Applied Stochastic Models in Business and Industry

Flores, Bruno; Rios Insua, David; Alfaro, Cesar; Gomez, Javier

2022 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2700

2022 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2628

He, Ting; Coolen, Frank P. A.; Coolen‐Maturi, Tahani

2022 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2668

This article considers a novel exotic option pricing method for incomplete markets. Nonparametric predictive inference (NPI) is applied to the option pricing procedure based on the binomial tree model allowing the method to evaluate exotic options with limited information and few assumptions. As the implementation of the NPI method is greatly simplified by the monotonicity of the option payoff in the tree, we categorize exotic options by their payoff monotonicity and study a typical type of exotic option in each category, the barrier option and the look‐back option. By comparison with the classic binomial tree model, we investigate the performance of our method either with different moneyness or varying maturity. All outcomes show that our model offers a feasible approach to price the exotic options with limited information, which makes it can be utilized for both complete and incomplete markets.

Liu, Qiang; Liu, Zhi; Zhang, Chuanhai

2022 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2669

In this paper, we are interested in testing whether the volatility process is constant or not during a given time span by using high‐frequency data with the presence of jumps and market microstructure noise. Based on estimators of integrated volatility and spot volatility, we propose a nonparametric procedure to depict the discrepancy between local variation and global variation. We show that our proposed test statistic converges to a standard normal distribution if the volatility is constant, and diverges to infinity otherwise. Simulation studies verify the theoretical results and show a good finite sample performance of the test procedure. We also apply our test procedure to some real high‐frequency financial datasets. We observe that in almost half of the days tested, the assumption of constant volatility within a day is violated. And this is due to that the stock prices in the periods near the opening and closing are highly volatile and account for a relatively large proportion of intraday variation.

Roch, Oriol

2022 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2670

An aging population and the economic crisis have placed pay‐as‐you‐go pension systems in need of mechanisms to ensure their financial stability. In this article, we consider optimal indexing of pensions as an instrument to cope with the financial imbalances typically found in these systems. Using dynamic programming techniques in a stochastic continuous‐time framework, we compute the optimal pension index and portfolio strategy that best target indexing and liquidity objectives determined by the government. A numerical example is provided to illustrate the results.

Souissi, Bilel; Ghorbel, Ahmed

2022 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2671

Data on online advertising is rising rapidly due to the fast development of science and technology. Click‐through rate (CTR) prediction has become a critical task regarding the digital advertising industry and a key element in increasing advertising profits and user experience. Therefore, this article describes the problem of CTR prediction as a function of sequence classification tasks. Then, we proposed a novel optimization strategy to solve the high‐dimensional problem and find a subset of relevant variables to ensure high performance of our model and maximize the number of clicks. Here, we introduced a feature selection and hyper‐parameter optimization approach using genetic algorithms (GA) and the upper confidence bound (UCB) model to optimize micro‐targeting technology, along with the long short‐term memory (LSTM) network‐based CTR prediction model. The efficiency of the proposed UCB‐LSTM‐GA model and two hybrid models, namely LSTM‐GA and LSTM‐PSO, is evaluated by comparing them to each other and to other machine‐learning‐based classification methods, including LSTM using a UCB algorithm (UCB‐LSTM), High‐order Attentive Factorization Machine (HoAFM), genetic algorithm‐artificial neural network (GA‐ANN), and a feature interaction graph neural network model (Fi‐GNN). Our solution achieved as high as 87%, 89%, and 92% for respectively accuracy, precision, and recall, using the popular python tools with real Avazu datasets.

Toscano, Giacomo

2022 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2672

We study the sensitivity of the leverage effect to changes of the volatility and the price, showing the existence of an analytical link between the latter and the price‐leverage covariation in settings with, respectively, stochastic and level‐dependent volatility. From the financial standpoint, the results we obtain allow for the interpretation of the price‐leverage covariation as a gauge of the responsiveness of the leverage effect to price and volatility changes. The empirical study of S&P500 high‐frequency prices over the period March 2018–April 2018, carried out by means of nonparametric Fourier estimators, supports this interpretation of the role of the price‐leverage covariation.

Sun, Ning; Yang, Chen; Zitikis, Ričardas

2022 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2674

We develop an anomaly detection method when systematic anomalies, possibly statistically very similar to genuine inputs, are affecting control systems at the input and/or output stages. The method allows anomaly free inputs (i.e., those before contamination) to originate from a wide class of random sequences, thus opening up possibilities for diverse applications. To illustrate how the method works on data, and how to interpret its results and make decisions, we analyze several actual time series, which are originally nonstationary but in the process of analysis are converted into stationary. As a further illustration, we provide a controlled experiment with anomaly free inputs following an ARMA time series model under various contamination scenarios.

Flores, Bruno; Rios Insua, David; Alfaro, Cesar; Gomez, Javier

2022 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2675

We present a general framework for aviation safety occurrence forecasting. This is a major component of a methodology for aviation safety risk management at national level. It covers novel models as well as novel combinations of earlier models. Having good quality occurrence and severity forecasting models is paramount to properly manage risks, maintain the confidence of its users and preserve the status of aviation as a safe transportation mode. The problem is involved due to the presence of complex effects like seasonality, trends, or stress that impact the rates of various occurrences and the uncertainty about future number of operations.