A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
This study predicts flight delays for American Airlines using data mining and machine learning.
This study analyzes and predicts airline delays using machine learning models.
This paper proposes a new method to learn combinatorial patterns for airline crew pairing optimization.
Machine learning predicts flight connections for airline crew scheduling.
Adaptive framework improves airline pricing models' performance.
A federated learning framework improves RUL prognosis for aircraft engines without sharing data.
We consider the problem of efficiently constructing cheap and novel round trip flight itineraries by combining legs from different airlines. We analyse the factors that contribute towards the price of such itineraries and find that many result from the combination of just 30% of airlines and that the closer the departu…
Airlines optimize fuel loading with better flight time predictions.
Subbagging estimation for big data reduces memory usage while maintaining statistical consistency.
Travel providers such as airlines and on-line travel agents are becoming more and more interested in understanding how passengers choose among alternative itineraries when searching for flights. This knowledge helps them better display and adapt their offer, taking into account market conditions and customer needs. Som…
American options can be equivalent to European options under certain conditions.
Study on pricing American Exchange options using Lévy processes.
The paper values perpetual callable American volatility options using a mean-reverting volatility model.
American options are the reference instruments for the model calibration of a large and important class of single stocks. For this task, a fast and accurate pricing algorithm is indispensable. The literature mainly discusses pricing methods for American options that are based on Monte Carlo, tree and partial differenti…
Study solves perpetual American option pricing using variational inequality and difference equation.
Ancillaries have become a major source of revenue and profitability in the travel industry. Yet, conventional pricing strategies are based on business rules that are poorly optimized and do not respond to changing market conditions. This paper describes the dynamic pricing model developed by Deepair solutions, an AI te…
Establish C^{1,2} regularity of American value functions in Heston model
Passenger Name Records (PNRs) are at the heart of the travel industry. Created when an itinerary is booked, they contain travel and passenger information. It is usual for airlines and other actors in the industry to inter-exchange and access each other's PNR, creating the challenge of using them without infringing data…
Researchers derive a new equation for valuing American options.
MNN improves American call option pricing accuracy.
The Volterra Heston model is used to price American options.
Since most of the traded options on individual stocks is of American type it is of interest to generalize the results obtained in semi-static trading to the case when one is allowed to statically trade American options. However, this problem has proved to be elusive so far because of the asymmetric nature of the positi…
Proposes ML methods for robust price-sensitivity estimation in dynamic pricing.
In this paper, we present a new method for calculating the limit of early exercise boundary at expiry. We price American style of general derivative using a formula expressed as a sum of the value of European style of derivative and so called American premium. We use the latter expression to calculate an analytic formu…
New framework identifies hidden risks and optionality in American options.
Paper develops semi-analytic method for American options in time-dependent jump-diffusion models.
In this paper, we price American-style Parisian down-and-in call options under the Black-Scholes framework. Usually, pricing an American-style option is much more difficult than pricing its European-style counterpart because of the appearance of the optimal exercise boundary in the former. Fortunately, the optimal exer…
This paper investigates analytic properties of American option prices under the finite moment log-stable (FMLS) model. Under this model the price of American options is characterised by the free boundary problem of a fractional partial differential equation (FPDE) system. Using the technique of approximation we prove t…
We consider the problem of finding a model-free upper bound on the price of an American put given the prices of a family of European puts on the same underlying asset. Specifically we assume that the American put must be exercised at either or and that we know the prices of all vanilla European puts with th…
The paper uses LSMC to price capped American options with time-dependent caps.
Paper proposes an alternative method to price American options using HJM approach.
Paper applies subdiffusive dynamics to American and barrier options pricing.
Study bounds for prices of European and American options with optional termination.
Research improves pricing of multidimensional American options using neural networks.
In cargo logistics, a key performance measure is transport risk, defined as the deviation of the actual arrival time from the planned arrival time. Neither earliness nor tardiness is desirable for customer and freight forwarders. In this paper, we investigate ways to assess and forecast transport risks using a half-yea…
We create a robust hedging method for American options.
Paper examines floating exercise boundaries for American options in time-inhomogeneous models.
We price and hedge American options robustly in continuous time.
This paper uses deep learning to price American options under stochastic volatility.
The virtue of an American option is that it can be exercised at any time. This right is particularly valuable when there is model uncertainty. Yet almost all the extensive literature on American options assumes away model uncertainty. This paper quantifies the potential value of this flexibility by identifying the supr…
The purpose of this note is to reconcile two different results concerning the model-free upper bound on the price of an American option, given a set of European option prices. Neuberger (2007, `Bounds on the American option') and Hobson and Neuberger (2016, `On the value of being American') argue that the cost of the c…
We consider the pricing of American put options in a model-independent setting: that is, we do not assume that asset prices behave according to a given model, but aim to draw conclusions that hold in any model. We incorporate market information by supposing that the prices of European options are known. In this setting…
Study values American passport options in an exponential Lévy model.
Our goal here is to discuss the pricing problem of European and American options in discrete time using elementary calculus so as to be an easy reference for first year undergraduate students. Using the binomial model we compute the fair price of European and American options. We explain the notion of Arbitrage and the…
New methods price American options in rough volatility models.
Binomial tree methods (BTM) and explicit difference schemes (EDS) for the variational inequality model of American options with time dependent coefficients are studied. When volatility is time dependent, it is not reasonable to assume that the dynamics of the underlying asset's price forms a binomial tree if a partitio…
We show that shortfall risks of American options in a sequence of multinomial approximations of the multidimensional Black--Scholes (BS) market converge to the corresponding quantities for similar American options in the multidimensional BS market with path dependent payoffs. In comparison to previous papers we conside…
This paper develops methods for pricing American Parisian options under general Markov models.