = {1950005},

abstract = {In most illiquid markets, there is no obvious proxy for the market price of an asset. The European corporate bond market is an archetypal example of such an illiquid market where mid-prices can only be estimated with a statistical model. In this OTC market, dealers/market makers only have access, indeed, to partial information about the market. In real time, they know the price associated with their trades on the dealer-to-dealer (D2D) and dealer-to-client (D2C) markets, they know the result of the requests for quotes (RFQ) they answered, and they have access to composite prices (e.g., Bloomberg CBBT). This paper presents a Bayesian method for estimating the mid-price of corporate bonds by using the real-time information available to a dealer. This method relies on recent ideas coming from the particle filtering/sequential Monte Carlo literature.},

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pubstate = {published},

tppubtype = {article}

}

@article{bismuth_1618,

title = {Portfolio choice, portfolio liquidation, and portfolio transition under drift uncertainty},

author = {Alexis Bismuth and Olivier Guéant and Jiang Pu},

url = {https://link.springer.com/article/10.1007/s11579-019-00241-1},

year  = {2019},

date = {2019-04-01},

journal = {Mathematics And Financial Economics},

volume = {13},

number = {4},

pages = {661-719},

abstract = {This paper presents several models addressing optimal portfolio choice, optimal portfolio liquidation, and optimal portfolio transition issues, in which the expected returns of risky assets are unknown. Our approach is based on a coupling between Bayesian learning and dynamic programming techniques that leads to partial differential equations. It enables to recover the well-known results of Karatzas and Zhao in a framework à la Merton, but also to deal with cases where martingale methods are no longer available. In particular, we address optimal portfolio choice, portfolio liquidation, and portfolio transition problems in a framework à la Almgren-Chriss, and we build therefore a model in which the agent takes into account in his decision process both the liquidity of assets and the uncertainty with respect to their expected return.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{gueant_1395,

title = {Option pricing and hedging with execution costs and market impact},

author = {Olivier Guéant and Jiang Pu},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/mafi.12102},

year  = {2017},

date = {2017-07-01},

journal = {Mathematical Finance},

volume = {27},

number = {3},

pages = {803-831},

abstract = {This paper considers the pricing and hedging of a call option when liquidity matters, that is, either for a large nominal or for an illiquid underlying asset. In practice, as opposed to the classical assumptions of a price?taking agent in a frictionless market, traders cannot be perfectly hedged because of execution costs and market impact. They indeed face a trade?off between hedging errors and costs that can be solved by using stochastic optimal control. Our modeling framework, which is inspired by the recent literature on optimal execution, makes it possible to account for both execution costs and the lasting market impact of trades. Prices are obtained through the indifference pricing approach. Numerical examples are provided, along with comparisons to standard methods.},

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pubstate = {published},

tppubtype = {article}

}

@article{gueant_1394,

title = {The behavior of dealers and clients on the European corporate bond market: the case of Multi-Dealer-to-Client platforms},

author = {Olivier Guéant and Jean-David Fermanian and Jiang Pu},

url = {https://www.worldscientific.com/doi/abs/10.1142/S2382626617500046},

year  = {2016},

date = {2016-12-01},

journal = {Market Microstructure and Liquidity},

volume = {2},

number = {03n04},

pages = {1750004},

abstract = {For the last two decades, most financial markets have undergone an evolution toward electronification. The market for corporate bonds is one of the last major financial markets to follow this unavoidable path. Traditionally quote-driven (i.e., dealer-driven) rather than order-driven, the market for corporate bonds is still mainly dominated by voice trading, but a lot of electronic platforms have emerged. These electronic platforms make it possible for buy-side agents to simultaneously request several dealers for quotes, or even directly trade with other buy-siders. The research presented in this paper is based on a large proprietary database of requests for quotes (RFQ) sent, through the multi-dealer-to-client (MD2C) platform operated by Bloomberg Fixed Income Trading, to one of the major liquidity providers in European corporate bonds. Our goal is (i) to model the RFQ process on these platforms and the resulting ...},

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pubstate = {published},

tppubtype = {article}

}

@article{gueant_1393,

title = {A convex duality method for optimal liquidation with participation constraints},

author = {Olivier Guéant and Jean-Michel Lasry and Jiang Pu},

url = {https://www.worldscientific.com/doi/abs/10.1142/S2382626615500021},

year  = {2015},

date = {2015-06-01},

journal = {Market Microstructure and Liquidity},

volume = {1},

number = {01},

pages = {1550002},

abstract = {In spite of the growing consideration for optimal execution in the financial mathematics literature, numerical approximations of optimal trading curves are almost never discussed. In this paper, we present a numerical method to approximate the optimal strategy of a trader willing to unwind a large portfolio. The method we propose is very general as it can be applied to multi-asset portfolios with any form of execution costs, including a bid-ask spread component, even when participation constraints are imposed. Our method, based on convex duality, only requires Hamiltonian functions to have C1,1 regularity while classical methods require additional regularity and cannot be applied to all cases found in practice.},

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pubstate = {published},

tppubtype = {article}

}

@article{gueant_1392,

title = {Accelerated Share Repurchase: pricing and execution strategy},

author = {Olivier Guéant and Royer Guillaume and Jiang Pu},

url = {https://www.worldscientific.com/doi/abs/10.1142/S0219024915500193},

year  = {2015},

date = {2015-05-01},

journal = {International Journal of Theoretical and Applied Finance},

volume = {18},

number = {03},

pages = {1550019},

abstract = {In this paper, we consider the optimal execution problem associated to accelerated share repurchase (ASR) contracts. When firms want to repurchase their own shares, they often enter such a contract with a bank. The bank buys the shares for the firm and is paid the average market price over the execution period, the length of the period being decided upon by the bank during the buying process. Mathematically, the problem is new and related to both option pricing (Asian and Bermudan options) and optimal execution. We provide a model, along with associated numerical methods, to determine the optimal stopping time and the optimal buying strategy of the bank.},

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pubstate = {published},

tppubtype = {article}

}