Optimal Decision on Dynamic Insurance Price and Investment Portfolio of An Insurer with Time-Varying Correlation of Mult-Stochastic Process

2017 ◽  
Author(s):  
Hong Mao
Keyword(s):  
Stochastic Process ◽  
Optimal Decision ◽  
Time Varying ◽  
Investment Portfolio ◽  
Insurance Price

Optimal decision on dynamic insurance price and investment portfolio of an insurer

2013 ◽  
Vol 52 (2) ◽  
pp. 359-369 ◽  
Author(s):  
Hong Mao ◽  
James M. Carson ◽  
Krzysztof M. Ostaszewski ◽  
Zhongkai Wen
Keyword(s):  
Optimal Decision ◽  
Investment Portfolio ◽  
Insurance Price

scholarly journals The Aumann–Serrano Performance Index for Multi-Period Gambles in Stock Data

2020 ◽  
Vol 13 (11) ◽  
pp. 288
Author(s):  
Jiro Hodoshima ◽  
Toshiyuki Yamawake
Keyword(s):  
Stochastic Process ◽  
Empirical Study ◽  
Performance Index ◽  
Sharpe Ratio ◽  
Time Varying ◽  
Normal Mixture ◽  
Evaluation Score ◽  
Favorable Evaluation ◽  
Selection Of

We present an empirical study of the Aumann-Serrano performance index for multi-period gambles when the underlying stochastic process is assumed to be a normal mixture process with time-varying volatility. We compare the Aumann-Serrano performance index for multi-period gambles with that for one-period gambles as well as the Sharpe ratio. Our empirical study is obtained using a selection of U.S. stock data and shows evaluation of a selection of stocks becomes more distinct in multi-period gambles than in one-period gambles in the sense that a favorable evaluation score becomes even better in multi-period gambles than in one-period gambles while an unfavorable evaluation score becomes even worse in multi-period gambles than in one-period gambles.

STATISTICAL LEARNING WITH TIME-VARYING PARAMETERS

2003 ◽  
Vol 7 (1) ◽  
pp. 119-139 ◽  
Author(s):  
Bruce McGough
Keyword(s):  
Stochastic Process ◽  
Kalman Filter ◽  
Statistical Learning ◽  
Rational Expectations ◽  
Stability Parameter ◽  
Time Varying ◽  
Rational Expectations Equilibrium ◽  
Varying Parameters ◽  
Time Varying Parameters ◽  
The Stability

In their landmark paper, Bray and Savin note that the constant-parameters model used by their agents to form expectations is misspecified and that, using standard econometric techniques, agents may be able to determine the time-varying nature of the model's parameters. Here, we consider the same type of model as employed by Bray and Savin except that our agents form expectations using a perceived model with parameters that vary with time. We assume agents use the Kalman filter to form estimates of these time-varying parameters. We find that, under certain restrictions on the structure of the stochastic process and on the value of the stability parameter, the model will converge to its rational expectations equilibrium. Further, the restrictions on the stability parameter required for convergence are identical to those found by Bray and Savin.

scholarly journals Optimizing Age Penalty in Time-Varying Networks with Markovian and Error-Prone Channel State

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 91
Author(s):  
Yuchao Chen ◽  
Haoyue Tang ◽  
Jintao Wang ◽  
Jian Song
Keyword(s):  
Optimal Policy ◽  
Infinite Horizon ◽  
Base Station ◽  
Optimal Decision ◽  
Time Varying ◽  
Sensitive Data ◽  
Multiple Sensors ◽  
Data Freshness ◽  
Markov Decision ◽  
Time Varying Networks

In this paper, we consider a scenario where the base station (BS) collects time-sensitive data from multiple sensors through time-varying and error-prone channels. We characterize the data freshness at the terminal end through a class of monotone increasing functions related to Age of information (AoI). Our goal is to design an optimal policy to minimize the average age penalty of all sensors in infinite horizon under bandwidth and power constraint. By formulating the scheduling problem into a constrained Markov decision process (CMDP), we reveal the threshold structure for the optimal policy and approximate the optimal decision by solving a truncated linear programming (LP). Finally, a bandwidth-truncated policy is proposed to satisfy both power and bandwidth constraint. Through theoretical analysis and numerical simulations, we prove the proposed policy is asymptotic optimal in the large sensor regime.

Optimization algorithms and investment portfolio analytics with machine learning techniques under time-varying liquidity constraints

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mazin A.M. Al Janabi
Keyword(s):  
Machine Learning ◽  
Liquidity Constraints ◽  
Market Risk ◽  
Machine Learning Techniques ◽  
Time Varying ◽  
Risk Modeling ◽  
Investment Portfolio ◽  
Content Type ◽  
Learning Techniques ◽  
Portfolio Managers

Purpose This paper aims to examine from commodity portfolio managers’ perspective the performance of liquidity adjusted risk modeling in assessing the market risk parameters of a large commodity portfolio and in obtaining efficient and coherent portfolios under different market circumstances. Design/methodology/approach The implemented market risk modeling algorithm and investment portfolio analytics using reinforcement machine learning techniques can simultaneously handle risk-return characteristics of commodity investments under regular and crisis market settings besides considering the particular effects of the time-varying liquidity constraints of the multiple-asset commodity portfolios. Findings In particular, the paper implements a robust machine learning method to commodity optimal portfolio selection and within a liquidity-adjusted value-at-risk (LVaR) framework. In addition, the paper explains how the adapted LVaR modeling algorithms can be used by a commodity trading unit in a dynamic asset allocation framework for estimating risk exposure, assessing risk reduction alternates and creating efficient and coherent market portfolios. Originality/value The optimization parameters subject to meaningful operational and financial constraints, investment portfolio analytics and empirical results can have important practical uses and applications for commodity portfolio managers particularly in the wake of the 2007–2009 global financial crisis. In addition, the recommended reinforcement machine learning optimization algorithms can aid in solving some real-world dilemmas under stressed and adverse market conditions (e.g. illiquidity, switching in correlations factors signs, nonlinear and non-normal distribution of assets’ returns) and can have key applications in machine learning, expert systems, smart financial functions, internet of things (IoT) and financial technology (FinTech) in big data ecosystems.

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