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We introduce a novel approach to options trading strategies using a highly scalable and data-driven machine learning algorithm. In contrast to traditional approaches that often require specifications of underlying market dynamics or assumptions on an ...

This paper investigates the optimal investment-reinsurance problems for an ambiguity averse insurer with access to the options market, who worries about ambiguity and aims to find robust strategies to minimize the probability of ruin. The insurer ...

To achieve the ambitious goals of artificial intelligence, reinforcement learning must include planning with a model of the world that is abstract in state and time. Deep learning has made progress with state abstraction, but temporal abstraction ...

Equity market returns alternate between periods of calm and crises. Researchers commonly employ regime switching models to capture this behaviour. We show that forward-looking information extracted from option prices improves regime ...

We consider the problem of path planning in an initially unknown environment where a robot does not have an a priori map of its environment but has access to prior information accumulated by itself from navigation in similar but not identical ...

Tasks that require many sequential decisions or complex solutions are hard to solve using conventional reinforcement learning algorithms. Based on the semi Markov decision process setting (SMDP) and the option framework, we propose a model which aims to ...

In this paper we investigate the consequences of the choice of the model to partial hedging in incomplete markets in finance. In fact we consider two models for the stock price process. The first model is a geometric Lévy process in which the small ...

Zhang-gradient (ZG) method is a combination of Zhang dynamics (ZD) and gradient dynamics (GD) methods which are two powerful methods for online time-varying problems solving. ZG controllers are designed using the ZG method to solve the tracking control ...

While investing in foreign assets may bring additional benefits in terms of risk diversification, it may also expose the portfolio to a further source of risk derived from changes in the value of the foreign currencies. Hedging strategies for ...

Internet storage services allow businesses to move away from maintaining their own internal storage networks. Service providers currently follow a utility pricing model which translates to them absorbing all the risk that arises from the fluctuating ...

The option pricing model with constant volatility in illiquid markets has been expanded by introducing two uncertain volatility models in this paper volatility. To conquer some insufficient existed in some literature, for example the time step should be ...

This paper proposes a novel approach to discover options in the form of stochastic conditionally terminating sequences; it shows how such sequences can be integrated into the reinforcement learning framework to improve the learning performance. The ...

The sequential auction problem is commonplace in open, electronic marketplaces such as eBay. This is the problem where a buyer has no dominant strategy in bidding across multiple auctions when the buyer would have a simple, truth-revealing strategy if ...

This field study research evaluates the viability of applying an option-based risk management (OBRiM) framework, and its accompanying theoretical perspective and methodology, to real-world sequential information technology (IT) investment problems. ...

This paper considers a class of multi-period flexible supply policies with options and capacity constraints. The main results are to characterize the optimal ordering and purchasing options policy and the minimum expected cost in a period and thereafter ...

MAXQ is a new framework for multi-agent reinforcement learning. But the MAXQ framework cannot decompose all subtasks into more refined hierarchies and the hierarchies are difficult to be discovered automatically. In this paper, a multi-agent hierarchical ...

We present the design and development of a flow-based software system that became necessary for the study of a class of options-based price-estimators in the financial markets. Because a combination of factors-cost, reliability, uniformity and ...

We consider a graph theoretic approach for automatic construction of options in a dynamic environment. A map of the environment is generated on-line by the learning agent, representing the topological structure of the state transitions. A clustering ...

This paper extends the Black-Scholes methodology to payoffs that are functions of a stochastically varying variable that can be observed but not traded. The stochastic price process proposed in this paper satisfies a partial differential equation that ...

Many of the options available to decision makers, such as college majors and romantic partners, can become unavailable if sufficient effort is not invested in them (taking classes, sending flowers). The question asked in this work is whether a threat of ...