A Bayesian Nonparametric Investigation of the Predictive Effect of Exchange Rates on Commodity Prices

This study proposes a full Bayesian nonparametric procedure to investigate the predictive power of exchange rates in relation to commodity prices for three commodity-exporting countries: Canada, Australia, and New Zealand. We propose a new time-dependent infinite mixture of a normal linear regression model of the conditional distribution of the commodity price index. The mixing weights follow a set of Probit stick-breaking priors that are time-varying. We find that exchange rates have a positive predictive effect in general, but accounting for time variation does not improve forecasting performance. By contrast, the intercept in the regression and the lagged dependent variable show signs of parameter change over time in most cases, which is important in forecasting both the mean and the density of commodity prices one period ahead. The results also suggest that the variance is a large source of the time variation in the conditional distribution of commodity prices.     

Endogeneity in multiple output production: Evidence from the US hotel industry

The treatment of endogenous inputs and outputs in the estimation of multiple–output distance functions is an important issue that has been largely ignored in the hotel and tourism literature. It is well known that directional distance functions with endogeneity are difficult to estimate using maximum likelihood-based methods since the specification of additional identifiable equations for each endogenous variable is highly challenging. In this paper, we propose a limited-information approach to models of this type using a flexible reduced form for the endogenous variables. This approach allows us to easily estimate technical efficiency and does not rely on information about input or output prices, which are typically unavailable. We employ Bayesian methods and propose and use novel posterior measures of weakness and relevance of instruments in an application involving data from major US hotels. We show that controlling for endogeneity has a substantial impact for relative and absolute hotel performance.     

Post-audits for managing cyber security investments: Bayesian post-audit using Markov Chain Monte Carlo (MCMC) simulation

With increasing security spending in organizations, evaluation of the quality and effectiveness of IT security investments has become an important component in managing these projects. The academic literature, however, is largely silent on post-audit of such investments, which is a formal evaluation of IT resource allocation decisions. IT post-audits are considered a useful risk management tool for organizations and are often emphasized in security certifications and standards. To fill this research gap and contribute to practice, we suggest post-auditing of IT security investments using the generic Markov Chain Monte Carlo (MCMC) simulation approach. This approach does not place stringent conjugate assumptions and can handle high-dimensional Bayesian post-audit inference problems often associated with information security resource allocation decisions. We develop two Bayesian post-audit models using the MCMC method: (1) measuring the effectiveness of an IT security investment using posterior mean score ratios (MSR), and posterior crossover error rates (CER); and (2) measuring the effectiveness through detection of a denial of service (DOS) attack using Bayesian estimation to statistically compare the degree of divergence using the concept of entropy. We demonstrate the utility of the proposed methodology using an email intrusion detection system application.     

Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model

The rough Bergomi (rBergomi) model, introduced recently in Bayer et al. [Pricing under rough volatility. Quant. Finance, 2016, 16(6), 887–904], is a promising rough volatility model in quantitative finance. It is a parsimonious model depending on only three parameters, and yet remarkably fits empirical implied volatility surfaces. In the absence of analytical European option pricing methods for the model, and due to the non-Markovian nature of the fractional driver, the prevalent option is to use the Monte Carlo (MC) simulation for pricing. Despite recent advances in the MC method in this context, pricing under the rBergomi model is still a time-consuming task. To overcome this issue, we have designed a novel, hierarchical approach, based on: (i) adaptive sparse grids quadrature (ASGQ), and (ii) quasi-Monte Carlo (QMC). Both techniques are coupled with a Brownian bridge construction and a Richardson extrapolation on the weak error. By uncovering the available regularity, our hierarchical methods demonstrate substantial computational gains with respect to the standard MC method. They reach a sufficiently small relative error tolerance in the price estimates across different parameter constellations, even for very small values of the Hurst parameter. Our work opens a new research direction in this field, i.e. to investigate the performance of methods other than Monte Carlo for pricing and calibrating under the rBergomi model.     

Big data tools for Islamic financial analysis

Behavioural science states that emotions, principles and the manner of thinking can affect the behaviour of individuals and even investors in their decision making on financial markets. In this paper, we have tried to measure the investor sentiment by three means of big data. The first is based on a search query of a list of words related to Islamic context. The second is inferred from the engagement degree on social media. The last measure of sentiment is built, based on the Twitter API classified into positive and negative directions by a machine learning algorithm based on the naive Bayes method. Then, we investigate whether these sensations and emotions have an impact on the market sentiment and the price fluctuations by means of a vector autoregression model and Granger causality analysis. In the final step, we apply the agent‐based simulation by means of the sequential Monte Carlo method with the control of our Twitter measure on Islamic index returns. We show, then, that the three social media sentiment measures present a remarkable impact on the contemporaneous and lagged returns of the different Islamic assets studied. We also give an estimation of the parameters of the latent variables relative to the agent model studied.     

Application of Monte Carlo AHP in ranking coastal tourism environmental carrying capacity factors

This study attempts to develop a novel method to measure the importance of the characteristics closely related to the carrying capacity measurement of coastal parks by using the hierarchical method with the Monte Carlo simulation approach. One big advantage of MCAHP approach is able to effectively avoid the subjectivity of survey responses in the data collection process, leading to more reliable assessment results. Empirical results indicate the newly developed method provides a better framework for identifying the significance of the concerned factors and also produces a significant cost-saving and revenue-increasing outcome for coastal park resource uses and yet enhance tourists’ satisfaction.     

物理学对金融学的影响

金融界以几何布朗运动来描述股价、油价、汇率等金融上重要变量的时间序列,并因此与许多物理学上的模型与定律存在显著的联系.理工学科人才能在金融领域拥有明显的竞争优势.     

Quantiles of the Euler Scheme for Diffusion Processes and Financial Applications

In this paper we briefly present the results obtained in our paper ( Talay and Zheng 2002a ) on the convergence rate of the approximation of quantiles of the law of one component of  ( X_t )  , where  ( X_t )  is a diffusion process, when one uses a Monte Carlo method combined with the Euler discretization scheme. We consider the case where  ( X_t )  is uniformly hypoelliptic (in the sense of Condition (UH) below), or the inverse of the Malliavin covariance of the component under consideration satisfies the condition (M) below. We then show that Condition (M) seems widely satisfied in applied contexts. We particularly study financial applications: the computation of quantiles of models with stochastic volatility, the computation of the VaR of a portfolio, and the computation of a model risk measurement for the profit and loss of a misspecified hedging strategy.     

ANTITHETIC VARIATES FOR MONTE CARLO ESTIMATION OF PROBABILITIES

This paper explores some possibilities for variance reduction by the use of antithetic variates when estimating probabilities.     

Smoothing the payoff for efficient computation of Basket option prices

We consider the problem of pricing basket options in a multivariate Black–Scholes or Variance-Gamma model. From a numerical point of view, pricing such options corresponds to moderate and high-dimensional numerical integration problems with non-smooth integrands. Due to this lack of regularity, higher order numerical integration techniques may not be directly available, requiring the use of methods like Monte Carlo specifically designed to work for non-regular problems. We propose to use the inherent smoothing property of the density of the underlying in the above models to mollify the payoff function by means of an exact conditional expectation. The resulting conditional expectation is unbiased and yields a smooth integrand, which is amenable to the efficient use of adaptive sparse-grid cubature. Numerical examples indicate that the high-order method may perform orders of magnitude faster than Monte Carlo or Quasi Monte Carlo methods in dimensions up to 35.