Optimal model averaging forecasting in high-dimensional survival analysis

This article considers ultrahigh-dimensional forecasting problems with survival response variables. We propose a two-step model averaging procedure for improving the forecasting accuracy of the true conditional mean of a survival response variable. The first step is to construct a class of candidate models, each with low-dimensional covariates. For this, a feature screening procedure is developed to separate the active and inactive predictors through a marginal Buckley–James index, and to group covariates with a similar index size together to form regression models with survival response variables. The proposed screening method can select active predictors under covariate-dependent censoring, and enjoys sure screening consistency under mild regularity conditions. The second step is to find the optimal model weights for averaging by adapting a delete-one cross-validation criterion, without the standard constraint that the weights sum to one. The theoretical results show that the delete-one cross-validation criterion achieves the lowest possible forecasting loss asymptotically. Numerical studies demonstrate the superior performance of the proposed variable screening and model averaging procedures over existing methods.     

Impact of walking speed on tourist carrying capacity: The case of Maiji Mountain Grottoes,China

This paper analyzes the impact of walking speed on tourist carrying capacity. To do so, a combination of simulation experimentation and on-the-ground experiment is suggested to work in a study case, Maiji Mountain Grottoes, China. The simulation findings indicate that walking speed has a negative impact on tourist carrying capacity and the correlation coefficient is 0.64, for the reason that walking takes up more space. The on-the-ground experiment shows that carrying capacity is not a unified limit all over a destination, but changeable temporally and spatially, and its estimation needs to be adjusted according to the minimum area required per tourist and tourist activity. The method applied in this paper and the simulation findings can serve as a methodological tool for further research about tourist carrying capacity where the main activity of tourists is walking while watching.     

Regime switching rough Heston model

This model combines two important stylized features of volatility, the rough behavior consistent with a Hurst parameter less than , and the regime switching property consistent with more long-term economic considerations. It is nevertheless highly tractable in the sense of semianalytic formulae for European options, and permits a partial Monte Carlo method of similar computational speed as the semianalytic formula (at an appropriate number of Monte Carlo simulations). While option prices are relatively insensitive to the choice of Hurst parameter, introducing rough volatility allows for a better fit to the at-the-money skew.     

Pooling data for the analysis of dynamic marketing systems

Vector autoregressive (VAR) models have become popular in marketing literature for analyzing the behavior of competitive marketing systems. One drawback of these models is that the number of parameters can become very large, potentially leading to estimation problems. Pooling data for multiple cross-sectional units (stores) can partly alleviate these problems. An important issue in such models is how heterogeneity among cross-sectional units is accounted for. We investigate the performance of several pooling approaches that accommodate different levels of cross-sectional heterogeneity in a simulation study and in an empirical application. Our results show that the random coefficients modeling approach is an overall good choice when the estimated VAR model is used for out-of-sample forecasting only. When the estimated model is used to compute Impulse Response Functions, we conclude that one should select a modeling approach that matches the level of heterogeneity in the data.     

On an efficient multiple time step Monte Carlo simulation of the SABR model

In this paper, we will present a multiple time step Monte Carlo simulation technique for pricing options under the Stochastic Alpha Beta Rho model. The proposed method is an extension of the one time step Monte Carlo method that we proposed in an accompanying paper Leitao et al. [Appl. Math. Comput. 2017, 293, 461–479], for pricing European options in the context of the model calibration. A highly efficient method results, with many very interesting and nontrivial components, like Fourier inversion for the sum of log-normals, stochastic collocation, Gumbel copula, correlation approximation, that are not yet seen in combination within a Monte Carlo simulation. The present multiple time step Monte Carlo method is especially useful for long-term options and for exotic options.     

Weather derivatives structuring and pricing: a sustainable agricultural approach in Africa

The objective of this article is to calculate the price of weather derivatives for different African countries with payout depending on temperature. A new approach for computing degree day contracts is shown and gives another scale to the numerical relevance and practical implementation of the findings. With historical data for each country, a stochastic process based on continuous time with mean reversion representing the evolution of the temperature is determined. Focusing on the Monte Carlo simulation method, the price of each contract and the potential implications to solve several aspects of the threatened African economy are presented.     

Can Warren Buffett forecast equity market corrections?

Warren Buffett suggested that the ratio of the market value of all publicly traded stocks to the Gross National Product could identify potential overvaluations and undervaluations in the US equity market when this ratio deviates above 120% or below 80%. We investigate whether this ratio is a statistically significant predictor of equity market corrections and rallies. We find that Buffett's decision rule does not deliver satisfactory forecasts. However, when we adopt a time-varying decision rule, the ratio becomes a statistically significant predictor of equity market corrections. The two time-varying decision rules are: (i) predict an equity market correction when the ratio exceeds a 95% one-tail confidence interval based on a normal distribution, and (ii) predict an equity market correction when the ratio exceeds a threshold computed using Cantelli's inequality. These new decision rules are robust to changes in the two key parameters: the confidence level and the forecasting horizon. This paper also shows that the MV/GNP ratio performs relatively well against the four most popular equity market correction models, but the ratio is not a particularly useful predictor of equity market rallies.     

Valuation and incentive effects of hurdle rate executive stock options

Traditional executive stock options are often criticized for inherently weak links between pay and performance. Hurdle rate executive stock options represent a viable improvement. However, valuing these options presents extraordinary analytic difficulties. With a constant dividend yield the strike price becomes a path-dependent function of the stock price and exact analytic valuation is intractable. To solve this problem, we apply the Monte Carlo valuation approach developed by Longstaff and Schwartz (Rev Financ Stud 4:113–147, 2001) to estimate the value of path-dependent American options. We also extend the methodology to incorporate the theoretical framework by Ingersoll (J Bus 79:453–487, 2006) to permit subjective valuation influenced by an executive’s risk aversion.

房地产开发企业违约概率压力测试研究——现金流蒙特卡洛模拟方法在银行中的应用

本文采用蒙特卡洛模拟方法,根据现金净额是否为负这一标准来判断房地产开发企业是否违约,在对企业的现金流进行随机模拟的基础上来计算企业的违约概率。压力测试的场景为房价下降,利率上升。压力传导途径为房价与利率变动导致企业销售收入变动,销售收入的改变导致企业的现金流量表发生变化。房价和利率对销售收入的冲击是随机的,企业的现金流也是随机的,本文通过随机模拟估算了企业的现金流为负的频率,以此作为企业违约的概率。压力测试表明,当房价下降幅度到达15%附近时,房地产开发商的违约概率开始急剧上升。