Trend Following Trading Under a Regime Switching Model

Dai, Min; Yang, Zhou; Zhang, Qing; Zhu, Qiji Jim

doi:10.2139/ssrn.1630903

Cited by 17 publications

(35 citation statements)

References 19 publications

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“…The issues covered along the line are the trendfollowing problem [19,20,29] and the portfolio optimization problem [30]. In the first issue, we consider an exponential natural evolution strategy (NES) [31] an efficent trend following strategy (For details, please refer to [20,29]), and propose the strategy of using the transaction cost, K, as a tuning parameter that can vary according to the GPC results (Fig.…”

Section: Applicationsmentioning

confidence: 99%

“…We apply a simplified version of the sparse Gaussian process (GP) classification method, which is a direct result of two recent remarkable Gaussian process papers [25,26], for performing risk sensitivity classification in dealing with financial portfolio management. Since portfolio management problems are optimal decision-making problems that rely on actual empirical data, theoretical and practical solutions can be formulated via many of recent machine learning and control advancements: the traditional mean-variance efficient portfolio problem [11]; index tracking portfolio formulation [12][13][14][15]; risk-adjusted expected return maximizing strategy [16][17][18]; trend following strategy [19][20][21][22][23]; long-short trading strategy (including the pairs trading strategy) [20,24], etc. In this paper, we also raise two important portfolio management issues in which the GP application results can be useful.…”

Section: Introductionmentioning

confidence: 99%

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Some Observations for Portfolio Management Applications of Modern Machine Learning Methods

Park

Heo

Kim

et al. 2016

IJFIS

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Recently, artificial intelligence has reached the level of top information technologies that will have significant influence over many aspects of our future lifestyles. In particular, in the fields of machine learning technologies for classification and decision-making, there have been a lot of research efforts for solving estimation and control problems that appear in the various kinds of portfolio management problems via data-driven approaches. Note that these modern data-driven approaches, which try to find solutions to the problems based on relevant empirical data rather than mathematical analyses, are useful particularly in practical application domains. In this paper, we consider some applications of modern data-driven machine learning methods for portfolio management problems. More precisely, we apply a simplified version of the sparse Gaussian process (GP) classification method for classifying users' sensitivity with respect to financial risk, and then present two portfolio management issues in which the GP application results can be useful. Experimental results show that the GP applications work well in handling simulated data sets.

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Section: Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Some Observations for Portfolio Management Applications of Modern Machine Learning Methods

Park

Heo

Kim

et al. 2016

IJFIS

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“…Stochastic approximation methods were developed in [15]. A trend following strategy was developed in [1] with the use of a Wonham filter to estimate bull market conditional probability given up-todate stock prices. Similar idea was developed following a confidence interval approach in [4].…”

Section: Introductionmentioning

confidence: 99%

A Constrained Stochastic Control Problem with Application to an Illiquid Stock Position Build-Up

Lu¹,

Yin²,

Zhang³

et al. 2017

2017 Proceedings of the Conference on Control and Its Applications

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We consider a class of constrained stochastic optimal control problems with applications to an illiquid stock position build-up. Using a geometric Brownian motion model, we allow the drift to be purchase-rate dependent to characterize "price impact" of heavy share accumulation over time. The constraint is the fund availability. That is, the expected fund availability has an upper bound. We use a Lagrange multiplier method to treat the constrained control problem. Because a closed-form solution is virtually impossible to obtained, we develop approximation schemes, which consist of inner and outer approximations. The inner approximation is a numerical procedure for obtaining optimal strategies based on a fixed parameter of the Lagrange multiplier. The outer approximation is a stochastic approximation algorithm for obtaining the optimal Lagrange multiplier. Convergence analysis together with numerical examples are provided.

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“…Recent research has shown that models based on stochastic volatility, jump diffusion, and regime switching processes produce better fits to market data. A nonexhaustive list of regime switching applications includes insurance [22], electricity markets [21,40], natural gas [12,2], optimal forestry management [11], trading strategies [15], valuation of stock loans [44], convertible bond pricing [3], and interest rate dynamics [27]. Regime switching models are intuitively appealing, and computationally inexpensive compared to a stochastic volatility jump diffusion model.…”

Section: Introductionmentioning

confidence: 99%

Methods for Pricing American Options under Regime Switching

Huang¹,

Forsyth²,

Labahn³

2011

SIAM J. Sci. Comput.

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Abstract. We analyze a number of techniques for pricing American options under a regime switching stochastic process. The techniques analyzed include both explicit and implicit discretizations with the focus being on methods which are unconditionally stable. In the case of implicit methods we also compare a number of iterative procedures for solving the associated nonlinear algebraic equations. Numerical tests indicate that a fixed point policy iteration, coupled with a direct control formulation, is a reliable general purpose method. Finally, we remark that we formulate the American problem as an abstract optimal control problem; hence our results are applicable to more general problems as well. Key words. regime switching, American options, iterative methods AMS subject classifications. 65N06, 93C20DOI. 10.1137/1108209201. Introduction. The standard approach to valuation of contingent claims (also known as derivatives) is to specify a stochastic process for the underlying asset and then construct a dynamic, self-financing hedging portfolio to minimize risk. The initial cost of constructing the portfolio is then considered to be the fair value of the contingent claim. This has been used with great success in the case of stochastic processes having constant volatility in the case of both European and (the more difficult) American options.However, it is well known that a financial model which follows a stochastic process having constant volatility is not consistent with market prices. Recent research has shown that models based on stochastic volatility, jump diffusion, and regime switching processes produce better fits to market data. A nonexhaustive list of regime switching applications includes insurance [22] [3], and interest rate dynamics [27]. Regime switching models are intuitively appealing, and computationally inexpensive compared to a stochastic volatility jump diffusion model.In this paper we study numerical techniques for the solution of American option contracts under regime switching. While our examples focus on problems with constant properties in each regime, the numerical methods developed can easily be applied to cases where the properties in each regime are more complex. An example would be the use of price-dependent regime switching (i.e., default hazard rates) in convertible bond pricing [4]. A number of different methods has been proposed for handling American options under regime switching models. Semianalytic approaches have been

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Trend Following Trading Under a Regime Switching Model

Cited by 17 publications

References 19 publications

Some Observations for Portfolio Management Applications of Modern Machine Learning Methods

Some Observations for Portfolio Management Applications of Modern Machine Learning Methods

A Constrained Stochastic Control Problem with Application to an Illiquid Stock Position Build-Up

Methods for Pricing American Options under Regime Switching

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