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Alexander Schied - One of the best experts on this subject based on the ideXlab platform.

  • optimal portfolio liquidation in target zone models and catalytic superProcesses
    Finance and Stochastics, 2016
    Co-Authors: Eyal Neuman, Alexander Schied
    Abstract:

    We study optimal buying and selling strategies in target zone models. In these models, the Price is modelled by a diffusion Process which is reflected at one or more barriers. Such models arise, for example, when a currency exchange rate is kept above a certain threshold due to central bank interventions. We consider the optimal portfolio liquidation problem for an investor for whom Prices are optimal at the barrier and who creates temporary Price impact. This problem is formulated as the minimization of a cost–risk functional over strategies that only trade when the Price Process is located at the barrier. We solve the corresponding singular stochastic control problem by means of a scaling limit of critical branching particle systems, which is known as a catalytic superProcess. In this setting, the catalyst is given by the barriers of the Price Process. For the cases in which the unaffected Price Process is a reflected arithmetic or geometric Brownian motion with drift, we moreover give a detailed financial justification of our cost functional by means of an approximation with discrete-time models.

  • optimal portfolio liquidation in target zone models and catalytic superProcesses
    2015
    Co-Authors: Eyal Neuman, Alexander Schied
    Abstract:

    We study optimal buying and selling strategies in target zone models. In these models the Price is modeled by a diffusion Process which is reflected at one or more barriers. Such models arise for example when a currency exchange rate is kept above a certain threshold due to central bank intervention. We consider the optimal portfolio liquidation problem for an investor for whom Prices are optimal at the barrier and who creates temporary Price impact. This problem will be formulated as the minimization of a cost-risk functional over strategies that only trade when the Price Process is located at the barrier. We solve the corresponding singular stochastic control problem by means of a scaling limit of critical branching particle systems, which is known as a catalytic superProcess. In this setting the catalyst is a set of points which is given by the barriers of the Price Process. For the cases in which the unaffected Price Process is a reflected arithmetic or geometric Brownian motion with drift, we moreover give a detailed financial justification of our cost functional by means of an approximation with discrete-time models.

  • OPTIMAL TRADE EXECUTION UNDER GEOMETRIC BROWNIAN MOTION IN THE ALMGREN AND CHRISS FRAMEWORK
    International Journal of Theoretical and Applied Finance, 2011
    Co-Authors: Jim Gatheral, Alexander Schied
    Abstract:

    With an alternative choice of risk criterion, we solve the HJB equation explicitly to find a closed-form solution for the optimal trade execution strategy in the Almgren–Chriss framework assuming the underlying unaffected stock Price Process is geometric Brownian motion.

Martin Larsson - One of the best experts on this subject based on the ideXlab platform.

  • informational efficiency with trading constraints a characterization
    Siam Journal on Financial Mathematics, 2020
    Co-Authors: Robert A Jarrow, Martin Larsson
    Abstract:

    Given a market with a Price Process $S$ populated by heterogeneous traders with differential information, beliefs, and trading constraints, let the smallest information set containing all of the tr...

  • informational efficiency under short sale constraints
    Social Science Research Network, 2013
    Co-Authors: Robert A Jarrow, Martin Larsson
    Abstract:

    A constrained informationally efficient market is defined to be one whose Price Process arises as the outcome of some equilibrium where agents face restrictions on trade. This paper investigates the case of short sale constraints, a setting which despite its simplicity, generates new insights. In particular, it is shown that short sale constrained informationally efficient markets always admit equivalent supermartingale measures and local martingale deflators, but not necessarily local martingale measures. And if in addition some local martingale deflator turns the Price Process into a true martingale, then the market is informationally efficient. Examples are given to illustrate the subtle phenomena that can arise in the presence of short sale constraints, with particular attention to representative agent equilibria and the different notions of no arbitrage.

Tae-hwy Lee - One of the best experts on this subject based on the ideXlab platform.

  • Forecasting Realized Volatility Using Subsample Averaging
    2014
    Co-Authors: Tae-hwy Lee, Huiyu Huang
    Abstract:

    When the observed Price Process is the true underlying Price Process plus microstructure noise, it is known that realized volatility (RV) estimates will be overwhelmed by the noise when the sampling frequency approaches infinity. Therefore, it may be optimal to sample less frequently, and averaging the less frequently sampled subsamples can improve estimation for quadratic variation. In this paper, we extend this idea to forecasting daily realized volatility. While the subsample-averaging has been proposed and used in estimating RV, this paper is the first that uses the subsample-averaging for forecasting RV. The subsample averaging method we examine incorporates the high frequency data in different levels of systematic sampling. It first pools the high frequency data into several subsamples, that generates forecasts from each subsample, and then combine these forecasts. We find that, in daily S&P 500 return RV forecasts, subsample-averaging generates better forecasts than those using only one subsample without averaging over all subsamples.

  • Forecasting Realized Volatility Using Subsample Averaging
    Open Journal of Statistics, 2013
    Co-Authors: Huiyu Huang, Tae-hwy Lee
    Abstract:

    When the observed Price Process is the true underlying Price Process plus microstructure noise, it is known that realized volatility (RV) estimates will be overwhelmed by the noise when the sampling frequency approaches infinity. Therefore, it may be optimal to sample less frequently, and averaging the less frequently sampled subsamples can improve estimation for quadratic variation. In this paper, we extend this idea to forecasting daily realized volatility. While subsample averaging has been proposed and used in estimating RV, this paper is the first that uses subsample averaging for forecasting RV. The subsample averaging method we examine incorporates the high frequency data in different levels of systematic sampling. It first pools the high frequency data into several subsamples, then generates forecasts from each subsample, and then combines these forecasts. We find that in daily S&P 500 return realized volatility forecasts, subsample averaging generates better forecasts than those using only one subsample.

Robert A Jarrow - One of the best experts on this subject based on the ideXlab platform.

  • informational efficiency with trading constraints a characterization
    Siam Journal on Financial Mathematics, 2020
    Co-Authors: Robert A Jarrow, Martin Larsson
    Abstract:

    Given a market with a Price Process $S$ populated by heterogeneous traders with differential information, beliefs, and trading constraints, let the smallest information set containing all of the tr...

  • informational efficiency under short sale constraints
    Social Science Research Network, 2013
    Co-Authors: Robert A Jarrow, Martin Larsson
    Abstract:

    A constrained informationally efficient market is defined to be one whose Price Process arises as the outcome of some equilibrium where agents face restrictions on trade. This paper investigates the case of short sale constraints, a setting which despite its simplicity, generates new insights. In particular, it is shown that short sale constrained informationally efficient markets always admit equivalent supermartingale measures and local martingale deflators, but not necessarily local martingale measures. And if in addition some local martingale deflator turns the Price Process into a true martingale, then the market is informationally efficient. Examples are given to illustrate the subtle phenomena that can arise in the presence of short sale constraints, with particular attention to representative agent equilibria and the different notions of no arbitrage.

Huiyu Huang - One of the best experts on this subject based on the ideXlab platform.

  • Forecasting Realized Volatility Using Subsample Averaging
    2014
    Co-Authors: Tae-hwy Lee, Huiyu Huang
    Abstract:

    When the observed Price Process is the true underlying Price Process plus microstructure noise, it is known that realized volatility (RV) estimates will be overwhelmed by the noise when the sampling frequency approaches infinity. Therefore, it may be optimal to sample less frequently, and averaging the less frequently sampled subsamples can improve estimation for quadratic variation. In this paper, we extend this idea to forecasting daily realized volatility. While the subsample-averaging has been proposed and used in estimating RV, this paper is the first that uses the subsample-averaging for forecasting RV. The subsample averaging method we examine incorporates the high frequency data in different levels of systematic sampling. It first pools the high frequency data into several subsamples, that generates forecasts from each subsample, and then combine these forecasts. We find that, in daily S&P 500 return RV forecasts, subsample-averaging generates better forecasts than those using only one subsample without averaging over all subsamples.

  • Forecasting Realized Volatility Using Subsample Averaging
    Open Journal of Statistics, 2013
    Co-Authors: Huiyu Huang, Tae-hwy Lee
    Abstract:

    When the observed Price Process is the true underlying Price Process plus microstructure noise, it is known that realized volatility (RV) estimates will be overwhelmed by the noise when the sampling frequency approaches infinity. Therefore, it may be optimal to sample less frequently, and averaging the less frequently sampled subsamples can improve estimation for quadratic variation. In this paper, we extend this idea to forecasting daily realized volatility. While subsample averaging has been proposed and used in estimating RV, this paper is the first that uses subsample averaging for forecasting RV. The subsample averaging method we examine incorporates the high frequency data in different levels of systematic sampling. It first pools the high frequency data into several subsamples, then generates forecasts from each subsample, and then combines these forecasts. We find that in daily S&P 500 return realized volatility forecasts, subsample averaging generates better forecasts than those using only one subsample.