A two-step risk parity strategy using markov chain driven asset ranking

Risk Parity is Not Short Volatility (Not That There's Anything Wrong with Short Volatility)

This chapter initially underlines the distinguishing features of the so called risk-based strategies for asset allocation in comparison with the Mean-Variance Analysis and proposes two criteria which highlight possible discrepancies among risk-based strategies. This is followed by a detailed illustration and interpretation of the theoretical concepts and tools of risk budgeting literature used to set up or analyse risk-based portfolios. We take into consideration the marginal risk, the total risk contribution and the percentage total risk contribution. The rest of the chapter offers an in depth discussion of the risk parity strategy, starting from the rudimental version, also known as naive risk parity or inverse volatility strategy, to the optimal one which can really build a portfolio such that risk contributions from the different asset classes to the portfolio overall risk are equalized. We explore the use of leverage combined with a risk parity strategy. The chapter concludes giving attention to the potential evolutions of the risk parity strategy. In particular, we look at points of attractiveness and shortcomings of a potential new version of risk parity strategy that considers “risk factors” rather than asset classes as the building blocks of a portfolio construction approach.

The global economic crisis, which resulted in devastating losses to traditional, capital-based asset allocation portfolios, spiked interest in risk-based portfolios. As a result, risk parity strategies have taken off in recent years. Champions of the increasingly popular risk parity strategy maintain that traditional strategies such as 60/40 typically end up unbalanced, with a higher allocation to riskier assets or equities, unlike risk parity strategies, which allocate portfolio risk across asset classes. But are the increasing ranks of so-called risk parity portfolios truly at risk parity? Because risk parity is open to different interpretations, it is difficult for investors to tell the difference between risk allocation approaches, research shows. <i><b>Are Risk Parity Managers at Risk Parity?</b></i>, published in the Fall 2013 issue of <i><b>The Journal of Portfolio Management</b></i>, aims to define risk parity and examines whether a sample of risk parity managers are truly adhering to the risk parity principle. “Risk parity since 2008 on the one hand has become an accepted investment strategy,” <b>Edward Qian</b>, Chief Investment Officer in the Multi-Asset Group at <b>PanAgora Asset Management</b> in Boston says. “As someone who christened the term, I thought there could be misinterpretation.” <b>TOPICS:</b>Portfolio management/multi-asset allocation, factors, risk premia, risk management

The risk parity investment model for asset allocation offers an alternative to the mean–variance framework. The fundamental idea is that the allocation to different asset classes should not be based on an optimization that targets a specific return with a minimal level of risk but, rather, should generate a portfolio in which the contribution to portfolio risk of each asset class is equal, regardless of its expected returns. In this article, the authors explain the fundamentals of the risk parity investment model and the variants in risk parity strategies due to the selection of the asset classes to be included in the portfolio, the choice of the risk metric, the portfolio risk target, how to obtain leverage, associated leverage, whether the selection of the specific investments within an asset class is made using an active or passive approach, and the tactical risk allocation strategy. In addition to describing the practical aspects of implementing risk parity strategies, the authors identify the various shortcomings of the model and some extensions of the basic risk parity model that attempt to address some of the issues identified by the model’s critics. TOPICS:Portfolio theory, portfolio construction, risk management, performance measurement Key Findings ▪ Unlike mean–variance optimization, a risk parity strategy allocates across asset classes such that each asset class contributes equally to portfolio risk, regardless of its expected returns. ▪ In practice, there are variants of the risk parity strategy due to choices made by the portfolio manager, such as selection of the asset classes, risk measure, targeted volatility, degree of leverage, asset selection using active or passive approach, and tactical risk allocation strategy. ▪ The performance of the risk parity strategy has varied, with critics of the strategy identifying theoretical and practical implementation issues.

This chapter examines the use and merits of hierarchical clustering techniques in the context of multi-asset multi-factor investing. In particular, it contrasts these techniques with several competing risk-based allocation paradigms, such as 1/N, minimum-variance, standard risk parity and diversified risk parity. The chapter introduces hierarchical risk parity (HRP) strategies based on the Pearson correlation coefficient and also introduces hierarchical clustering based on the lower tail dependence coefficient. The chapter provides an overview of traditional risk-based allocation strategies and outlines a framework to measure and manage portfolio diversification. It examines the performance of the introduced HRP strategies relative to the traditional alternatives. The chapter discusses Meucci's approach to managing diversification, which serves to construct a diversified risk parity strategy based on economic factors.

This study examines the performance and effectiveness of the Risk Parity (RP) strategy in strategic asset allocation specifically utilizing the inverse volatility (IV) approach. The study utilizes data from 13 developed countries, encompassing stocks and government data, to analyze the performance of RP portfolio. A comparative analysis is conducted between the RP portfolio, traditional 60/40 and equal-weighted portfolios across significant market crises, including the dot-com crisis, financial crisis, sovereign debt crisis, and COVID-19 pandemic. This study reveals that inverse volatility approach or a portfolio with a higher weight towards safer assets compared to riskier assets delivers superior risk-adjusted returns. It presents evidence that challenges the notion of the market or 60/40 portfolio as an efficient portfolio and suggests that an alternative portfolio such as RP may offer superior efficiency in terms of risk and return. Moreover, the study examines the role of leveraging in RP portfolios, highlighting how investors can align risk levels with benchmark portfolios while achieving higher risk-adjusted returns. The findings highlight the substantial impact of leveraging on RP portfolio performance, demonstrating their resilience and superiority even in challenging market conditions.

In this paper, a risk parity strategy based on portfolio kurtosis as reference measure is introduced. This strategy allocates the asset weights in a portfolio in a manner that allows an homogeneous distribution of responsibility for portfolio returns' huge dispersion, since portfolio kurtosis puts more weight on extreme outcomes than standard deviation does. Therefore, the goal of the strategy is not the minimization of kurtosis, but rather its ‘fair diversification’ among assets. An original closed-form expression for portfolio kurtosis is devised to set up the optimization problem for this type of risk parity strategy. The latter is then compared with the one based on standard deviation by using data from a global equity investment universe and implementing an out-of-sample analysis. The kurtosis-based risk parity strategy has interesting portfolio effects, with lights and shadows. It outperforms the traditional risk parity according to main risk-adjusted performance measures. In terms of asset allocation solutions, it provides more unbalanced and more erratic portfolio weights (albeit without excluding any component) in comparison to those pertaining the traditional risk parity strategy.

Since the great financial crisis of 2008, many studies have pointed out that even in the portfolio where the asset allocation is sufficiently diversified, it is still possible that risk allocation is well concentrated to a few assets. One approach to this problem is risk parity strategies which equalize the risk contribution of each asset. However, even if we equalize the risk contribution, risk sources are not necessarily diversified. In this paper, we propose non-hierarchical clustering-risk parity strategy which will equalize risk contribution from and within each cluster. In addition, in order to ensure robustness of clustering, we also propose x-means++ algorithm which combines k-means++ with x-means. Assuming assets with similar movement have common risk sources, our approach will construct a portfolio which equalizes risk sources. Empir-ical analysis using actual price data of various asset classes shows that our proposed method will outperform risk-parity strategies or hierarchical clustering risk parity strategies.

The Risk Parity Approach to Asset Allocation - Climbing the Wall of Worries?

A stopped clock is right twice a day. Similarly, any portfolio allocation is likely to be optimal at least at some point. Risk parity is no stopped clock. The authors derive a general result regarding when and why risk parity is Sharpe ratio optimal, even with negative-Sharpe-ratio assets. This derivation goes beyond the simple observation that risk parity is Sharpe ratio optimal when asset correlations and Sharpe ratios are identical. Based on the analytical result, the authors develop an indicator to describe when risk-parity strategies are likely to be more or less optimal. They also explain how negative-Sharpe-ratio assets can still be an important part of a portfolio—whether it is a risk-parity portfolio or not. Although risk parity and risk balancing in general do not require assumptions about returns, the authors provide guidance regarding how to infer returns that are consistent with the portfolios built from targeting risk. TOPICS:Statistical methods, portfolio theory, portfolio construction Key Findings • Conditions that are generally sufficient for risk-parity portfolios to be Sharpe ratio optimal are provided, beyond the special case of identical cross-asset correlations and Sharpe ratios. From this, an indicator of how likely risk parity is to be Sharpe ratio optimal can be derived. • Based on diversification properties of assets, the generally sufficient conditions for risk-parity portfolios to be Sharpe ratio optimal explain why even negative-return assets can be included in a diversified portfolio. • A method is provided to translate risk targets into a rank ordering of asset returns. With an additional set of assumptions, these rank orderings can be translated into returns for a coherent set of risk targets, risks, and returns.

Striving for maximum diversification we follow the 2009 work of Meucci in measuring and managing a multi-asset class portfolio. Under this paradigm the maximum diversification portfolio is equivalent to a risk parity strategy with respect to the uncorrelated risk sources embedded in the underlying portfolio assets. We characterize the mechanics and properties of this diversified risk parity strategy. Moreover, we explore the risk and diversification characteristics of traditional risk-based asset allocation techniques such as 1/N, minimum-variance or risk parity and show the diversified risk parity strategy to be very meaningful when benchmarked against these alternatives.

The authors investigate a regime-aware risk parity strategy designed to stabilize portfolio risk across various regimes. Their method dynamically adjusts an industry-standard multi-asset portfolio to the current macroeconomic and stock market environment. Specifically, they integrate macroeconomic data and stock market risk indicators, including the S&P 500 implied volatility term structure, into an estimation of the covariance matrix that drives the risk parity strategy. This integration aims to enhance the robustness of regime predictions. The empirical results demonstrate that the ability to differentiate risk and return profiles leads to more precise control of ex post portfolio risk across all regimes. It also improves out-of-sample portfolio performance when macroeconomic and market conditionalities are incorporated into the original risk parity portfolio. These enhancements are observed to be robust across various definitions of conditionality, time periods, and asset classes.

The authors present a machine learning approach to regime-based asset allocation. The framework consists of two primary components: (1) regime modeling and prediction and (2) identifying a regime-based strategy to enhance the performance of a risk parity portfolio. For the former, they apply supervised learning algorithms, including the random forest, based on a large macroeconomic database to estimate the probability of an upcoming recession or a stock market contraction. Out-of-sample tests show the reliability of these predictions, especially for recessions in the United States, over the period 1973 to 2020. The probability estimates are linked to a dynamic investment overlay strategy. The combined approach improves risk-adjusted returns by a substantial amount over nominal risk parity in two-asset and multi-asset test cases, even during rising interest rates in the late 1970s. <bold>TOPICS:</bold> <ext-link>Big data/machine learning</ext-link>, <ext-link>portfolio construction</ext-link>, <ext-link>performance measurement</ext-link> <bold>Key Findings</bold> <list><list-item> ▪ We examine a regime prediction problem with supervised learning approaches and implement regime-switching risk parity portfolios. </list-item><list-item> ▪ All recession periods after 1973 are captured by the random forest model, and stock market regime predictions lead to better portfolio performance. </list-item><list-item> ▪ Regime-switching models enhance risk parity portfolios, even during a rising interest rate period. Regime-based overlay strategies provide higher risk-adjusted returns in risk parity strategies. </list-item></list>

Diversifying Risk Parity

The asset allocation strategy is important to manage assets effectively. In recent years, the risk parity strategy has become attractive to academics and practitioners. The risk parity strategy determines the allocation for asset classes in order to equalize their contributions to overall portfolio risk. Roncalli and Weisang (2016) propose the use of “risk factors” instead of asset classes. This approach achieves the portfolio diversification based on the decomposition of portfolio risk into risk factor contribution. The factor-based risk parity approach can diversify across the true sources of risk whereas the asset-class-based approach may lead to solutions with hidden risk concentration. However, it has some shortcomings. In our paper, we propose a methodology of constructing the well-balanced portfolio by the mixture of asset-class-based and factor-based risk parity approaches. We also propose the method of determining the weight of two approaches using the diversification index. We can construct the portfolio dynamically controlled with the weight which is adjusted in response to market environment. We examine the characteristics of the model through the numerical tests with seven global financial indices and three factors. We find it gives the well-balanced portfolio between asset and factor diversifications. We also implement the backtest from 2005 to 2018, and the performances are measured on a USD basis. We find our method decreases standard deviation of return and downside risk, and it has a higher Sharpe ratio than other portfolio strategies. These results show our new method has practical advantages.