Factor investing and currency portfolio management

Currency-specific pricing factors are pervasive in international asset pricing. However, portfolio and risk management based on forex factors, instead of individual currencies, are rarely discussed. This paper tries to fill this gap by modelling dynamic correlations and non-normality among forex factors. By considering the four most popular forex factors: the dollar risk factor, the carry trade factor, the currency momentum factor, and the currency value factor, we find that a dynamic conditional correlation copula (DCC-copula) model with skewed-t kernel fits the joint distribution well. We show that, for risk-averse investors who focus on factor investing or employ the forex factors to resize the specific risk exposure, ignoring the tail dependence structure of forex factors brings significant costs.     

Have cryptocurrencies become an inflation hedge after the reopening of the U.S. economy?

In this paper, we study how the comovement between cryptocurrencies and the U.S. inflation expectation rates has changed during the post-reopening of the U.S. economy after the Covid-19 crisis. To do so, we develop a new concept of “exceedance co-kurtosis” which allows us to quantify asymmetry in strong comovement between each cryptocurrency and the inflation expectation rate. The key findings are as follows. First, we show the change in the co-kurtosis asymmetry for major cryptocurrencies: the downside co-kurtosis was higher than the upside co-kurtosis but it decreased after the reopening of the economy. Although the unconditional correlations between cryptocurrencies and the inflation expectation rates remain very low, our results indicate that the major cryptocurrencies become a slightly better inflation hedge after the reopening. Second and more interestingly, the results do not depend on whether a cryptocurrency has a cap on maximum supply or not. Therefore, treating the major cryptocurrencies as digital commodities could be misleading from the viewpoint of portfolio optimization.     

Learning risk preferences from investment portfolios using inverse optimization

The level of risk an investor can endure, known as risk-preference, is a subjective choice that is tightly related to psychology and behavioral science in decision making. This paper presents a novel approach of measuring risk preference from existing portfolios using inverse optimization on mean–variance portfolio allocation framework. Our approach allows the learner to continuously estimate real-time risk preferences using concurrent observed portfolios and market price data. We demonstrate our methods on robotic investment portfolios and real market data that consists of 20 years of asset pricing and 10 years of mutual fund portfolio holdings. Moreover, the quantified risk preference parameters are validated with two well-known risk measurements currently applied in the field. The proposed methods could lead to practical and fruitful innovations in automated/personalized portfolio management, such as Robo-advising, to augment financial advisors’ decision intelligence in a long-term investment horizon.     

Technical analysis,spread trading,and data snooping control

This paper utilizes a large universe of 18,410 technical trading rules (TTRs) and adopts a technique that controls for false discoveries to evaluate the performance of frequently traded spreads using daily data over 1990–2016. For the first time, the paper applies an excessive out-of-sample analysis in different subperiods across all TTRs examined. For commodity spreads, the evidence of significant predictability appears much stronger compared to equity and currency spreads. Out-of-sample performance of portfolios of significant rules typically exceeds transaction cost estimates and generates a Sharpe ratio of 3.67 in 2016. In general, we reject previous studies’ evidence of a uniformly monotonic downward trend in the selection of predictive TTRs over 1990–2016.     

Diversification benefits of NFTs for conventional asset investors: Evidence from CoVaR with higher moments and optimal hedge ratios

This study investigates the risk and returns on one of the newest digital asset classes instruments, non-fungible tokens (NFTs), by accounting for tail dependence of higher-order moments and portfolio characteristics. We used a wide range of asset classes, encompassing equites, fixed income securities, and commodities, and document the desirable hedging and portfolio attributes of NFTs by employing Conditional Value-at-Risk (CoVaR) and ∆CoVaRs with various copula functions. We found that NFTs exhibit beneficial investment and hedging attributes under all market conditions, including the Covid-19 pandemic. Our findings have important implications for investors, risk managers, and regulators.     

Dynamic spillover effects among green bond,renewable energy stocks and carbon markets during COVID-19 pandemic: Implications for hedging and investments strategies

This study has been inspired by the emergence of socially responsible investment practices in mainstream investment activity as it examines the transmission of return patterns between green bonds, carbon prices, and renewable energy stocks, using daily data spanning from 4th January 2015 to 22nd September 2020. In this study, our dataset comprises the price indices of S&P Green Bond, Solactive Global Solar, Solactive Global Wind, S&P Global Clean Energy and Carbon. We employ the TVP-VAR approach to investigate the return spillovers and connectedness, and various portfolio techniques including minimum variance portfolio, minimum correlation portfolio and the recently developed minimum connectedness portfolio to test portfolio performance. Additionally, a LASSO dynamic connectedness model is used for robustness purposes. The empirical results from the TVP-VAR indicate that the dynamic total connectedness across the assets is heterogeneous over time and economic event dependent. Moreover, our findings suggest that clean energy dominates all other markets and is seen to be the main net transmitter of shocks in the entire network with Green Bonds and Solactive Global Wind, emerging to be the major recipients of shocks in the system. Based on the hedging effectiveness, we show that bivariate and multivariate portfolios significantly reduce the risk of investing in a single asset except for Green Bonds. Finally, the minimum connectedness portfolio reaches the highest Sharpe ratio implying that information concerning the return transmission process is helpful for portfolio creation. The same pattern has been observed during the COVID-19 pandemic period.     

A novel two-stage method for well-diversified portfolio construction based on stock return prediction using machine learning

The performance of portfolio model can be improved by introducing stock prediction based on machine learning methods. However, the prediction error is inevitable, which may bring losses to investors. To limit the losses, a common strategy is diversification, which involves buying low-correlation stocks and spreading the funds across different assets. In this paper, a diversified portfolio selection method based on stock prediction is proposed, which includes two stages. To be specific, the purpose of the first stage is to select diversified stocks with high predicted returns, where the returns are predicted by machine learning methods, i.e. random forest (RF), support vector regression (SVR), long short-term memory networks (LSTM), extreme learning machine (ELM) and back propagation neural network (BPNN), and the diversification level is measured by Pearson correlation coefficient. In the second stage, the predictive results are incorporated into a modified mean–variance (MMV) model to determine the proportion of each asset. Using China Securities 100 Index component stocks as study sample, the empirical results demonstrate that the RF+MMV model achieves better results than similar counterparts and market index in terms of return and return–risk metrics.     

Re-evaluating portfolio diversification and design using cryptocurrencies: Are decentralized cryptocurrencies enough?

This paper investigates the portfolio diversification potential of a pool of cryptocurrencies classified based on their degree of leadership. We employ the mean-variance and the higher-order moments optimization approaches to evaluate the diversification potential of centralized and decentralized cryptocurrencies across multiple frameworks. While theoretical implications of the mean-variance and the higher-order moments optimization approaches are similar, our results suggest that the latter provides a more precise portfolio allocation strategy because it considers investor risk-aversion for each moment. Furthermore, we find that extending the pool of cryptocurrencies achieves marginal diversification benefits due to considerable co-movements among the cryptocurrencies. Moreover, we find that decentralized cryptocurrencies offer greater diversification potential than centralized cryptocurrencies, although centralized cryptocurrencies carry some diversification potential during alt-seasons. In order of their weights, Bitcoin, Chainlink, and Ethereum (all decentralized) offer the highest contribution to portfolio diversification across most portfolio frameworks, while Ethereum offers greater diversification benefits during the alt-seasons.     

Spillovers and directional predictability between international energy commodities and their implications for optimal portfolio and hedging

This study sheds a new light on the dependence and the directional predictability between eight major energy price returns, using the Cross-Quantilogram (CQ) and the Partial CQ (PCQ) analysis. The energy prices cover the time series for the U.S. natural gas and seven internationally traded crude oil types. The results reveal a significant directional predictability running from most of energy commodities returns to the OPEC basket and the very light Tapis crude oil returns. However, the quantile predictability in both directions is enabled only for the relations between the light Brent and the light WTI, and between the OPEC basket and the Malaysian Tapis. The time-varying predictability analysis reveals that there is a significant upper quantile dependence between these international energy commodities. Finally, we find that the TAPIS can be a good hedging vehicle for other energy markets. These findings may be instructive for both policymakers (in terms of financial stability) and market participants (in terms of performance).     

Economic policy uncertainty and household consumption: Evidence from Chinese households

We find that households reduce their consumption in response to higher economic policy uncertainty (EPU). Compared with lower income households, high income group is more severely affected which can be explained by the portfolio choice of illiquid asset and liquid asset. In addition, the uncertainty effect is more pronounced among older, wealthier, well-educated and urban households. The impact of EPU on household consumption is also persistent. Holding more liquid asset and commercial insurance represent important channels in mitigating the negative effect of EPU on household consumption.