Long-term optimal portfolios with floor

Long-term risk-sensitive portfolio optimization is studied with floor constraint. A simple rule to characterize its solution is mentioned under a general setting. Following this rule, optimal portfolios are constructed in several ways, using the optimal portfolio without floor constraint, combined with ideas of dynamic portfolio insurance, such as CPPI (constant proportion portfolio insurance), OBPI (option-based portfolio insurance), and DFP (dynamic fund protection). In addition, examples are presented with explicit computations of solutions.     

Is the Market Portfolio a Dynamic Factor? Evidence from Individual Stock Returns

This paper uses a factor model to test whether the market portfolio is a dynamic factor in the sense that individual stock returns contain a premium linked to the conditional risk of the market portfolio. The market conditional risk is based on a decomposition of the market variance into a time-varying trend component and a transitory component. The evidence shows that the conditional market premium is rising when the permanent trend rises relative to the conditional variance. The evidence for individual stock returns supports the notion that the market portfolio is a dynamic factor. Individual stock return autocorrelations are fully explained by the time variation in the market premium. The risk premia attributed to static factors are statistically insignificant.     

What drives international portfolio flows?

Understanding what drives international portfolio flows has important policy implications for countries wishing to exert some control on the size, direction and volatility of the flows. This paper empirically assesses the relative contribution of common (push) and country-specific (pull) factors to the variation of bond and equity flows from the US to 55 other countries. Using a Bayesian dynamic latent factor model, we find that more than 80% of the variation in bond and equity flows is due to push factors from the US to other countries. Hence global economic forces seem to prevail over domestic economic forces in explaining movements in international portfolio flows. The dynamics of push and pull factors can be partially explained by US and foreign economic fundamentals.     

Estimating portfolio risk for tail risk protection strategies

We forecast portfolio risk for managing dynamic tail risk protection strategies, based on extreme value theory, expectile regression, copula‐GARCH and dynamic generalized autoregressive score models. Utilizing a loss function that overcomes the lack of elicitability for expected shortfall, we propose a novel expected shortfall (and value‐at‐risk) forecast combination approach, which dominates simple and sophisticated standalone models as well as a simple average combination approach in modeling the tail of the portfolio return distribution. While the associated dynamic risk targeting or portfolio insurance strategies provide effective downside protection, the latter strategies suffer less from inferior risk forecasts, given the defensive portfolio insurance mechanics.     

Dynamic mean–VaR portfolio selection in continuous time

The value-at-risk (VaR) is one of the most well-known downside risk measures due to its intuitive meaning and wide spectra of applications in practice. In this paper, we investigate the dynamic mean–VaR portfolio selection formulation in continuous time, while the majority of the current literature on mean–VaR portfolio selection mainly focuses on its static versions. Our contributions are twofold, in both building up a tractable formulation and deriving the corresponding optimal portfolio policy. By imposing a limit funding level on the terminal wealth, we conquer the ill-posedness exhibited in the original dynamic mean–VaR portfolio formulation. To overcome the difficulties arising from the VaR constraint and no bankruptcy constraint, we have combined the martingale approach with the quantile optimization technique in our solution framework to derive the optimal portfolio policy. In particular, we have characterized the condition for the existence of the Lagrange multiplier. When the opportunity set of the market setting is deterministic, the portfolio policy becomes analytical. Furthermore, the limit funding level not only enables us to solve the dynamic mean–VaR portfolio selection problem, but also offers a flexibility to tame the aggressiveness of the portfolio policy.     

A comparison of static and dynamic portfolio policies

Garleanu and Pedersen (2013) show that the optimal static portfolio policy in light of quadratic transaction costs is a weighted average of the existing portfolio and the target portfolio. In this paper, we demonstrate the importance of the robust target portfolio in the static portfolio policy that considers quadratic transaction costs. By using both empirical and simulated data, we find no evidence that the optimal dynamic portfolio policy proposed by Garleanu and Pedersen (2013) is superior to the static portfolio policy that trades towards the robust target portfolio. The robust target portfolio is achieved by either introducing time-varying covariances or restricting portfolio weights. Furthermore, the static portfolio with time-varying covariances and the short sale-constrained static portfolio are both very efficient in reducing portfolio turnover. The good performance of the static portfolio policy is robust to parameter uncertainty and trading parameters.     

Maximizing an equity portfolio excess growth rate: a new form of smart beta strategy?

It has been claimed that, for dynamic investment strategies, the simple act of rebalancing a portfolio can be a source of additional performance, sometimes referred to as the volatility pumping effect or the diversification bonus because volatility and diversification turn out to be key drivers of the portfolio performance. Stochastic portfolio theory suggests that the portfolio excess growth rate, defined as the difference between the portfolio expected growth rate and the weighted-average expected growth rate of the assets in the portfolio, is an important component of this additional performance (see Fernholz [Stochastic Portfolio Theory, 2002 (Springer)]). In this context, one might wonder whether maximizing a portfolio excess growth rate would lead to an improvement in the portfolio performance or risk-adjusted performance. This paper provides a thorough empirical analysis of the maximization of an equity portfolio excess growth rate in a portfolio construction context for individual stocks. In out-of-sample empirical tests conducted on individual stocks from 4 different regions (US, UK, Eurozone and Japan), we find that portfolios that maximize the excess growth rate are characterized by a strong negative exposure to the low volatility factor and a higher than 1 exposure to the market factor, implying that such portfolios are attractive alternatives to competing smart portfolios in markets where the low volatility anomaly does not hold (e.g. in the UK, or in rising interest rate scenarios) or in bull market environments.     

A dynamic model of active portfolio management with benchmark orientation

This paper studies optimal dynamic portfolios for investors concerned with the performance of their portfolios relative to a benchmark. Assuming that asset returns follow a multi-linear factor model similar to the structure of Ross (1976) [Ross, S., 1976. The arbitrage theory of the capital asset pricing model. Journal of Economic Theory, 13, 342–360] and that portfolio managers adopt a mean tracking error analysis similar to that of Roll (1992) [Roll, R., 1992. A mean/variance analysis of tracking error. Journal of Portfolio Management, 18, 13–22], we develop a dynamic model of active portfolio management maximizing risk adjusted excess return over a selected benchmark. Unlike the case of constant proportional portfolios for standard utility maximization, our optimal portfolio policy is state dependent, being a function of time to investment horizon, the return on the benchmark portfolio, and the return on the investment portfolio. We define a dynamic performance measure which relates portfolio’s return to its risk sensitivity. Abnormal returns at each point in time are quantified as the difference between the realized and the model-fitted returns. Risk sensitivity is estimated through a dynamic matching that minimizes the total fitted error of portfolio returns. For illustration, we analyze eight representative mutual funds in the U.S. market and show how this model can be used in practice.     

Learning about Predictability: The Effects of Parameter Uncertainty on Dynamic Asset Allocation

This paper examines the effects of uncertainty about the stock return predictability on optimal dynamic portfolio choice in a continuous time setting for a long-horizon investor. Uncertainty about the predictive relation affects the optimal portfolio choice through dynamic learning, and leads to a state-dependent relation between the optimal portfolio choice and the investment horizon. There is substantial market timing in the optimal hedge demands, which is caused by stochastic covariance between stock return and dynamic learning. The opportunity cost of ignoring predictability or learning is found to be quite substantial.     

Modelling default contagion using multivariate phase-type distributions

We model dynamic credit portfolio dependence by using default contagion in an intensity-based framework. Two different portfolios (with ten obligors), one in the European auto sector, the other in the European financial sector, are calibrated against their market CDS spreads and the corresponding CDS-correlations. After the calibration, which are perfect for the banking portfolio, and good for the auto case, we study several quantities of importance in active credit portfolio management. For example, implied multivariate default and survival distributions, multivariate conditional survival distributions, implied default correlations, expected default times and expected ordered default times. The default contagion is modelled by letting individual intensities jump when other defaults occur, but be constant between defaults. This model is translated into a Markov jump process, a so called multivariate phase-type distribution, which represents the default status in the credit portfolio. Matrix-analytic methods are then used to derive expressions for the quantities studied in the calibrated portfolios.     

Dynamic Trading with Predictable Returns and Transaction Costs

We derive a closed‐form optimal dynamic portfolio policy when trading is costly and security returns are predictable by signals with different mean‐reversion speeds. The optimal strategy is characterized by two principles: (1) aim in front of the target, and (2) trade partially toward the current aim. Specifically, the optimal updated portfolio is a linear combination of the existing portfolio and an “aim portfolio,” which is a weighted average of the current Markowitz portfolio (the moving target) and the expected Markowitz portfolios on all future dates (where the target is moving). Intuitively, predictors with slower mean‐reversion (alpha decay) get more weight in the aim portfolio. We implement the optimal strategy for commodity futures and find superior net returns relative to more naive benchmarks.     

Comparing Trading Performance of the Constant and Dynamic Hedge Models: A Note

The constant and dynamic hedge models, with the presence of transaction costs are compared for the Share Price Index futures contract trading on the Sydney Futures Exchange. The optimal hedge ratio is estimated by using a dynamic, bivariate two-stage model for the return equation with a dynamic GARCH error structure for the conditional hedge ratios. When portfolio projections are compared based on their profit positions (net of transaction costs), the GARCH hedge model dominates the next best competitor in terms of trading profit.     

Asset market games of survival: a synthesis of evolutionary and dynamic games

The paper examines a game-theoretic model of a financial market in which asset prices are determined endogenously in terms of a short-run equilibrium. Investors use general, adaptive strategies (portfolio rules) depending on the exogenous states of the world and the observed history of the game. The main goal is to identify portfolio rules, allowing an investor to “survive,” i.e., to possess a positive, bounded away from zero, share of market wealth over an infinite time horizon. The model under consideration combines a strategic framework characteristic for stochastic dynamic games with an evolutionary solution concept (survival strategies), thereby linking two fundamental paradigms of game theory.     

Higher-order moment risk connectedness and optimal investment strategies between international oil and commodity futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict

This paper investigates the higher-order moment risk connectedness between West Texas Intermediate (WTI) oil futures, Brent oil futures, Chinese oil futures and commodity futures (agricultural, industrial metals, and precious metals) before and during the COVID-19 pandemic and following the outbreak of the Russia-Ukraine conflict, by combining ex-post moment measures and the novel time-varying parameter (TVP)-vector auto-regression (VAR)-based connectedness approach. Further, this paper depicts the dynamic overall and pairwise correlations between oil and commodity futures and constructs the hedging and optimal-weighted portfolio strategies using the DCC-GARCH t-Copula model. This paper also constructs the multivariate oil-commodity portfolio based on the newly proposed minimum connectedness portfolio approach and takes into account the higher-order moment risk connectedness. The empirical results demonstrate that the dynamic linkages between international oil and commodity futures are positive, time-varying, and have been greatly intensified by the outbreak of the 2018 China-US trade war, the 2020 COVID-19 pandemic, and the 2022 Russia-Ukraine conflict. The risk connectedness results are moment-dependent. The averaged total skewness and kurtosis spillovers are lower than the return and volatility connectedness. Brent (WTI) oil is the largest net transmitter of the return and volatility (skewness and kurtosis) risk spillovers. The dynamic total, net, and net-pairwise spillovers are all time-varying and highly reactive to major crises, especially the COVID-19 pandemic and the Russia-Ukraine conflict. Furthermore, the optimal-weighted portfolio shows a higher risk reduction than the hedging strategy. Finally, the minimum skewness connectedness portfolio shows relatively higher hedging effectiveness, while the minimum kurtosis connectedness portfolio offers the highest cumulative returns.     

Dynamic portfolio optimization with ambiguity aversion

This paper investigates portfolio selection in the presence of transaction costs and ambiguity about return predictability. By distinguishing between ambiguity aversion to returns and to return predictors, we derive the optimal dynamic trading rule in closed form within the framework of Gârleanu and Pedersen (2013), using the robust optimization method. We characterize its properties and the unique mechanism through which ambiguity aversion impacts the optimal robust strategy. In addition to the two trading principles documented in Gârleanu and Pedersen (2013), our model further implies that the robust strategy aims to reduce the expected loss arising from estimation errors. Ambiguity-averse investors trade toward an aim portfolio that gives less weight to highly volatile return-predicting factors, and loads less on the securities that have large and costly positions in the existing portfolio. Using data on various commodity futures, we show that the robust strategy outperforms the corresponding non-robust strategy in out-of-sample tests.     

A bootstrap-based comparison of portfolio insurance strategies

This study presents a systematic comparison of portfolio insurance strategies. We implement a bootstrap-based hypothesis test to assess statistical significance of the differences in a variety of downside-oriented risk and performance measures for pairs of portfolio insurance strategies. Our comparison of different strategies considers the following distinguishing characteristics: static versus dynamic protection; initial wealth versus cumulated wealth protection; model-based versus model-free protection; and strong floor compliance versus probabilistic floor compliance. Our results indicate that the classical portfolio insurance strategies synthetic put and constant proportion portfolio insurance (CPPI) provide superior downside protection compared to a simple stop-loss trading rule and also exhibit a higher risk-adjusted performance in many cases (dependent on the applied performance measure). Analyzing recently developed strategies, neither the TIPP strategy (as an ‘improved’ CPPI strategy) nor the dynamic VaR-strategy provides significant improvements over the more traditional portfolio insurance strategies.     

A Dynamic Model of Firm Valuation

We propose a dynamic version of the dividend discount model, solve it in closed form, and assess its empirical validity. The valuation method is tractable and can be easily implemented. We find that our model produces equity value forecasts that are very close to market prices, and explains a large proportion of the observed variation in share prices. Moreover, we show that a simple portfolio strategy based on the difference between market and estimated values earns considerably positive returns. These returns cannot be simply explained either by the Fama‐French three‐factor model (even after adding a momentum factor) or the Fama‐French five‐factor model.     

The Impact of Portfolio Diversification on Trading Rules Profits: Some Evidence for UK Share Portfolios

This paper demonstrates how the autocorrelation structure of UK portfolio returns is linked to dynamic interrelationships among the component securities of that portfolio. Moreover, portfolio return autocorrelation is shown to be an increasing function of the number of securities in the portfolio. Since the security interrelationships seemed to be more a product of their history of non-synchronous trading than of systematic industry-related phenomena, it should not be possible to exploit the high levels of return persistence using trading rules. We show that rules designed to exploit this portfolio autocorrelation structure do not produce economic profits.     

A general equilibrium model of portfolio insurance

This article examines the effects of portfolio insurance onmarket and asset price dynamics in a general equilibrium continuous-timemodel. Portfolio insurers are modeled as expected utility maximizingagents. Martingale methods are employed in solving the individualagents' dynamic consumption-portfolio problems. Comparisonsare made between the optimal consumption processes, optimallyinvested wealth and portfolio strategies of the portfolio insurersand 'normal agents'. At a general equilibrium level, comparisonsacross economies reveal that the market volatility and riskpremium are decreased, and the asset and market price levelsincreased, by the presence of portfolio insurance.     

Analysis of Dynamic Correlation of Japanese Stock Returns with Network Clustering

In this paper, the dynamic correlation of Japanese stock returns is estimated by using the dynamic conditional correlation (DCC–GARCH) model to study their correlation dynamics empirically. It is difficult to fit the model to the whole stock market jointly at the same time; therefore, a network-based clustering is applied for the dimensionality reduction of the sample data. Two types correlation structures are estimated: homogeneous groups of stocks in a balanced size are created by clustering to observe within-group correlation, while a single portfolio that comprises group portfolio returns is also created to observe between-group correlation. The estimation result reveals dynamic changes in correlation intensity represented by the largest eigenvalue of the estimated correlation matrix. A higher level of correlation intensity and volatility are observed during the crisis periods, namely after both the Lehman collapse and the Great East Japan Earthquake, for the between- and within-group correlations. It is also confirmed that the pattern of correlation change is significantly different between the groups. The proposed method is useful for monitoring dynamic correlation of asset returns efficiently in a large scale of portfolio.