The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Jonathon A Chambers - One of the best experts on this subject based on the ideXlab platform.

  • game theoretic power allocation and the nash Equilibrium Analysis for a multistatic mimo radar network
    IEEE Transactions on Signal Processing, 2017
    Co-Authors: Anastasios Deligiannis, Anastasia Panoui, Sangarapillai Lambotharan, Jonathon A Chambers
    Abstract:

    We investigate a game-theoretic power allocation scheme and perform a Nash Equilibrium Analysis for a multistatic multiple-input multiple-output radar network. We consider a network of radars, organized into multiple clusters, whose primary objective is to minimize their transmission power, while satisfying a certain detection criterion. Since there is no communication between the distributed clusters, we incorporate convex optimization methods and noncooperative game-theoretic techniques based on the estimate of the signal-to-interference-plus-noise ratio (SINR) to tackle the power adaptation problem. Therefore, each cluster egotistically determines its optimal power allocation in a distributed scheme. Furthermore, we prove that the best response function of each cluster regarding this generalized Nash game belongs to the framework of standard functions. The standard function property together with the proof of the existence of the solution for the game guarantees the uniqueness of the Nash Equilibrium. The mathematical Analysis based on Karush–Kuhn–Tucker conditions reveals some interesting results in terms of the number of active radars and the number of radars that over satisfy the desired SINRs. Finally, the simulation results confirm the convergence of the algorithm to the unique solution and demonstrate the distributed nature of the system.

Sanford J Grossman - One of the best experts on this subject based on the ideXlab platform.

  • Equilibrium Analysis of portfolio insurance
    Journal of Finance, 1996
    Co-Authors: Sanford J Grossman, Zongquan Zhou
    Abstract:

    A martingale approach is used to characterize general Equilibrium in the presence of portfolio insurance. Insurers sell to noninsurers in bad states, and general Equilibrium requires that the risk premium rises to induce noninsurers to increase their holdings. We show that portfolio insurance increases price volatility, causes mean reversion in asset returns, raises the Sharpe ratio and volatility in bad states, and causes volatility to be correlated with volume. We also explain why out-of-the-money SP that is, the demand for an absolute level of price protection is equivalent to a demand for put options.1 We show that a similar argument can be used in a

  • Equilibrium Analysis of portfolio insurance
    Social Science Research Network, 1996
    Co-Authors: Sanford J Grossman, Zongquan Zhou
    Abstract:

    A martingale approach is used to characterize general Equilibrium in the presence of portfolio insurance. Insurers sell to non-insurers in bad states, and general Equilibrium requires that the risk premium rises to induce non-insurers to increase their holdings. We show that portfolio insurance increases price volatility, causes mean reversion in asset returns, raises the Sharpe ratio and volatility in bad states, and causes volatility to be correlated with volume. We also explain why out-of-the-money S&P 500 put options trade at a higher volatility than do in-the-money puts.

Anastasios Deligiannis - One of the best experts on this subject based on the ideXlab platform.

  • game theoretic power allocation and the nash Equilibrium Analysis for a multistatic mimo radar network
    IEEE Transactions on Signal Processing, 2017
    Co-Authors: Anastasios Deligiannis, Anastasia Panoui, Sangarapillai Lambotharan, Jonathon A Chambers
    Abstract:

    We investigate a game-theoretic power allocation scheme and perform a Nash Equilibrium Analysis for a multistatic multiple-input multiple-output radar network. We consider a network of radars, organized into multiple clusters, whose primary objective is to minimize their transmission power, while satisfying a certain detection criterion. Since there is no communication between the distributed clusters, we incorporate convex optimization methods and noncooperative game-theoretic techniques based on the estimate of the signal-to-interference-plus-noise ratio (SINR) to tackle the power adaptation problem. Therefore, each cluster egotistically determines its optimal power allocation in a distributed scheme. Furthermore, we prove that the best response function of each cluster regarding this generalized Nash game belongs to the framework of standard functions. The standard function property together with the proof of the existence of the solution for the game guarantees the uniqueness of the Nash Equilibrium. The mathematical Analysis based on Karush–Kuhn–Tucker conditions reveals some interesting results in terms of the number of active radars and the number of radars that over satisfy the desired SINRs. Finally, the simulation results confirm the convergence of the algorithm to the unique solution and demonstrate the distributed nature of the system.

  • Game-theoretic power allocation and the Nash Equilibrium Analysis for a multistatic MIMO radar network
    2017
    Co-Authors: Anastasios Deligiannis, Anastasia Panoui, Sangarapillai Lambotharan, Jonathon Chambers
    Abstract:

    CCBY We investigate a game-theoretic power allocation scheme and perform a Nash Equilibrium Analysis for a multistatic multiple-input multiple-output (MIMO) radar network. We consider a network of radars, organized into multiple clusters, whose primary objective is to minimize their transmission power, while satisfying a certain detection criterion. Since there is no communication between the distributed clusters, we incorporate convex optimization methods and noncooperative game-theoretic techniques based on the estimate of the signal to interference plus noise ratio (SINR) to tackle the power adaptation problem. Therefore, each cluster egotistically determines its optimal power allocation in a distributed scheme. Furthermore, we prove that the best response function of each cluster regarding this generalized Nash game (GNG) belongs to the framework of standard functions. The standard function property together with the proof of the existence of solution for the game guarantees the uniqueness of the Nash Equilibrium. The mathematical Analysis based on Karush-Kuhn-Tucker conditions reveal some interesting results in terms of number of active radars and the number of radars that over satisfy the desired SINRs. Finally, the simulation results confirm the convergence of the algorithm to the unique solution and demonstrate the distributed nature of the system

Zongquan Zhou - One of the best experts on this subject based on the ideXlab platform.

  • Equilibrium Analysis of portfolio insurance
    Journal of Finance, 1996
    Co-Authors: Sanford J Grossman, Zongquan Zhou
    Abstract:

    A martingale approach is used to characterize general Equilibrium in the presence of portfolio insurance. Insurers sell to noninsurers in bad states, and general Equilibrium requires that the risk premium rises to induce noninsurers to increase their holdings. We show that portfolio insurance increases price volatility, causes mean reversion in asset returns, raises the Sharpe ratio and volatility in bad states, and causes volatility to be correlated with volume. We also explain why out-of-the-money SP that is, the demand for an absolute level of price protection is equivalent to a demand for put options.1 We show that a similar argument can be used in a

  • Equilibrium Analysis of portfolio insurance
    Social Science Research Network, 1996
    Co-Authors: Sanford J Grossman, Zongquan Zhou
    Abstract:

    A martingale approach is used to characterize general Equilibrium in the presence of portfolio insurance. Insurers sell to non-insurers in bad states, and general Equilibrium requires that the risk premium rises to induce non-insurers to increase their holdings. We show that portfolio insurance increases price volatility, causes mean reversion in asset returns, raises the Sharpe ratio and volatility in bad states, and causes volatility to be correlated with volume. We also explain why out-of-the-money S&P 500 put options trade at a higher volatility than do in-the-money puts.

Goran Strbac - One of the best experts on this subject based on the ideXlab platform.

  • Multi-Period and Multi-Spatial Equilibrium Analysis in Imperfect Electricity Markets: A Novel Multi-Agent Deep Reinforcement Learning Approach
    IEEE Access, 2019
    Co-Authors: Yujian Ye, Jing Li, Goran Strbac
    Abstract:

    Previously works on analysing imperfect electricity markets have employed conventional game-theoretic approaches. However, such approaches necessitate that each strategic market player has full knowledge of the operating parameters and the strategies of its rivals as well as the computational algorithm of the market clearing process. This unrealistic assumption, along with the modeling and computational complexities, renders such approaches less applicable for conducting practical multi-period and multi-spatial Equilibrium Analysis. This paper proposes a novel multi-agent deep reinforcement learning (MA-DRL) based methodology, combining multi-agent intelligence, the deep policy gradient (DPG) method, and an innovative long short term memory (LSTM) based representation network for optimizing the offering strategies of multiple self-interested generation companies (GENCOs) as well as exploring the market outcome stemming from their interactions. The proposed approach is tailored to align with the nature of the examined problem by posing it, for the first time, in multi-dimensional continuous state and action spaces, enabling GENCOs to receive accurate feedback regarding the impact of their offering strategies on the market clearing outcome, and devise more profitable bidding decisions by exploiting the entire action domain, and thereby facilitates more accurate Equilibrium Analysis. The proposed LSTM-based representation network extracts discriminative features which further improves the learning performance and thus promises more profitable offerings strategies for each GENCO. Case studies demonstrate that the proposed method i) achieves a significantly higher profit than state-of-the-art RL methods for a single GENCO's optimal offering strategy problem and ii) outperforms the state-of-the-art Equilibrium programming models in efficiently identifying an imperfect market Equilibrium with/without network congestion. Quantitative economic Analysis is carried out on the obtained Equilibrium.