Returns Of Asset Classes Research Articles

Hybrid retirement strategy in South Africa

Background: Many retirees in South Africa face the challenge of either outliving their retirement savings or living below their means. Studies suggest a ‘safe’ withdrawal rate of between 4% and 5%, which is below the average fund size-weighted drawdown rate of approximately 6.66%.Aim: To provide a scientific basis for the success rate of a ‘hybrid’ retirement strategy, whereby a retiree invests a proportion of their savings in a life annuity and the remaining proportion in a living annuity, to increase the success rate for South African retirees.Setting: Historical asset class returns (equities, bonds and inflation) for South Africa were sourced for the period 1900–2020.Method: Bootstrap sampling of historical asset returns was employed to simulate 10 000 random scenarios to investigate the success rate of various compositions of the ‘hybrid’ retirement strategy.Results: The success rate of all ‘hybrid’ portfolio compositions is significantly greater than the success rate of a pure living annuity when the withdrawal rate is less than 8%.Conclusion: In a South African context, a ‘hybrid’ retirement portfolio increases the probability of success for retirees withdrawing less than 8% from their portfolio – which constitutes approximately 50% of the current annuatised population – and may increase the inheritance of a retiree’s heir.Contribution: Where other studies have focussed solely on the success rate of a living annuity, we have shown that a ‘hybrid’ retirement strategy increases a South African retiree’s likelihood of retiring successfully when the withdrawal rate is less than 8%, which is approximately 50% of the annuatised population.

A Study on Futures and Options in Derivative

Examining the processes that influence risk and return profiles for market players, this research probes the complicated world of futures and options markets. This study takes a holistic view of the topic, delving into market behavior studies, quantitative models, and historical data to provide a detailed picture of how financial market options and futures work. Part one of the research delves into the theory of futures and options, explaining how these derivative products work. It delves into the ways these monetary instruments help hedgers manage risk, investors make speculative bets, and arbitrageurs use them strategically. Futures and options are both distinguished from one another in the research, with the study elaborating on the specific features and consequences of each. In the future, the study will examine the volatility patterns and risk-adjusted returns of several asset classes' futures and options markets. The research intends to find patterns and outliers that have affected the risk-reward environment for market players by looking at case studies and past market occurrences. Option pricing models and their use in analyzing market expectations and implied volatility are further explored in the paper. The study investigates the effect of market mood on option pricing and uses empirical analysis to assess how well option pricing models capture market dynamics.

Time to Retire: The 4% Withdrawal Rule

The so-called “<i>4% rule</i>” is perhaps the single most commonly referenced retirement withdrawal approach. Most discussion of this rule centers on the appropriate percentage to use, 4%, 3%, or 5%. My objective is to suggest a superior retirement rule. Superior in the sense that it will deliver a higher standard of living during one’s retirement years. I start with the observation that past retirement withdrawal analysis suffers from two key deficiencies, that is, a reliance on past US stock and bond market returns (ex-post cherry picking the best performing market) and an assumption that asset class returns can be modeled with independent and identically distributed random variables, serving to mask their important time series properties. This article repairs these two deficiencies. A new retirement distribution rule is proposed. One that distributes/liquidates an ever-increasing proportion of the retiree’s then-current portfolio as they age, but subject to a minimum monthly distribution expressed in dollar terms. This approach provides simplicity, ease of execution, and transparency. It also results in a standard of living for the retiree as expressed by the median and average monthly distributions that are 101.3% and 174.2%, respectively, above those delivered by the best possible constant-dollar withdrawal rule. This pleasing result occurs for two primary reasons. First, this new rule realizes significant benefit from selling more/less shares when markets are high/low (which is the exact opposite of what transpires with all constant-dollar rules). Or to use other words, it delivers a measurably higher IRR despite utilizing an identical asset mix. Second, the new rule effectively consumes the retiree’s entire portfolio over the retirement life, leaving no unused residual balance (again, the exact opposite of all constant-dollar rules).

The dynamics of returns predictability in cryptocurrency markets

In this paper, we take a forecasting perspective and compare the information content of a set of market risk factors, cryptocurrency-specific predictors, and sentiment variables for the returns of cryptocurrencies vs traditional asset classes. To this aim, we rely on a flexible dynamic econometric model that not only features time-varying coefficients, but also allows for the entire forecasting model to change over time to capture the time variation in the exposures of major digital currencies to the predictive variables. Besides, we investigate whether the inclusion of cryptocurrencies in an already diversified portfolio leads to additional economic gains. The main empirical results suggest that cryptocurrencies are not systematically predicted by stock market factors, precious metal commodities or supply factors. On the contrary, they display a time-varying but significant exposure to investors' attention. In addition, also because of a lack of predictability compared to traditional asset classes, cryptocurrencies lead to realized expected utility gains for a power utility investor.

Practical Applications of The Role of Cryptocurrencies in Investor Portfolios

In <b><i>The Role of Cryptocurrencies in Investor Portfolios</i></b>, from the Spring 2022 issue of <b><i>The Journal of Alternative Investments</i></b>, <b>Megan Czasonis</b> of <b>State Street Associates</b>, <b>Mark Kritzman</b> of the <b>MIT Sloan School of Management</b> and <b>Windham Capital Management</b>, <b>Baykan Pamir</b> of <b>Alphachain Capital</b>, and <b>David Turkington</b> of <b>State Street Associates</b> examine correlations among asset class returns, with a focus on those of stocks and cryptocurrencies. They highlight that correlation is time-varying and dependent on the frequency of data. They find that cryptocurrencies produce less of a diversification benefit than other asset classes such as bonds or gold. Their analysis provides a template for investigating the role of cryptocurrencies in investor portfolios as the market develops. The approach provides more relevant information than a single, full-sample correlation number.

Global financial crisis versus COVID‐19: Evidence from sentiment analysis

This study examines the relationship between sentiment and the realized volatility of returns for different asset classes (stocks, bonds, foreign currency, and commodities). Specifically, we aim to answer two key questions: first, how does sentiment relate to volatility during crises (mainly during the global financial crisis [GFC] and the COVID‐19 pandemic)? Second, can sentiment be used to forecast volatility during crises? Using two nonparametric methods, mutual information and transfer entropy, we find that information sharing and transfer increased during the pandemic. We also find that sentiment information transfer to the volatility of assets differed between the GFC and the COVID‐19 crisis. Since sentiment can reduce uncertainty around the realized variance of assets, we investigate the forecasting ability of sentiment during crises. We find that sentiment has a greater predictive power on realized volatility during crises, with a differential impact on volatility depending on the asset class. Our findings carry important implications for hedging, risk management and building models to predict variance during crises.

Select Asset Classes and Short-Term Risk Return Trade Off

Investors and investments are two inseparable constituents of financial system. However, investors are hardly be able to make informed decisions regarding asset classes for their short-term investments since the returns and risks in the very short term up to say 30 days would be very unpredictable. Still to compensate against the inflation, investors need to invest even their short- term surplus funds in suitable asset classes. While in the end the benefit of average is inherently available to address risk-return trade-off, the case of short-term investments is different. Considering this challenge, this research attempts to illustrate the returns, risks, and the trade-off for select asset classes in short term periods. This article endeavors to briefly discuss the investment decisions by looking at the returns of various asset classes in a very short-term period of about 30 days. The objective of the study is To compute the returns of various asset classes viz., equity in Sensex, gold, USD, and Bitcoin. This study does not rule out the possibility that there could be aberrations during different short- term periods where due to unforeseen, irrational and specific reason driven movements, the return in any of the select asset classes could be exponentially much higher than expected or justified.Classification-JEL: G10

The Role of Cryptocurrencies in Investor Portfolios

The role of cryptocurrencies as a vehicle for speculation has been well established. However, it is less clear if cryptocurrencies can also serve to manage risk. The authors seek to determine the diversification potential of cryptocurrencies both for short and long horizons. For short horizons, they estimate correlations that consider the direction and magnitude of returns for relevant asset classes, rather than focusing on full-sample correlations, as is customary. For long horizons, they compute “single period correlations” that capture the extent to which cryptocurrencies move synchronously with, or drift apart from, other assets over an investor’s horizon. They also identify utility-maximizing allocations to cryptocurrencies directly from historical return samples that account for all features of the data as well as more nuanced preferences than are typically assumed.

TOP-DOWN PORTFOLIO IMPLICATIONS OF CLIMATE CHANGE

This paper reviews the significant progress in academic research on economic impact of climate change and explores the implications for expected returns and strategic portfolio allocation across major public asset classes. There have been numerous efforts to measure the environmental impact within a broader ESG framework with a focus on microeconomic and firm-level implications. In this paper, we assess the impact of climate change on long-term expected returns across asset classes from a top-down macroeconomic perspective. We use well-accepted climate risk scenarios to assess the potential impact of alternative climate scenarios on economic growth, inflation and asset returns for major asset classes. Finally, we design hypothetical portfolios given these top-down assumptions and explore portfolio allocation implications.

The Role of Cryptocurrencies in Investor Portfolios

The role of cryptocurrencies as a vehicle for speculation has been well established. However, it is less clear if cryptocurrencies can also serve to manage risk. The authors seek to determine the diversification potential of cryptocurrencies both for short and long horizons. For short horizons, they estimate correlations that consider the direction and magnitude of returns for relevant asset classes, rather than focus on full-sample correlations, as is customary. For long horizons, they compute “single period correlations,” which capture the extent to which cryptocurrencies move synchronously or drift apart from other assets over an investor’s horizon. They also identify utility-maximizing allocations to cryptocurrencies directly from historical return samples that account for all features of the data as well as more nuanced preferences than typically assumed.

Investing in Crises

We investigate asset returns around banking crises in 44 advanced and emerging economies from 1960 to 2018. In contrast to the view that buying assets during banking crises is a profitable long-run strategy, we find returns of equity and other asset classes generally underperform after banking crises. While prices are depressed during crises and partially recover after acute stress ends, consistent with theories of fire sales and intermediary-based asset pricing, we argue that investors do not fully anticipate the consequences of debt overhang, which result in lower long-run dividends. Our results on bank stock underperformance suggest that government-funded bank recapitalizations can often lead to substantial taxpayer losses.

Stocks, Bonds, Bills, and Inflation® (SBBI®): 2021 Summary Edition

The SBBI® Summary Edition will help investment professionals gain an understanding of major asset class returns, return calculations, and the long term impact of size, value/growth, and liquidity on returns.

Portfolio Choice with Path-Dependent Scenarios

Sophisticated investors rely on scenario analysis to select portfolios. We propose a new approach to scenario analysis that enables investors to consider sequential outcomes. We define scenarios not as average values but as paths for the economic variables. And we measure the likelihood of these paths on the basis of the statistical similarity of the paths to historical sequences. We also use a novel forecasting technique called “partial sample regression” to map economic outcomes onto asset class returns. This process allows investors to evaluate portfolios on the basis of the likelihood that the scenario will produce a certain pattern of returns over a specified investment horizon.

Portfolio Choice with Path-Dependent Preferences

Many sophisticated investors rely on scenario analysis to select a portfolio. These investors define prospective economic scenarios, assign probabilities to them, translate the scenarios into expected asset class returns, and select the portfolio with the highest expected return or expected utility, given all these inputs. With this approach, the investor only considers single period outcomes. The authors propose a new approach to scenario analysis that enables investors to consider sequential outcomes. They define prospective scenarios, not as average values of economic variables, but as paths for these variables. And they measure the likelihood that these paths will prevail in the future based on their statistical similarity to the historical sequences of these variables. The authors also employ a novel forecasting technique called partial sample regression to map economic outcomes onto asset class returns. This process allows investors to evaluate portfolios based on the likelihood they will produce a certain pattern of returns over a specified investment horizon.

Regime-based tactical allocation for equity factors and balanced portfolios

It is now an accepted fact that the majority of financial markets worldwide are neither normal nor constant, and South Africa is no exception. One idea that can be used to understand such markets and has been gaining popularity recently is that of regimes and regime-switching models. In this research, we consider whether regimes can add value to the asset allocation process. Four methods for regime identification—economic cycle variables, fundamental valuation metrics, technical market indicators and statistical regime-switching models—are discussed and tested on two asset universes—longonly South African equity factor returns and representative balanced portfolio asset class returns. We find several promising regime indicators and use these to create two regime-based tactical allocation frameworks. Out-of-sample testing on both the equity factor and balanced asset class data shows very promising results, with both regime-based tactical strategies outperforming their respective static benchmarks on an absolute return and risk-adjusted return basis. We also turn our attention to a potentially major recent development in the local fund management space; namely, the introduction of Capped Shareholder-Weighted indices as new benchmarks. We provide comparative analysis between the capped and uncapped Shareholder-Weighted indices in terms of sector weights, stock concentration, currency exposure and factor risk contributions.Keywords: Tactical asset allocation; equity factors; balanced portfolios; market regimes; regime-switching models; turbulence index; technical indicators

Scenario-Based Asset Allocation With Fat Tails And Non-Linear Correlation

This paper highlights the shortfalls of Modern Portfolio Theory (MPT). Amongst other flaws, MPT assumes that returns are normally distributed, that correlations are linear and risks are symmetrical. We propose a dynamic and flexible scenario-based approach to portfolio selection that incorporates an investor's economic forecast. Extreme Value Theory (EVT) is used to capture the skewness and kurtosis inherent in asset class returns and account for the volatility clustering and extreme co-movements across asset classes. The estimation consists of using an asymmetric GJR-GARCH model to extract filtered residuals for each asset class return. Subsequently, a marginal cumulative distribution function (CDF) of each asset class is constructed by using a Gaussian-kernel estimation for the interior, together with a generalised Pareto distribution (GPD) for the upper and lower tails. The distribution of exceedance method is applied to find residuals in the tails. A Student's t copula is then fitted to the data to induce correlation between the simulated residuals of each asset class. A Monte Carlo technique is applied to simulate standardised residuals, which represent a univariate stochastic process when viewed in isolation but maintain the correlation induced by the copula. The results are mean-CVaR optimised portfolios, which are derived based on an investor's forward-looking expectation.

Performance evaluation of the Turkish pension fund system

Purpose The purpose of this paper is to provide an in-depth performance evaluation of funds offered by the Turkish pension system. Design/methodology/approach This paper compares aggregate fund index returns with the corresponding asset class returns, estimates a factor model to decompose excess returns to factor exposures, i.e., β return and excess return originating from residual α and analyzes persistence of fund returns using migration tables and Fama–MacBeth regressions and tests for market timing ability. Findings Majority of pension funds are unable to generate excess returns. Majority of funds are unable to generate a positive α and fund returns are predominantly driven factor exposures. There is evidence for slight persistence in returns, mainly due to factor exposures and funds do not exhibit market timing ability. Originality/value In this paper, the authors perform an in-depth analysis of pension fund performance for the Turkish pension fund system. The authors identify weaknesses and strengths of the pension fund industry and provide policy recommendations for a better design of pension fund system.

The inflation hedging properties of South African and international asset classes

ABSTRACTWithin the period 1965 to 2015 all domestic asset class returns (except cash) are found to exhibit negative correlations with the contemporaneous inflation rate. Cash has hedging qualities due to Reserve Bank inflation targeting policy action but has a low real yield. Furthermore, Engle-Granger cointegration tests show that none of the asset class prices displays a long-term equilibrium relationship with the CPI. Using local growth assets as “inflation hedges” in a “CPI plus” mandate is more of an attempt to outperform inflation than to actually hedge against it.In contrast, it was found that “rand-hedge” asset classes could offer inflation protection. Offshore bond returns exhibited a significant positive contemporaneous relationship with inflation over a one- to three-year horizon and was the only asset class to do so. Looking at non-contemporaneous relationships, the prior 12-month rand returns on all foreign asset classes (and the local RESI) were found to be positively correlated with current inflation due to the return enhancement of rand weakening later feeding through into future imported inflation. Thus, rand hedges offer an “up-front” compensation for future inflation and, understood as such, can provide effective inflation protection for locally based investors.

Power law in tails of bourse volatility – evidence from India

Inverse cubic law has been an established Econophysics law. However, it has been only carried out on the distribution tails of the log returns of different asset classes (stocks, commodities, etc.). Financial Reynolds number, an Econophysics proxy for bourse volatility has been tested here with Hill estimator to find similar outcome. The Tail exponent or α ≈ 3, is found to be well outside the Levy regime (0 &amp;amp;lt; α &amp;amp;lt; 2). This confirms that asymptotic decay pattern for the cumulative distribution in fat tails following inverse cubic law. Hence, volatility like stock returns also follow inverse cubic law, thus stay way outside the Levy regime. This piece of work finds the volatility proxy (econophysical) to be following asymptotic decay with tail exponent or α ≈ 3, or, in simple terms, ‘inverse cubic law’. Risk (volatility proxy) and return (log returns) being two inseparable components of quantitative finance have been found to follow the similar law as well. Hence, inverse cubic law truly becomes universal in quantitative finance.

A comparative analysis of returns of various financial asset classes in South Africa: a triumph of bonds?

There is a popular view that equities always outperform other financial asset classes; especially bonds. This study investigates the performance of three common asset classes to determine whether or not this view is validated in South Africa. Conceptually, the popular view is irrational. If one class consistently and materially outperforms other asset classes, in the absence of other reasons, the other asset classes would disappear. Accordingly, rationally, in the long run and on a risk-adjusted basis, returns on all asset classes should conceptually more or less converge. The results from this study, which concentrates on equities, bonds and cash, show that in South Africa, even before adjusting for risk, there was no material difference between the returns of equities over long bonds over the 27-year period covered by this study (1986–2013). This is equally true for other shorter fixed periods with the end-date (28 February 2013) being the focal point. It is even more evident that bonds outperform equities when a system of rolling periods is used. On a nominal basis (before adjusting for risk), over any randomly selected rolling period, bonds outperform equities in six of the seven categories. This study does not take tax into consideration. After adjusting for risk using the Sharpe ratio or other risk measures, bonds outperformed equities.