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View article: Closed‐Form Optimal Investment Under Generalized GARCH Models
Closed‐Form Optimal Investment Under Generalized GARCH Models Open
This paper introduces a new class of stochastic volatility models for asset prices, the generalized Heston Nandi GARCH (GHN‐GARCH), with the primary objective of optimal dynamic asset allocation under expected utility theory for constant r…
View article: The Repayment Structure of Agricultural Loans under a Full Repayment Constraint
The Repayment Structure of Agricultural Loans under a Full Repayment Constraint Open
We investigate the optimal asset allocation and repayment strategy of an agricultural loan under a guaranteed repayment condition in a continuous-time setting. We propose two forms of the problem: an analytically solvable “separable” probl…
View article: The Generative Adversarial Approach: A Cautionary Tale of Finite Samples
The Generative Adversarial Approach: A Cautionary Tale of Finite Samples Open
Given the relevance and wide use of the Generative Adversarial (GA) methodology, this paper focuses on finite samples to better understand its benefits and pitfalls. We focus on its finite-sample properties from both statistical and numeri…
View article: Local Stochastic Correlation Models for Derivative Pricing
Local Stochastic Correlation Models for Derivative Pricing Open
This paper reveals a simple methodology to create local-correlation models suitable for the closed-form pricing of two-asset financial derivatives. The multivariate models are built to ensure two conditions. First, marginals follow desirab…
View article: Markov-Modulated and Shifted Wishart Processes with Applications in Derivatives Pricing
Markov-Modulated and Shifted Wishart Processes with Applications in Derivatives Pricing Open
The popular Wishart (WI) processes, first introduced by Bru in 1991, exhibit convenient analytical properties for modeling asset prices, particularly a closed-form characteristic function, and the ability to jointly model stochastic volati…
View article: Is There a Future for Stochastic Modeling in Business and Industry in the Era of Machine Learning and Artificial Intelligence?
Is There a Future for Stochastic Modeling in Business and Industry in the Era of Machine Learning and Artificial Intelligence? Open
The paper arises from the experience of Applied Stochastic Models in Business and Industry which has seen, over the years, more and more contributions related to Machine Learning rather than to what was intended as a stochastic model. The …
View article: Data-Based Parametrization for Affine GARCH Models Across Multiple Time Scales—Roughness Implications
Data-Based Parametrization for Affine GARCH Models Across Multiple Time Scales—Roughness Implications Open
This paper revisits the topic of time-scale parameterizations of the Heston–Nandi GARCH (1,1) model to create a new, theoretically valid setting compatible with real financial data. We first estimate parameters using three US market indice…
View article: Value-at-risk constrained portfolios in incomplete markets: a dynamic programming approach to Heston’s model
Value-at-risk constrained portfolios in incomplete markets: a dynamic programming approach to Heston’s model Open
We solve an expected utility-maximization problem with a Value-at-risk constraint on the terminal portfolio value in an incomplete financial market due to stochastic volatility. To derive the optimal investment strategy, we use the dynamic…
View article: Optimal Market Completion through Financial Derivatives with Applications to Volatility Risk
Optimal Market Completion through Financial Derivatives with Applications to Volatility Risk Open
This paper investigates the optimal choices of financial derivatives to complete a financial market in the framework of stochastic volatility (SV) models. We first introduce an efficient and accurate simulation-based method applicable to g…
View article: Do jumps matter in discrete-time portfolio optimization?
Do jumps matter in discrete-time portfolio optimization? Open
This paper studies a discrete-time portfolio optimization problem, wherein the underlying risky asset follows a Lévy GARCH model. Besides a Gaussian noise, the framework allows for various jump increments, including infinite-activity jumps…
View article: Bayesian Learning in an Affine GARCH Model with Application to Portfolio Optimization
Bayesian Learning in an Affine GARCH Model with Application to Portfolio Optimization Open
This paper develops a methodology to accommodate uncertainty in a GARCH model with the goal of improving portfolio decisions via Bayesian learning. Given the abundant evidence of uncertainty in estimating expected returns, we focus our ana…
View article: Robust Portfolio Optimization with Environmental, Social, and Corporate Governance Preference
Robust Portfolio Optimization with Environmental, Social, and Corporate Governance Preference Open
This study addresses the crucial but under-explored topic of ambiguity aversion, i.e., model misspecification, in the area of environmental, social, and corporate governance (ESG) within portfolio decisions. It considers a risk- and ambigu…
View article: Robust Portfolio Choice under the Modified Constant Elasticity of Variance
Robust Portfolio Choice under the Modified Constant Elasticity of Variance Open
This study investigates ambiguity aversion within the framework of a utility-maximizing investor under a modified constant-elasticity-of-volatility (M-CEV) model for the underlying asset. We derive closed-form solutions of a non-affine typ…