Option Pricing under the Double Exponential Jump-Diffusion Model with Stochastic Volatility and Interest Rate

This paper proposes an efficient option pricing model that incorporates stochastic interest rate (SIR), stochastic volatility (SV), and double exponential jump into the jump-diffusion settings. The model comprehensively considers the leptokurtosis and heteroscedasticity of the underlying asset’s returns, rare events, and an SIR. Using the model, we deduce the pricing characteristic function and pricing formula of a European option. Then, we develop the Markov chain Monte Carlo method with latent variable to solve the problem of parameter estimation under the double exponential jump-diffusion model with SIR and SV. For verification purposes, we conduct time efficiency analysis, goodness of fit analysis, and jump/drift term analysis of the proposed model. In addition, we compare the pricing accuracy of the proposed model with those of the Black–Scholes and the Kou (2002) models. The empirical results show that the proposed option pricing model has high time efficiency, and the goodness of fit and pricing accuracy are significantly higher than those of the other two models.     

STOCHASTIC LOCAL INTENSITY LOSS MODELS WITH INTERACTING PARTICLE SYSTEMS

It is well known from the work of Schönbucher that the marginal laws of a loss process can be matched by a unit increasing time inhomogeneous Markov process, whose deterministic jump intensity is called local intensity. The stochastic local intensity (SLI) models such as the one proposed by Arnsdorf and Halperin allow to get a stochastic jump intensity while keeping the same marginal laws. These models involve a nonlinear stochastic differential equation (SDE) with jumps. The first contribution of this paper is to prove the existence and uniqueness of such processes. This is made by means of an interacting particle system, whose convergence rate toward the nonlinear SDE is analyzed. Second, this approach provides a powerful way to compute pathwise expectations with the SLI model: we show that the computational cost is roughly the same as a crude Monte Carlo algorithm for standard SDEs.     

An interactive land use transition agent-based model (ILUTABM): Endogenizing human-environment interactions in the Western Missisquoi Watershed

Forest Transition Theory (FTT) suggests that reforestation may follow deforestation as a result of and interplay between changing social, economic and ecological conditions. We develop a simplistic but empirically data driven land use transition agent-based modeling platform, interactive land use transition agent-based model (ILUTABM), that is able to reproduce the observed land use patterns and link the forest transition to parcel-level heuristic-based land use decisions and ecosystem service (ES). The ILUTABM endogenously links landowners’ land use decisions with ecosystem services (ES) provided by the lands by treating both lands and landowners as interacting agents. The ILUTABM simulates both the land use changes resulting from farmers’ decision behaviors as well as the recursive effects of changing land uses on farmers’ decision behaviors. The ILUTABM is calibrated and validated at 30 m × 30 m spatial resolution using National Land Cover Data (NLCD) 1992, 2001 and 2006 across the western Missisquoi watershed, which is located in the north-eastern US with an estimated area of 283 square kilometers and 312 farmers farming on 16% of the total Missisquoi watershed area. This study hypothesizes that farmers’ land use decisions are made primarily based on their summed expected utilities and that impacts of exogenous socio-economic factors, such as natural disasters, public policies and institutional/social reforms, on farmers’ expected utilities can significantly influence the land use transitions between agricultural and forested lands. Monte Carlo experiments under six various socio-economic conditions combined with different ES valuation schemes are used to assess the sensitivities of the ILUTABM. Goodness-of-fit measures confirm that the ILUTABM is able to reproduce 62% of the observed land use transitions. However, the spatial patterns of the observed land used transitions are more clustered than the simulated counterparts. We find that, when farmers value food provisioning Ecosystem Services (ES) more than other ES (e.g., soil and water regulation), deforestation is observed. However, when farmers value less food provisioning than other ES or they value food provisioning and other ES equally, the forest transition is observed. The ILUTABM advances the Forest Transition Theory (FTT) framework by endogenizing the interactions of socio-ecological feedbacks and socio-economic factors in a generalizable model that can be calibrated with empirical data.     

Modeling a Dynamic Portfolio for Pension Plans in Emerging Markets With Myopic and Nonmyopic Behavior

ABSTRACT

We introduce a dynamic formulation for the problem of portfolio selection of pension funds in the absence of a risk-free asset. In emerging markets, a risk-free asset might be unavailable, and the approaches commonly used may no longer be suitable. We use a parametric approach to combine dynamic programming and Monte Carlo simulation to gain additional flexibility. This approach is general in the sense that optimal asset allocation is tractable for all HARA utility functions in the absence of a risk-free asset. The traditional case composed of several risky assets and one risk-free asset is compared to a case in which the risk-free asset is unavailable.     

A STOCHASTIC SETTING TO BANK FINANCIAL PERFORMANCE FOR REFINING EFFICIENCY ESTIMATES

This study contributes to develop a framework to measure the financial performance of banks in a stochastic setting. The framework comprises several steps, the first of which is the development of a financial performance measurement model to evaluate a bank's financial performance using a set of factors from the CAMEL (Capital adequacy, Assets, Management Capability, Earning and Liquidity) system. Second, the stochastic setting of the efficiency measurement is handled using the data collection budget allocation approach, whereby Monte Carlo simulations are used to analyse additional generated data and a genetic algorithm is used to refine the accuracy of the efficiency estimates. The results show that the accuracy of the model is greatly improved using the proposed approach. In contrast to the conventional deterministic model, the proposed framework is more useful to managers in determining the bank's future financial operations to improve the overall financial soundness of the bank. Copyright © 2014 John Wiley & Sons, Ltd.     

马尔可夫调制模型下交换期权的定价

在这篇文章中,假定市场经济状态由一个两状态马尔可夫链描述,风险资产满足一个两状态的马尔可夫调制过程。当市场处于高波动状态时,风险资产的价格满足跳扩散过程;当市场处于稳定状态时,风险资产的价格满足几何布朗运动.通过测度变换的技术,得到了交换期权的定价公式。最后,利用蒙特卡洛方法给出了期权价值的数值结果。     

基于限价指令的最优变现策略

最优变现策略是投资者指在一定时间内变现给定数量的头寸,并使其收益最大化的交易策略。以投资者最优卖价（限价单报价）高于市场实时一档买价的价差为变量,以收益最大化为目标建立随机控制模型,并采用HJB方程转换成一组常微分方程的求解,给出限价单的最优报价策略,利用蒙特卡洛模拟出限价指令策略的交易曲线。该模型同时考虑价格波动风险和未执行风险,并将买卖价差标准化后带入模型,避免了绝对价格的不同所带来的差异。     

Bayesian estimation of an extended local scale stochastic volatility model

A new version of the local scale model of Shephard (1994) is presented. Its features are identically distributed evolution equation disturbances, the incorporation of in-the-mean effects, and the incorporation of variance regressors. A Bayesian posterior simulator and a new simulation smoother are presented. The model is applied to publicly available daily exchange rate and asset return series, and is compared with t-GARCH and Lognormal stochastic volatility formulations using Bayes factors.     

Two new methods for estimating structural equation models: An illustration and a comparison with two established methods

The application of structural equation models (SEMs) is common in marketing and the behavioral sciences. Accordingly, the exploration of more effective methods to estimate SEMs is also a popular area of research. [Croon, 2002] and [Skrondal and Laake, 2001] have each proposed a new method for estimating SEMs, but since these proposals nearly a decade ago, these methods have been mostly overlooked by applied researchers. We suggest that reasons for this oversight may include not only a lack of guidance in implementing these new methods but also the absence of a formal comparison to review these new methods relative to the more familiar maximum likelihood structural equation modeling (MLSEM) and partial least squares (PLS). In this paper, our goal was to make the Croon and Skrondal-Laake (SL) methods more accessible to applied researchers. We first provide a step-by-step illustration of how to implement the Croon and SL methods. We also present the first comprehensive evaluation of the new methods relative to MLSEM and PLS. From this evaluation, we can better appreciate the circumstances under which these new methods are preferable to MLSEM and PLS. Thus, we intend to help readers understand how and when to apply these new methods.