Dynamic Portfolio Model Research Articles

Dynamic Mean–Variance Portfolio Optimization with Value-at-Risk Constraint in Continuous Time

Recognizing the importance of incorporating different risk measures in the portfolio management model, this paper examines the dynamic mean-risk portfolio optimization problem using both variance and value at risk (VaR) as risk measures. By employing the martingale approach and integrating the quantile optimization technique, we provide a solution framework for this problem. We demonstrate that, under a general market setting, the optimal terminal wealth may exhibit different patterns. When the market parameters are deterministic, we derive the closed-form solution for this problem. Examples are provided to illustrate the solution procedure of our method and demonstrate the benefits of our dynamic portfolio model compared to its static counterpart.

Green transition, investment horizon, and dynamic portfolio decisions

This paper analyzes the implications of investors’ short-term oriented asset holding and portfolio decisions (or short-termism), and its consequences on green investments. We adopt a dynamic portfolio model, which contrary to conventional static mean-variance models, allows us to study optimal portfolios for different decision horizons. Our baseline model contains two assets, one asset with fluctuating returns and another asset with a constant risk-free return. The asset with fluctuating returns can arise from fossil-fuel based sectors or from clean energy related sectors. We consider different drivers of short-termism: the discount rate, the nature of discounting (exponential vs. hyperbolic), and the decision horizon of investors itself. We study first the implications of these determinants of short-termism on the portfolio wealth dynamics of the baseline model. We find that portfolio wealth declines faster with a higher discount rate, with hyperbolic discounting, and with shorter decision horizon. We extend our model to include a portfolio of two assets with fluctuating returns. For both model variants, we explore the cases where innovation efforts are spent on fossil fuel or clean energy sources. Detailing dynamic portfolio decisions in such a way may allow us for better pathways to empirical tests and may provide guidance to some online financial decision making.

Green Bonds for the Transition to a Low-Carbon Economy

The green bond market is emerging as an impactful financing mechanism in climate change mitigation efforts. The effectiveness of the financial market for this transition to a low-carbon economy depends on attracting investors and removing financial market roadblocks. This paper investigates the differential bond performance of green vs non-green bonds with (1) a dynamic portfolio model that integrates negative as well as positive externality effects and via (2) econometric analyses of aggregate green bond and corporate energy time-series indices; as well as a cross-sectional set of individual bonds issued between 1 January 2017, and 1 October 2020. The asset pricing model demonstrates that, in the long-run, the positive externalities of green bonds benefit the economy through positive social returns. We use a deterministic and a stochastic version of the dynamic portfolio approach to obtain model-driven results and evaluate those through our empirical evidence using harmonic estimations. The econometric analysis of this study focuses on volatility and the risk–return performance (Sharpe ratio) of green and non-green bonds, and extends recent econometric studies that focused on yield differentials of green and non-green bonds. A modified Sharpe ratio analysis, cross-sectional methods, harmonic estimations, bond pairing estimations, as well as regression tree methodology, indicate that green bonds tend to show lower volatility and deliver superior Sharpe ratios (while the evidence for green premia is mixed). As a result, green bond investment can protect investors and portfolios from oil price and business cycle fluctuations, and stabilize portfolio returns and volatility. Policymakers are encouraged to make use of the financial benefits of green instruments and increase the financial flows towards sustainable economic activities to accelerate a low-carbon transition.

Modeling Price Dynamics, Optimal Portfolios, and Option Valuation for Cryptoassets

The authors demonstrate the construction of an optimal dynamic portfolio of cryptoassets that minimizes either return variance or conditional value at risk. One can view such a portfolio as a minimum-risk index for this asset class. They carefully backtested the dynamic portfolio model and developed a fair valuation model for options based on a dynamic pricing model for the underlying cryptoasset index. They obtain the valuation by passing from the natural world to the equivalent martingale measure via the Esscher transform. The work underscores the need for a cryptoasset index–based exchange-traded fund, the development of derivatives, particularly for cryptoportfolio insurance purposes, and the development of (nearly) riskless rates for this asset class. <b>TOPICS:</b>Currency, options, statistical methods, VAR and use of alternative risk measures of trading risk <b>Key Findings</b> ▪ This article provides a methodology for constructing an optimal dynamic portfolio of major cryptoassets that minimizes either return variance or conditional value at risk. ▪ The authors develop a fair valuation model for options based on a dynamic pricing model for the underlying cryptoindex. ▪ The work emphasizes the need for a cryptoasset index–based exchange-traded fund and the development of derivatives, in particular for cryptoportfolio insurance purposes.

The Efficiency of Dynamic Tracking Formulation for Investment Portfolio Selection Problem

In the paper we consider dynamic investment portfolio model. Investment strategy is minimization of the expectation of terminal wealth squared deviation from the benchmark portfolio. We showed that the optimal tracking portfolio holds mean-variance efficiency.

Optimal Tax-Timing Strategy in the Presence of Transaction Costs

Although prevalent in the financial markets, transaction costs have been largely ignored in the existing literature on the impact of capital gains tax. We develop a dynamic portfolio model that incorporates both transaction costs and annually payable capital gains taxes. We theoretically characterize and numerically compute the optimal tax-timing strategy. We show that our model can help explain the puzzle that even when investors face equal long-term/short-term capital gains tax rates, they may still defer realizing large capital losses and vary the realization strategies of capital gains and losses across months in a calendar year. In addition, our model provides some unique, empirically testable implications, such as (1) investors with capital losses may purchase a large number of shares without realizing any losses; and (2) investors' trading may increase with transaction costs.

Dynamic mean-variance portfolio optimization with Value-at-Risk constraint in continuous-time

This paper studies the dynamic mean-risk portfolio optimization problem with variance and Value-at-Risk(VaR) as the risk measures in recognizing the importance of incorporating different risk measures in the portfolio management model. Using the martingale approach and combining it with the quantile optimization technique, we provide the solution framework for this problem and show that the optimal terminal wealth may have different patterns under a general market setting. When the market parameters are deterministic, we develop the closed-form solution for this problem. Examples are provided to illustrate the solution procedure of our method and demonstrate the beneft of our dynamic portfolio model comparing with its static counterpart.

Management of Market Assets in Crisis

Introduction, Purpose . Most of the classic and modern investment models do not take into account the crisis processes in the economy. This happens mostly because crises are not studied well enough and serious researches are to be made in this sphere. Methods . This paper offers a model for estimating the market condition, considering the five edge states: crisis growth, growth, stagnation, recession, crisis recession. In this paper has been developed a crisis indicator, which can be the basis for determination of the crisis initial phase. Results . A concept of a dynamic portfolio model, adapting to the market changes, has been introduced to manage market assets. Adaptation of the investment portfolio occurs according to the crisis processes in economy. Investment is understood as a process of buying assets for profit-making. The term “strategic investment” is used to refer to long-range investments. Financial assets exist in various forms and may range from almost completely safe low yield government bonds to international assets that are at much greater risk and produce more profit. Discussion . Usually investor owns an assets bundle. A set of financial assets, bounded by a single investment strategy, is called an investment portfolio. Warren Buffett states that a profitable investment strategy should be long-ranged with selection of the right assets. For managing the market assets the authors suggest the use of the dynamic investment strategy which adapts to the crisis.

The Predominance of Real Estate in the Household Portfolio

This paper investigates why household portfolios are heavily skewed toward real estate. Previous studies suggest that the large portfolio share of real estate primarily stems from non-investment-related motives as homeowners are often forced to invest heavily to buy the home they want to consume. In contrast, we show that homeowners would still invest the bulk of their wealth in real estate in a frictionless setting where they could own and consume separate amounts of housing. We provide empirical support to this argument and derive a dynamic portfolio model to study why real estate has such a strong investment appeal.

Continuous time portfolio optimization

This paper presents dynamic portfolio model based on the Merton's optimal investment-consumption model, which combines dynamic synthetic put option using risk-free and risky assets. This paper is extended version of methodological paper published by Yuan Yao (2012). Because of the long history of the development of foreign financial market, with a variety of financial derivatives, the study on theory or empirical analysis of portfolio insurance focused on how best can portfolio strategies be used in minimizing risk and market volatility. In this paper, stock and risk-free assets are used to replicate options and to establish a new dynamic model to analyze the implementation of the dynamic process of investors' actions using dynamic replication strategy. Our results show that investors' optimal strategies of portfolio are not dependent on their wealth, but are dependent on market risk and this new methodology is broaden in compare to paper of Yuan Yao (2012).

Hedging Supply Risks: An Optimal Water Portfolio

Although water supply diversification has been proposed as a solution to dwindling water reserves, the optimal mix of natural and manufactured sources of water remains largely unexplored. We develop a dynamic portfolio model of water supply that hedges against the supply risks from all potential water sources, by taking into account the size of water reserves, uncertainties of water flows as well as differences in supply costs. The optimal portfolio shares for an existing water supply system are derived and compared with the observed contributions to total water stock, revealing unexploited hedging opportunities between various naturally occurring water sources as well as a general over‐reliance on manufactured water. The optimal solution implies that future supply augmentations should target natural sources of water ahead of manufactured water. It is estimated that the optimization of the water supply portfolio for a medium‐sized city results in annual cost‐savings of up to $463 million.

Construction of Dynamic Portfolio Modeling Based on Order Impact Coefficient and its Empirical Analysis

The fund flow reflects investors’ choosing stocks, and contains investors’ trading information. Order flow is one of core variables of financial market microstructure theory, and it can characterize the fund flow. In order to capture the information of the fund flow, this paper introduces order flow indicator to measure net inflows, and puts forward order impact coefficient to characterize fund inflow and its change rate. From the perspective of investors’ expected utility maximization, this paper determines portfolio weight according to order impact coefficient, and constructs the dynamic portfolio model with order impact coefficient. Using the optimization theory and methods, by constructing Lagrange function, this paper gains the optimal portfolio weights of the dynamic portfolio model. This paper arbitrarily selects weekly data of six stocks listed on Chinese Shenzhen Stock Exchange to do an empirical analysis. The empirical results show that weekly return of dynamic portfolio based on order impact coefficient is higher than both mean-variance portfolio return and Shenzhen composite index return. Accumulative return of dynamic portfolio based on order impact coefficient is higher than the latter two by 8.47% and 11.46% respectively. This indicates that the dynamic portfolio model based on order impact coefficient can obtain better investment return.

Dynamic optimal capital growth with risk constraints

In this paper, risk metrics in capital growth and drawdown as a financial risk measure were considered. Moreover, we developed a dynamic portfolio management model with constraints on the maximal drawdown. Exact optimization algorithms run into difficulties in this framework and this motivates the investigation of simulated annealing optimized algorithm to solve the problem of maximizing long term growth of simultaneous risky investment. Empirical research indicates that the approach is inspiring for this class of portfolio optimization problems.

A Dynamic Risk Allocation of Value-at-Risks with Portfolios

A mathematical dynamic portfolio model with uncertainty is discussed by use of value-at-risks. The risk criterion is composed by the sum of unexpected shortterm risks which occur suddenly in each period. By dynamic programming approach, we derive an optimality condition for the optimal value-at-risk portfolio in a stochastic decision process. It is shown that the optimal value-at-risk is a solution of the optimality equation under a reasonable assumption, and an optimal trading strategy is obtained from the equation. A numerical example is given to illustrate our idea.

A dynamic CVaR‐portfolio approach using real options: an application to energy investments

SUMMARYEven though portfolio theory has increasingly been applied to analyze large‐scale investments under uncertainty—and especially so in the electricity sector—most analysis so far has been based on the static mean‐variance approach. Such an approach has two shortcomings: on the one hand, it fails to take into account irreversibility in the form of high sunk costs and the associated implications for optimal dynamic behavior. On the other hand, variance is not always the ideal risk measure, given that return or cost distributions are not necessarily normal. In fact, if large, potential losses are involved, it makes more sense to adopt a risk measure that can also take into account fat tails. In this paper, we generate these distributions arising from the investment behavior optimized in a real options model, thus accounting for uncertainty and irreversibility at the plant level, and use them in a dynamic portfolio model, where the conditional value‐at‐risk (CVaR) is the risk measure. More specifically, we look at the dynamics of the (CVaR‐) optimal technology mix over a future time period conditional on the initial distribution of technologies, such that given energy demand is met. The application to investment in the electricity sector with uncertain climate change policy shows that this approach is not only useful from the aggregate investment point‐of‐view but also for the purpose of evaluating the effects of policy on investment patterns and the resulting energy mix. Copyright © 2010 John Wiley &amp; Sons, Ltd.

How Does Programa Familias Guardabosques Affect Criminality? (Impacto del programa familia guardabosques sobre la criminalidad)

There is a double-causality relationship between narcotraffic and violence in Colombia. This paper proposes a theoretical dynamic portfolio model on criminal activity, that explains the retroalimentation process between these two phenomena through criminality traps, resulting from low levels of human and social capital. Policies aiming at resolving these traps should have an impact not only on narcotraffic, but also on violence, in order to be effective. Different economic evaluations give evidence supporting the efficiency of alternative development in diminishing narcotraffic. This paper studies the effect that the colombian program of alternative development Familias Guardabosques (Forest Ranger Families) has had on violence. Results show that the affiliation to the program diminishes the homicide rate on a municipal level in its first two stages, while the effect has no significance in the program’s stages III and IV, when the economic incentive delivered to the families diminishes.

Trend-following hedge funds and multi-period asset allocation

Selected hedge funds employ trend-following strategies in an attempt to achieve superior risk-adjusted returns. We employ a lookback straddle approach for evaluating the return characteristics of a trend-following strategy. The strategies can improve investor performance in the context of a multi-period dynamic portfolio model. The gains are achieved by taking advantage of the funds' high level of volatility. A set of empirical results confirms the advantages of the lookback straddle for investors at the top end of the multi-period efficient frontier.

Income uncertainty, substitution effect and relative yield spreads

The persistent downward-sloping term structure of relative yield spreads of U.S. taxable and tax-exempt bonds has been a puzzle in economics and finance. Since the late 1980s, a number of researchers have found this “anomaly” and analyzed this phenomenon from various vantage points such as market segmentation, special trading behaviors, and risk-aversion. This paper suggests that the term structure may be affected by the dynamic portfolio choice of risk-averse investors who face uncertain future income and tax rates. This paper proposes a dynamic portfolio model that can incorporate future uncertainty into a dynamic decision process. It is hoped that this model could offer some new insight into possible causes.

Dynamic Portfolio Adjustment and Capital Controls: A Euler Equation Approach

We present a multiasset dynamic portfolio balance model that is based on the maximization of an intertemporal utility function in consumption when investors operate under uncertainty, quadratic adjustment costs, and capital market regulations. We estimate the model's structural parameters on quarterly portfolio data of the German private sector. Asset demand is insensitive to return changes, although the coefficients are significant. Adjustment costs are low but highly significant, giving rise to moderate lags of adjustment. Still, existing capital controls and adjustment costs cannot explain the currently observed “home bias” in the portfolio. Overall, our results imply a strong rejection of the portfolio model.

Dynamic Portfolio Adjustment and Capital Controls: A Euler Equation Approach

We present a multiasset dynamic portfolio balance model that is based on the maximization of an intertemporal utility function in consumption when investors operate under uncertainty, quadratic adjustment costs, and capital market regulations. We estimate the model's structural parameters on quarterly portfolio data of the German private sector. Asset demand is insensitive to return changes, although the coefficients are significant. Adjustment costs are low but highly significant, giving rise to moderate lags of adjustment. Still, existing capital controls and adjustment costs cannot explain the currently observed "home bias" in the portfolio. Overall, our results imply a strong rejection of the portfolio model.