We propose an early warning system to timely forecast turbulence in the US stock market. In a first step, a Markov-switching model with two regimes (a calm market and a turbulent market) is developed. Based on the time series of the monthly returns of the S&P 500 price index, the corresponding filtered probabilities are successively estimated. In a second step, the turbulent phase of the model is further specified to distinguish between bullish and bearish trends. For comparison only, a Markov-switching model with three states (a calm market, a turbulent bullish market, and a turbulent bearish market) is examined as well. In a third step, logistic regression models are employed to forecast the filtered probabilities provided by the Markov-switching models. A major advantage of the presented modeling framework is the timely identification of the factors driving the different phases of the capital market. In a fourth step, the early warning system is applied to an asset management case study. The results show that explicit consideration of the models’ signals yields better portfolio performance and lower portfolio risk compared to standard buy-and-hold and constant proportion portfolio insurance strategies.

Forecasting market turbulence using regime-switching models

We consider a stochastic-factor financial model wherein the asset price and the stochastic-factor processes depend on an observable Markov chain and exhibit an affine structure. We are faced with a finite investment horizon and derive optimal dynamic investment strategies that maximize the investor's expected utility from terminal wealth. To this end we apply Merton's approach, because we are dealing with an incomplete market. Based on the semimartingale characterization of Markov chains, we first derive the Hamilton–Jacobi–Bellman (HJB) equations that, in our case, correspond to a system of coupled nonlinear partial differential equations (PDE). Exploiting the affine structure of the model, we derive simple expressions for the solution in the case with no leverage, i.e. no correlation between the Brownian motions driving the asset price and the stochastic factor. In the presence of leverage, we propose a separable ansatz that leads to explicit solutions. General verification results are also proved. The results are illustrated for the special case of a Markov-modulated Heston model.

Portfolio optimization in affine models with markov switching

Recent periods of market turbulence and stress have created considerable interest in credible alternatives to traditional asset allocation methodologies. It would be preferred if portfolios can be decomposed into components that can be directly connected to independent risks and individually rewarded by the market for their level of risk. This can be achieved through factor-based investing, which relies on the observation that most return and risk characteristics for all asset classes can be well explained by particular building blocks, or factors.We describe main features of factors, factor investing and factor models, with emphasis placed on practical topics such as selection of significant factors associated to specific asset classes, differentiating between factors, anomalies or stylized facts, and preference for composite portfolios based on combining factors. We have also analyzed implementation details and the factor risk parity strategy.Then we consider improvements to factor-based investing through regime switching and sensitivity analysis. We present theoretical and practical frameworks for Markov switching models and for sensitivity analysis, and rely on representative examples to illustrate the benefits of efficiently incorporating regimes and sensitivity analysis into portfolio management.The final section describes features of good testing procedures for portfolio behavior and performance, in contrasts with possible testing pitfalls.

Better Investing Through Factors, Regimes and Sensitivity Analysis

A comparison of the gold-oil portfolio and oil portfolio: A stochastic dominance approach

We present a flexible multidimensional bond–stock model incorporating regime switching, a stochastic short rate and further stochastic factors, such as stochastic asset covariance. In this framework we consider an investor whose risk preferences are characterized by the hyperbolic absolute risk-aversion utility function and solve the problem of optimizing the expected utility from her terminal wealth. For the optimal portfolio we obtain a constant-proportion portfolio insurance-type strategy with a Markov-switching stochastic multiplier and prove that it assures a lower bound on the terminal wealth. Explicit and easy-to-use verification theorems are proven. Furthermore, we apply the results to a specific model. We estimate the model parameters and test the performance of the derived optimal strategy using real data. The influence of the investor’s risk preferences and the model parameters on the portfolio is studied in detail. A comparison to the results with the power utility function is also pro...

HARA utility maximization in a Markov-switching bond–stock market

In this article, the dependence structure of the asset classes stocks, government bonds, and corporate bonds in different market environments and its implications on asset management are investigated for the US, European, and Asian market. Asset returns are modelled by a Markov-switching model which allows for two market regimes with completely different risk-return structures. Using major stock indices from all three regions, calm and turbulent market periods are identified for the time period between 1987 and 2009 and the correlation structures in the respective periods are compared. It turns out that the correlations between as well as within the asset classes under investigation are far from being stable and vary significantly between calm and turbulent market periods as well as in time. It also turns out that the US and European markets are much more integrated than the Asian and US/European ones. Moreover, the Asian market features more and longer turbulence phases. Finally, the impact of these findings is examined in a portfolio optimization context. To accomplish this, a case study using the mean-variance and the mean-conditional-value-at-risk framework as well as two levels of risk aversion is conducted. The results show that an explicit consideration of different market conditions in the modelling framework yields better portfolio performance as well as lower portfolio risk compared to standard approaches. These findings hold true for all investigated optimization frameworks and risk-aversion levels.

Asset correlations in turbulent markets and the impact of different regimes on asset management

In this article, the dependence structure of the asset classes stocks, government bonds, and corporate bonds in different market environments and its implications on asset management are investigated. Asset returns are modelled by a Markov-switching model which allows for two market regimes with completely different risk-return structures. Using major stock indices, calm and turbulent market periods are identified for the time period between 1987 and 2009 and the correlation structures in the respective periods are compared. It turns out that the correlations between as well as within the asset classes under investigation are far from being stable and vary significantly between calm and turbulent market periods as well as in time. Finally, the impact of these findings is examined in a portfolio optimization context. To accomplish this, a case study using the mean-variance and the mean-conditional-value-at-risk framework as well as two levels of risk aversion is conducted. The results show that an explicit consideration of different market conditions in the modelling framework yields better portfolio performance as well as lower portfolio risk compared to standard approaches. These findings hold true for all investigated optimization frameworks and risk-aversion levels.

Asset Correlations in Turbulent Markets and their Implications on Asset Management

Korrelation spielt in vielen Anwendungen der modernen Finanzmarkttheorie eine wichtige Rolle. Eine Vielzahl von Modellen verwendet numerische Ausdrucke fur dieses Konzept der Abhangigkeit als Input-Parameter. Angesichts der zentralen Bedeutung der Korrelation erscheint es jedoch uberraschend, dass der Stabilitat der Korrelationsstrukturen im Zeitverlauf und insbesondere in verschiedenen Marktphasen bislang relativ wenig Beachtung in Forschung und Praxis geschenkt worden ist. Diese Arbeit setzt an eben erwahnter Fragestellung an, indem die Stabilitat der Korrelationen zwischen internationalen Aktien-, Staatsanleihen- und Unternehmensanleihen-Indizes untersucht wird. Zu diesem Zweck werden die Renditen der verschiedenen Assets mit Hilfe eines Markov-Switching Modells (MSM) beschrieben. Dadurch wird die Abbildung zweier hinsichtlich ihrer Rendite-Risikostrukturen vollig verschiedener Marktumgebungen ermoglicht. Anschliesend werden ruhige und turbulente Marktphasen im Zeitraum von 1987 bis 2009 identifiziert sowie die Korrelationsstrukturen in diesen Phasen verglichen. Es stellt sich heraus, dass die Korrelationen zwischen den untersuchten Asset-Klassen keineswegs stabil sind, sondern sich vielmehr betrachtlich sowohl in Ruhe- und Turbulenzphasen als auch im Zeitverlauf unterscheiden. Abschliesend werden diese Erkenntnisse auf ihre praktische Relevanz im Portfoliooptimierungs-Kontext uberpruft. Hierfur werden als Optimierungsansatze der Mean-Variance- und der Mean-Conditional-Value-at-Risk-Ansatz sowie zwei Risikoaversionsniveaus betrachtet. Die Ergebnisse zeigen, dass die explizite Berucksichtigung zweier verschiedener Marktphasen sowohl eine bessere Portfolio-Performance als auch ein geringeres Portfoliorisiko ermoglicht. Dies gilt fur alle untersuchten Optimierungsansatze und Risikoaversionsniveaus.

Asset Correlations in Turbulent Markets

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Asset correlations in turbulent markets and the impact of different regimes on asset management

Asset Correlations in Turbulent Markets and their Implications on Asset Management

Asset Correlations in Turbulent Markets