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报告题目: Downside Risk Optimization with Random Targets and Portfolio Amplitude报告人：英国麦克斯韦数学科学研究所和赫瑞瓦特大学统计精算系副教授报告时间：2021年11月29日下午3:30-4:30报告地点：苏州大学金融工程研究中心105学术报告厅Abstract:In this paper, we rationalize using random targets in downside risk optimization that is applicable for both financial and actuarial context. We derive analytical solutions to the downside risk optimization with respect to random targets and investigate how the random target affects the optimum. In doing so, we propose using port
报告题目：A Conrmation of a Conjecture about the FeldmansTwo-armed Bandit Problem报告人：陈增敬教授 山东大学报告时间：2021.11.28(周日) 10:00-11:30报告地点：腾讯会议号：117213998报告摘要：For the Bernoulli bandit problem, Nouiehed and Ross posed a conjecture thatthe strategy ofalways playing the arm with a higher probability of being the bestarm, stochastically maximizes the number of wins. In this paper, we consider thetwo-armed bandit problem with more general distributions and a utility function.We confirm this conjecture by proving
报告题目：Centralized systemic risk control in the interbank system: weak formulation and Gamma-convergence主讲人：中国科学技术大学薄立军教授时间：2021年11月26日（星期五）上午9:30--10:30地点：https://meeting.tencent.com/dm/U6jr10GXP59j 主办单位：金融工程研究中心报告摘要: This talk discusses a systemic risk control problem by the central bank, which dynamically plans monetary supply for the interbank system with borrowing and lending activities. Facing both heterogeneity among banks and the common noise, the central bank aims to find an optimal strat
报告题目：S-shaped narrow framing, skewness and the demand for insurance报告人：池义春研究员中央财经大学报告时间：2021.11.25(周四) 09:30-10:30报告地点：腾讯会议号：134 626 863报告摘要：The existing literature in insurance economics has shown that narrow framing can explain why people buy too little insurance compared to what standard theory predicts. However, there is also ample evidence suggesting people sometimes buy too much insurance. In this talk, we assume S-shaped narrow framing, i.e., the local utility function for evaluating the
报 告 人：罗鹏，上海交通大学数学科学学院副教授报告时间：2021年11月17日下午3点—4点报告地址：腾讯会议号128446520摘 要： The present paper is devoted to the study of the well-posedness of a type of BSDEs with triangularly quadratic generators. This work is motivated by the recent results obtained by Hu and Tang [14] and Xing and Zitkovic [28]. By the contraction mapping argument, we first prove that this type of triangularly quadratic BSDEs admits a unique local solution on a small time interval whenever the terminal value is bounded. Under add
报 告 人：马敬堂 西南财经大学报告时间：2021.11.20(周六) 10:00-11:00报告地点：腾讯会议号：608 864 626报告摘要：The paper investigates the optimal reinsurance-investment strategies with assumption that the insurer can purchase proportional reinsurance contracts and invest its wealth in the financial market consisting of one risk-free asset and one risky asset whose price process obeys the rough Heston model, and then formulates a utility maximization problem with minimum guarantee under S-shaped utility. This paper uses concavificat
报告人：梁进 教授，同济大学时 间：2021.11.17(周三) 9:30-10:30地 点：金融工程研究中心105学术报告厅摘 要：Carbon reduction is a hot topic of recent times, and this presentation will introduce several mathematical models we established and their research developments on carbon reduction, including optimized emission reductions with carbon market factors, optimal carbon reduction investments, and so on.
主讲人：多伦多大学 陈宁远教授时间：2021年10月13日（星期三）上午9:30--10:30地点： https://meeting.tencent.com/dm/3ufYEhIGGfzt密码：2021主办单位： 金融工程研究中心报告摘要：We study the problem when a firm sets prices for products based on the transaction data, i.e., which product past customers chose from an assortment and what were the historical prices that they observed. Our approach does not impose a model on the distribution of the customers valuations and only assumes, instead, that purchase choices satisfy incentive-compatible constraints.
主 讲 人：香港中文大学 何雪冬教授时 间：2021年9月30日（星期四）上午9:30--10:30地 点：https://meeting.tencent.com/dm/ajwp3ZcKxVtW主办单位：金融工程研究中心报告摘要：Although maximizing median and quantiles is intuitively appealing and has an axiomatic foundation, it is difficult to study the optimal portfolio strategy due to the discontinuity and time inconsistency in the objective function. We use the intra-personal equilibrium approach to study the problem. Interestingly, we find that the only viable outcome is from the median maximization, b
Time: 9 Sep (Thursday), 15:30-17:00Speaker:Min Dai, National University of SingaporeJoin Zoom Meeting：https://nus-sg.zoom.us/j/84419249997?pwd=UVNjTXpYcmwycHdIVGpnNWtkWmtUZz09Meeting ID: 844 1924 9997Passcode: 108687Abstract: We propose a tractable model of dynamic investment, division sales (spinoffs), financing, and risk management for a multi-division firm facing costly external finance. Our main results are: (1) within-firm resource allocation is based not only on the divisions’ productivity
Time: 7 Sep (Tuesday), 15:30-17:00Speaker:Min Dai, National University of SingaporeJoin Zoom Meeting：https://nus-sg.zoom.us/j/88454814397?pwd=aHFhOWhSbEVJY3plaUxkcnJRaW85dz09Meeting ID: 884 5481 4397Passcode: 009765Abstract: We develop a dynamic tractable model where an investor derives realization utility as in Barberis and Xiong (2012) and Ingersoll and Jin (2013), but importantly can dynamically rebalance her portfolio between a risky asset and a risk-free asset. We show that the option of in
报 告人：Min DAI, National University of Singapore报告时间：2021.08.04，14：00-17：00报告地址：Zoom会议：850 450 9172报告摘要:We develop a dynamic equilibrium Bitcoin mining model to characterize miners’ optimal entry and exit strategies with technology innovation. We formulate the model as a singular stochastic control problem from a social planner’s angleand show that the resulting optimal strategy must be an equilibrium strategy. We prove that the value function associated with the singular stochastic control proble
报告人：鄂维南中国科学院院士，美国数学学会、美国工业与应用数学学会、英国物理学会Fellow。北京大数据研究院院长。报告时间：2021.08.03，16：30-17：30报告地址：腾讯会议，会议号：423 2979 4552报告简介：现代机器学习的核心问题是怎样有效地逼近一个高维空间的函数。传统的逼近论方法会导致维数灾难，这是对许多领域来说困惑了我们多年的问题。在这个演讲里，我们将介绍以下几方面的内容。1. 怎样建立起一个数学理论？这里的问题本身跟传统的数值分析基本一样。不同的是机器学习需要处理的核心问题是维数灾难。所以我们需要建立起一个高维数值分析理论，包括逼近论，先验和后验误差估计，优化理论等。这个理论会帮助我们理解什么样的模型和算法没有维数灾难。2. 怎样formulate 一个好的机器学习的数学模型？正确的方法是首先在连续的层面formulate 好的机器学习的模型，然后采用数值分析的想法，对这些连续模型作离散化而得到所需要的机器学习算法。我们发现许多神经网络模型，包括残差网络模型，都可以通过这种途径得到。因为有一个好的连续模型作为背景，这样得到的机器学习模型和算法自然就有比较
报告人：王军波教授，香港城市大学经济与金融系时间：2021.06.29(周二) 9:30-10:30地点：腾讯会议ID：146 658 183摘要： Using CDS-implied risk premium measures, we find that these variables have higher predictive power in the cross-section for bond returns than traditional default risk measures. The positive effect of the credit risk premium (CRP) factor on expected returns is pervasive, stronger for lower-rated bonds and robust to controlling for conventional risk factors and bond characteristics. Besides the systematic CRP factor, idiosy
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