Multiple Asset Classes Research Articles

Green Assets and Global Portfolio Tail Risk? A Stress-Testing exercise under multiple asset classes under distinct market phases

Despite a burgeoning literature in the sphere of cryptocurrencies and green assets, yet, as of date, the literature fares poorly in terms of a holistic assessment of all asset classes, let alone stress testing such global portfolio risk under various market conditions. Our paper fulfills such a gap in the literature. Findings reveal that, irrespective of bearish or bullish market phases, green assets should be incorporated to mute down portfolio tail risk. Robustness tests performed either via different distribution type or CVaR analysis do not materially alter the main findings of our paper. The confluence of two forces substantially boosts tail risk, namely, passive investment strategy coupled with a crypto-augmented base model. Overall, our paper advocates the inclusion of green assets not as a choice but as an obligation to portfolio managers in view of curtailing both VaR and CVaR risk levels, all geared towards hitting two birds with one stone-simultaneously buttressing a greener world while effectively mitigating global portfolio tail risk.

Liquidity spillovers between cryptocurrency and foreign exchange markets

Market liquidity spillover has been studied using fiat and digital currency (cryptocurrency), though separately. In today’s globally connected financial markets, there is interconnectedness among financial assets, as economic agents make decisions involving multiple asset classes. In this paper, we construct daily liquidity measures for five major cryptocurrencies and seven major fiat currencies and examine their interdependence in the time–frequency domain. We find that liquidity connectedness is event-driven and varies with time. Notably, there is an increase in liquidity spillover at the peak of the COVID-19 pandemic, reflecting heightened uncertainty and market instability. We also find that, at the peak of the pandemic, the total spillover index is stronger in the long run than the short run, highlighting a possible flight-to-liquidity during periods of stress. The Euro stands out as the principal net transmitter of liquidity shocks in the short run, while Ethereum plays this role in the long run. Further analysis highlights the role of some economic and financial variables in driving the dynamics of liquidity spillovers and reveals some heterogeneity in the identity of drivers between the short and long run. The results enrich the academic literature by providing evidence of liquidity spillovers between cryptocurrencies and foreign exchange markets during times of stress, which is insightful for traders, investors, and policymakers.

Investment Portfolio Management System: A Survey

Abstract: An investment portfolio management system is a highly sophisticated software application meticulously crafted to assist investors in the management of their investment portfolios. This innovative system provides investors with a centralized platform that empowers them to track their investments meticulously, closely monitor their performance, and judiciously make informed investment decisions. The system encompasses several advanced features such as portfolio analysis, risk management tools, asset allocation strategies, and performance reporting, that provide investors with a comprehensive overview of their portfolio's performance. Additionally, this cutting-edge platform offers investors the opportunity to diversify their portfolio by investing across multiple asset classes such as stocks, bonds, and mutual funds. This paper delves into the various techniques and methods employed to identify the optimal strategy to maximize gains from the investment. The fusion of algorithms and investments has revolutionized the investment landscape, enabling investors to obtain insightful data and make data-driven decisions. Several research studies have been conducted in the investment field, bolstered by machine learning models and algorithms, resulting in exceptional gains for investors.

Pricing weather derivatives in an uncertain environment

Abstract This article deals with the problem of finding a pricing formula for weather derivatives based on temperature dynamics through an uncertain differential equation. Weather-related derivatives are being employed more frequently in alternative risk portfolios with multiple asset classes. We first propose an uncertain process that uses data from the past to describe how the temperature has changed. Despite this, pricing these assets is difficult since it necessitates an incomplete market framework. The volatility is described by a truncated Fourier series, and we provide a novel technique for calculating this constant using Monte Carlo simulations. With this approach, the risk is assumed to have a fixed market price.

Asset holders’ consumption risk and tests of conditional CCAPM

We test the conditional consumption-CAPM using asset holders’ consumption and find that the time variation in the prices of asset holders’ consumption risk is procyclical. This puzzling time variation is at odds with the implication of existing consumption-based equilibrium asset pricing models. We show that our finding is a salient feature of the data observed in multiple asset classes (aggregate equity market, equity portfolios, bond portfolios, and commodities portfolios), using different measures of consumption (household survey data and high-frequency retail shopping data) and alternative empirical methodologies.

Trend Following Made Easy: How to Create a CTA from Scratch

CTAs have generated strong returns in 2022 and regained investors’ interest in improving portfolio diversification. However, given their nature of being simultaneously long and short multiple asset classes, and constantly changing their portfolios, they often are viewed as black-box strategies. We show that CTA performance can be approximately replicated top-down by using just four asset classes, as well as by creating a CTA bottom-up utilizing 59 indexes. Neither process is complicated and both increase transparency on the investment process and portfolio construction of CTAs, which may help investors become more comfortable with this investment strategy.

Allocating the tracking error for the multi-asset-class fund by reconciling bottom-up model with top-down model

In a portfolio with multiple asset classes, each having its own benchmark, an alpha-seeking manager must decide how much tracking error (TE) to incur in each asset class, subject to given TE constraints. This paper helps practitioners clarify how to formulate this TE-allocation problem from a top-down perspective. By reconciling with a conceptual bottom-up formulation, we discover that the validity of the top-down model relies crucially on how one specifies the correlation structure of the asset classes’ alphas. We propose a method for estimating this correlation structure that helps users of the top-down model avoid misallocation of TEs.

Multi‐objective optimization models for the renewal planning of multiple asset classes

AbstractManaging the aging infrastructure of water distribution systems presents a challenge for many utilities. With various asset types competing for limited dollars, designing an effective asset management program is a resource allocation problem. Mobilization of equipment and crew is a significant cost (typically 2%–10%) within any capital improvement program. Therefore, selecting projects that target multiple asset classes together can reduce mobilization and help utilities stretch their budgets further. This research presents a process for modeling the joint renewal planning of multiple asset classes. The problem is framed as a dual‐objective optimization, where the selection of project areas aims to maximize lead service line removal and water meter changeout together. A case study from a Midwest utility is presented, and empirical data suggests the dual‐objective approach effectively reduces duplicate interventions in the same regions. Equity considerations are also examined, where constraints are added to enforce system‐wide project selection. Results show that the sensitivity of the objective toward equity is dependent on the underlying spatial distribution of the target asset itself, where uneven spread of the target asset leads to greater negative impact on model performance.

Forecasting Long-Horizon Factor Volatility

This article investigates forecasts of long-term volatility for the fast-growing field of long–short factor strategies in an extensive in-sample and out-of-sample framework. The author follows previous work by empirically comparing various forecast configurations to provide guidance for academics and practitioners on how to form accurate predictions of future volatility for various established factors. The set spans 21 factor return time series over multiple asset classes, factor styles, and a long historical data period. Both in-sample and out-of-sample results suggest monotonically increasing forecast accuracy for longer historical lookback periods, longer forecasting windows, and more-sophisticated models (considering short-term volatility clustering and external predictors motivated by the asset-pricing literature), while the findings appear less pronounced in a real-time setting than observed in-sample. Moreover, investors engaging in carry-styled factor strategies and multifactor portfolios (rather than single factors) achieve more-reliable forecasts, on average, as confirmed by the out-of-sample analysis.

Momentum and the Cross-section of Stock Volatility

Recent literature shows that momentum strategies exhibit significant downside risks over certain periods, called “momentum crashes”. We find that high uncertainty of momentum strategy returns is sourced from the cross-sectional volatility of individual stocks. Stocks with high realised volatility over the formation period tend to lose momentum effect. We propose a new approach, generalised risk-adjusted momentum (GRJMOM), to mitigate the negative impact of high momentum-specific risks. GRJMOM is proven to be more profitable and less risky than existing momentum ranking approaches across multiple asset classes, including the UK stock, commodity, global equity index, and fixed income markets.

Can downside-risk measures help to explain the reluctance of households to invest in XTFs? An empirical study using the SHS-base

PurposeThe purpose of this paper is to investigate whether downside-risk measures help to explain why households largely refrain from investing in Exchange Traded Funds that replicate broad and internationally diversified market indices, so-called XTFs, although studies frequently recommend to do so.Design/methodology/approachThe paper analyzes whether evaluating risk in terms of downside-risk measures which reflect households' interpretation of risk closer than the standard deviation (SD) of returns, yields less risk-return-enhancements, and thus, fewer incentives for households to invest in XTFs. Household portfolios are compiled by combining stylized portfolio compositions that involve multiple asset classes and German households' security holdings. The data set covers the period from January 2014 to December 2016 and includes 47,388 securities.FindingsThe results indicate that none of the downside-risk measures can help to explain the reluctance of households to invest in XTFs. On the flip side, the results show that all stylized household portfolios can enhance the risk-return position from employing XTFs, regardless of the underlying risk measure. This supports the advice to invest in XTFs and extends it upon households that evaluate risk in terms of downside-risk.Originality/valueTo the best of the authors' knowledge, this study is the first to investigate risk-return-enhancements from XTFs while simultaneously considering various downside-risk measures and multiple asset classes of household portfolios.

Money Illusion and TIPS Demand

AbstractThe market demand for the Treasury Inflation‐Protected Securities (TIPS) is rather small. This is puzzling, as we show that an agent, who derives utility from real wealth and dynamically invests into multiple asset classes over a 30‐year horizon, incurs a certainty equivalent loss of 1.6% per annum from not investing in inflation‐indexed bonds. However, if the investor suffers from money illusion, the perceived loss is only 0.5% per annum. Furthermore, the perceived loss is totally negligible for an unsophisticated money‐illusioned investor ignoring the time variation of risk premia. Money illusion causes significant portfolio shifts from inflation‐indexed toward nominal bonds, with little effects on equity allocations, contributing to the low market demand for TIPS.

A comparison of minimum variance and maximum Sharpe ratio portfolios for mainstream investors

PurposeMotivated by the recent theoretical rehabilitation of mean-variance analysis, the authors revisit the question of whether minimum variance (MinVar) or maximum Sharpe ratio (MaxSR) investment weights are preferable in practical portfolio formation.Design/methodology/approachThe authors answer this question with a focus on mainstream investors which can be modeled by a preference for simple portfolio optimization techniques, a tendency to cling to past asset characteristics and a strong interest in index products. Specifically, in a rolling-window approach, the study compares the out-of-sample performance of MinVar and MaxSR portfolios in two asset universes covering multiple asset classes (via investable indices and their subindices) and for two popular input estimation methods (full covariance and single-index model).FindingsThe authors find that, regardless of the setting, there is no statistically significant difference between MinVar and MaxSR portfolio performance. Thus, the choice of approach does not matter for mainstream investors. In addition, the analysis reveals that, contrary to previous research, using a single-index model does not necessarily improve out-of-sample Sharpe ratios.Originality/valueThe study is the first to provide an in-depth comparison of MinVar and MaxSR returns which considers (1) multiple asset classes, (2) a single-index model and (3) state-of-the-art bootstrap performance tests.

Tail-GAN: Nonparametric Scenario Generation for Tail Risk Estimation

The estimation of loss distributions for dynamic portfolios requires the simulation of scenarios representing realistic joint dynamics of their components, with particular importance devoted to the simulation of tail risk scenarios. Commonly used parametric models have been successful in applications involving a small number of assets, but may not be scalable to large or heterogeneous portfolios involving multiple asset classes. We propose a novel data-driven approach for the simulation of realistic multi-asset scenarios with a particular focus on the accurate estimation of tail risk for a given class of static and dynamic portfolios selected by the user. By exploiting the joint elicitability property of Value-at-Risk (VaR) and Expected Shortfall (ES), we design a Generative Adversarial Network (GAN) architecture capable of learning to simulate price scenarios that preserve tail risk features for these benchmark trading strategies, leading to consistent estimators for their Value-at-Risk and Expected Shortfall. We demonstrate the accuracy and scalability of our method via extensive simulation experiments using synthetic and market data. Our results show that, in contrast to other data-driven scenario generators, our proposed scenario simulation method correctly captures tail risk for both static and dynamic portfolios.

Using Asymmetric Dominance to Resolve Toxic Debt: Combining Online and Field Experiments

This study employs a behavioral technique known as asymmetric dominance used to nudge defaulted borrowers toward a repayment option that is in their best interest (lowest implied APR) while at the same time encouraging greater repayment, resulting in a win-win for both lenders and borrowers. We demonstrate the efficacy of this approach through an online experiment and then through a field experiment of 1st- and 2nd-lien actual defaulted mortgage pools. We address the generalization of asymmetric dominance by documenting success across multiple consumer asset classes – mortgages, auto loans, payday loans, student loans, health care debt and credit card debt. Finally, our results hold across 2-, 3-, and 4-digit monthly repayment amounts.

Thinking Beyond the Hiring and Firing of Asset Managers: A New Framework Truly Aligning Asset Owners With Asset Managers

Research has shown that the way asset owners deal with their asset managers often leads to adverse outcomes for themselves and their asset managers. This paper introduces an innovative framework which plays a key role in achieving a stronger alignment between an asset owner and their asset manager. The framework has been designed to offer a strong theoretical foundation to determine the asset owner’s optimal set of investment strategies across multiple asset classes. Based on this framework, the paper offers an asset owner a coherent set of alignment strategies which contribute to a better alignment with the asset owner’s investment objectives.

Empirical Evidences on the Interconnectedness between Sampling and Asset Returns’ Distributions

The aim of this work was to test how returns are distributed across multiple asset classes, markets and sampling frequency. We examine returns of swaps, equity and bond indices as well as the rescaling by their volatilities over different horizons (since inception to Q2-2020). Contrarily to some literature, we find that the realized distributions of logarithmic returns, scaled or not by the standard deviations, are skewed and that they may be better fitted by t-skew distributions. Our finding holds true across asset classes, maturity and developed and developing markets. This may explain why models based on dynamic conditional score (DCS) have superior performance when the underlying distribution belongs to the t-skew family. Finally, we show how sampling and distribution of returns are strictly connected. This is of great importance as, for example, extrapolating yearly scenarios from daily performances may prove not to be correct.

Thinking Beyond the Hiring and Firing of Asset Managers: a New Framework Truly Aligning Asset Owners with Asset Managers

Research has shown that the way asset owners deal with their asset managers often leads to adverse outcomes for themselves and their asset managers. This paper introduces an innovative framework which plays a key role in achieving a stronger alignment between an asset owner and their asset manager. The framework has been designed to offer a strong theoretical foundation to determine the asset owner’s optimal set of investment strategies across multiple asset classes. Based on this framework, the paper offers an asset owner a coherent set of alignment strategies which contribute to a better alignment with the asset owner’s investment objectives.

University Endowment Committees, Modern Portfolio Theory and Performance

University endowments with broad portfolio diversification have been correlated with performance, but committees’ decision-making process has received relatively little attention. This study is unique in postulating that the committee’s learning commitment and open-mindedness are significant contributors to a decision process that is based on the principles of Modern Portfolio Theory (or, simply, Portfolio Theory). The use of Portfolio Theory as a decision-making framework leads to greater portfolio diversification, which, in turn, leads to higher risk-adjusted returns. This study also demonstrates that greater committee expertise across multiple asset classes contributes to more diversified portfolios.

The Term Structure of the Rebalancing Premium

The optimal rebalance interval that maximizes the expected geometric return of passive strategies decreases in relation to increasing asset volatility, increasing dispersion in asset volatility, and increasing negative 1-month autocorrelation of asset returns. Conversely, the optimal rebalance interval increases in relation to increasing positive 1-month return autocorrelation and increasing dispersion in expected return of the underlying assets. Much of the literature on the rebalancing premium has focused on the effect of asset volatility. But empirically, the positive autocorrelation of asset returns and the dispersion in asset expected returns have a much larger impact on the rebalancing premium. Depending on the set of assets chosen, the optimal rebalance frequency can vary between 6 months for an equal-weighted portfolio of multiple asset classes and 10 years for an equal-weighted portfolio of regional indices. The large variation in optimal rebalance frequency challenges the notion of a universal rebalancing premium. <b>TOPICS:</b>Portfolio construction, wealth management, pension funds <b>Key Findings</b> • The empirical and theoretical term structure of the rebalancing premium is driven primarily by return dispersion in asset classes and the autocorrelation of return. • The optimal rebalancing frequency to maximize return varies between 6 months and at least 10 years, depending on the particular set of asset classes in the portfolio.