An operational interpretation and existence of the Aumann-Serrano index of riskiness

In this note we provide an operational interpretation of the economic index of riskiness of Aumann and Serrano (2008) and discuss its existence in the case of non-finite gambles.     

Fancy Bitcoin and conventional financial assets: Measuring market integration based on connectedness networks

We investigate the relationship between Bitcoin and conventional financial assets from a perspective on the connectedness of asset networks. We adopt the method of measuring connectedness proposed by Diebold and Yilmaz (2009, 2012, and 2014) in a VAR system to study the dynamic interdependence between returns in Bitcoin, stocks, oil, and gold. We find that the connectedness between bitcoin and conventional assets is weak. The separation of positive and negative returns in the Bitcoin market shows the existence of an asymmetric pattern of the spillover effects between Bitcoin and conventional assets. A rolling window analysis finds that although Bitcoin prices experience a rising link to other financial assets, the magnitude is proven to be moderate. However, connectedness via negative returns is much stronger than via positive ones and exhibits a clearly increasing trend in recent periods. Our results in application are generally robust to other popular cryptocurrencies, such as ETH and Ripple. The findings presented in this paper have important implications for financial market participants, policymakers, and researchers in light of projected increases in the adoption of Bitcoin, as well as the rapid development of cryptocurrency.     

Economic indices of absolute and relative riskiness

Following Aumann and Serrano (J Polit Econ 116:810–836, 2008) who characterize by axioms an index of riskiness defined on absolute returns, we characterize a new index of riskiness defined on relative returns. Both indices are characterized by a similar principle of duality between risk and risk aversion, but while the index of absolute riskiness refers to absolute risk aversion, the index of relative riskiness refers to relative risk aversion. The similarities and differences between the two indices are studied.     

Quantile spillovers and dependence between Bitcoin,equities and strategic commodities

This paper has two aims. We first examine the dynamic spillovers between Bitcoin and 12 developed equities, gold, and crude oil for different market conditions using a Bayesian Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with daily spot prices. Our econometric approach enables us to capture the left and right tails as well as the shoulders of the return distribution corresponding to volatility spillovers under the bear, normal, and bull market states among these financial assets. We quantify and trace the dependence and directional predictability from Bitcoin to other assets using the sample cross-quantilogram. Our key findings offer convincing evidence of time variation in the level of volatility. Spillovers between Bitcoin and other financial assets intensify during extreme global market conditions. Secondly, results from the cross-quantilogram indicate strong dependence and positive directional predictability between Bitcoin and most equities and crude oil when market returns are bullish. However, during the bearish market period, there is negative dependence and predictability from Bitcoin to stocks in Finland, the Netherlands, the U.S.A, and the crude oil market only. This implies that Bitcoin can act as a hedge to stocks in Finland, the Netherlands, the U.S.A, and the crude oil market. However, insignificant dependence and directional predictability from Bitcoin to the remaining assets indicate that Bitcoin may act as a safe-haven to these assets during bearish markets. Our findings hold important implications for both international investors and portfolio managers who consider Bitcoin as part of their portfolio diversification and other investment strategies.     

Spillovers between Bitcoin and other assets during bear and bull markets

This article contributes to the embryonic literature on the relations between Bitcoin and conventional investments by studying return and volatility spillovers between this largest cryptocurrency and four asset classes (equities, stocks, commodities, currencies and bonds) in bear and bull market conditions. We conducted empirical analyses based on a smooth transition VAR GARCH-in-mean model covering daily data from 19 July 2010 to 31 October 2017. We found significant evidence that Bitcoin returns are related quite closely to those of most of the other assets studies, particularly commodities, and therefore, the Bitcoin market is not isolated completely. The significance and sign of the spillovers exhibited some differences in the two market conditions and in the direction of the spillovers, with greater evidence that Bitcoin receives more volatility than it transmits. Our findings have implications for investors and fund managers who are considering Bitcoin as part of their investment strategies and for policymakers concerned about the vulnerability that Bitcoin represents to the stability of the global financial system.     

Portfolios in the Ibex 35 before and after the Global Financial Crisis

In this article, we present an analysis of the effectiveness of various portfolio optimization strategies applied to the stocks included in the Spanish Ibex 35 index, for a period of 14 years, from 2001 until 2014. The period under study includes episodes of volatility and instability in financial markets, incorporating the Global Financial Crisis and the European Sovereign Debt Crisis. This implies a challenge in portfolio optimization strategies since the methodologies are restricted to the assignment of positive weights. We have taken for asset allocation the daily returns with an estimation window equal to 1 year and we hold portfolio assets for another year. This article attempts to influence the discussion over whether the naive diversification proves to be an effective strategy as opposed to portfolio optimization models. For that, we evaluate the out-of-sample performance of 15 strategies for asset allocation in the Ibex 35, before and after of the Global Financial Crisis. Our results suggest that a large number of strategies outperform to the 1/N rule and to the Ibex 35 index in terms of return, Sharpe ratio and lower VaR and CVaR. The mean-variance portfolio of Markowitz with short-sale constraints is the only strategy that renders a Sharpe ratio statistically different from Ibex 35 index in the 2001–2007 and 2008–2014 time periods.     

Combining the minimum-variance and equally-weighted portfolios: Can portfolio performance be improved?

It is documented in the literature that due to estimation errors, mean-variance efficient portfolios deliver no higher out-of-sample Sharpe ratios than does the naïve equally-weighted portfolio (EWP). This paper demonstrates how the out-of-sample performance of the minimum-variance portfolio (MVP) can be improved in the presence of estimation errors by combining the MVP and EWP. Our results indicate that an appropriate combination of the MVP and EWP can enhance Sharpe ratios under any scenarios considered, and can also reduce the portfolio risk if short-selling is allowed. However, the combination strategy is not able to generate a lower risk level than the MVP when a short-selling restriction is imposed. We find that the optimal combination coefficient depends on the factors that greatly impact estimation errors in the MVP, including sample size, estimation method, no-short-selling restriction, and length of the out-of-sample period under consideration.     

Bitcoin—A hype or digital gold? Global evidence

This study explores whether Bitcoin constitutes as a hedging instrument whilst seeking portfolio diversification opportunities among sustainable, conventional and Islamic asset classes since Bitcoin emerges as a distinct alternative investment and asset class across the world. We apply multivariate generalised autoregressive conditional heteroscedastic-dynamic conditional correlation and continuous wavelet transforms based on the recent data set ranging from August 18, 2011, to September 10, 2018. First, our findings show that Bitcoin returns are mean-reverting which implies that its value tends to come down to mean value in the long run and not completely crushed to zero irrespective of price changes suggesting Bitcoin as a sustainable asset class. Second, the time-invariant model shows that Bitcoin offers portfolio diversification opportunities with almost all equity indices, in particular, Dow Jones Islamic followed by FTSE 4 Good index. Finally, the time-variant analysis reconfirms that Bitcoin offers portfolio diversification benefits both in the short and long run. These findings carry meaningful policy considerations for fund managers and cross-country investors.     

Bitcoin for energy commodities before and after the December 2013 crash: diversifier,hedge or safe haven?

We study the relationship between Bitcoin and commodities by assessing the ability of Bitcoin to act as a diversifier, hedge, or safe haven against daily movements in commodities in general, and energy commodities in particular. We focus on energy commodities because energy, in the form of electricity, is an essential input in the Bitcoin production. For the entire period, results show that Bitcoin is a strong hedge and a safe-haven against movements in both commodity indices. We further examine whether that ability is also present for non-energy commodities and our analysis show insignificant results when energy commodities are excluded from the general commodity index. We also account for the December 2013 Bitcoin price crash and our results reveal that Bitcoin hedge and safe-haven properties against commodities and energy commodities are only present in the pre-crash period, whereas in the post-crash period Bitcoin is no more than a diversifier. In addition to uncovering the time-varying role of Bitcoin, we highlight the dissimilarity in the dynamic correlations between the extreme downward and extreme upward movements.     

消费习惯、异质偏好与动态资产定价:纯交换经济情形

本文用Chan和Kogan、Bask和Cuoco等的方法考虑纯交换经济下的定价问题,我们引进了两个投资者:一个具有外在性消费习惯;一个不具有消费习惯。我们重点考察消费习惯对投资者的最优消费规则的影响以及对资产价格的确定。此外,我们还考虑了对数效用函数下,消费习惯以差的形式出现的情形下的消费规则和定价问题。我们发现当两个投资者中一个具有消费习惯而另一个不具有该习惯时,消费习惯同时改变两个投资者的最优消费规则、消费动态和财富动态。此时的动态资产定价受外在性消费习惯的影响,即时Sharpe比为常数,并等于同质量经济下的即时Sharpe比。同时,如果考虑对数效用函数下消费习惯以差的形式出现,则即时Sharpe比是时变的,反周期的。     

动量思想与大宗商品的战术配置价值*——基于Black Litterman模型

本文讨论了大宗商品的战术资产配置价值。通过利用1973—2016年的季度数据，笔者提出了基于动量思想的Black Litterman配置模型。该配置模型表明，在传统股票债券组合中加入GSCI大宗商品指数会明显改善组合的回报。在适当的策略周期下，加入大宗商品指数还能够提高组合的夏普比。这表明，大宗商品指数的战术配置价值明显。与其他常见的配置模型相比，本文提出的基于动量思想的Black Litterman模型表现相对较好。上述发现在经过交易成本调整后仍然成立。     

Safe havens in the face of Presidential election uncertainty: A comparison between Bitcoin,oil and precious metals

Even though the empirical literature on safe haven properties of different assets with respect to financial risks is increasing, their abilities to safeguard against political risks has not been the subject of large empirical investigations. This paper uses an Empirical Mode Decomposition-based approach to look into the time-varying role of different assets (in particular, oil, precious metals and Bitcoin) as a safe haven against U.S. stocks in times of heightened uncertainty surrounding the outcome of the 2016 U.S. presidential election. Our results suggest that oil can act as an effective safe haven against political risk exposure; but such property varies over time. The abilities of gold and silver to provide positive returns during downturns have been also documented in the medium-and the long-term. Bitcoin also serves as a safe haven against U.S. stock losses but in the short-term. These findings provide useful and relevant information to investors to help ensure better asset allocation in an uncertain environment.     

Technological diversification, complementary assets, and performance

Most research on technological diversification or complementary assets has been carried out in isolation when assessing their effects on performance. In this study, we posit that technological diversification and performance are positively linked and that specialized complementary assets have a moderating effect on this relationship. This study also finds that different specialized complementary assets have distinctive moderating effects on the relationship between technological diversification and performance. We conclude that maintaining a coherent relationship between technological diversification and specialized complementary assets give firms generates competitive advantage.     

Commercial bank ownership and performance in China

This study examines the determinants of bank performance based on proxy variables that assess the quality of assets, profitability, liquidity and overall performance. Using a sample of 111 Chinese commercial banks over the period of 2000–2012, we find that foreign banks appear to have better asset quality and overall performance although lower profitability compared to domestic banks. In contrast, the state-owned banks tend to be more profitable and have better liquidity position compared with other domestic banks and foreign banks. At bank level, equity/liability ratio exerts significant influence on overall bank performance, while at the macroeconomic level, per capital GDP, GDP growth, inflation and unemployment rates appear to have a bearing on bank performance.     

Prediction of volatility based on realized-GARCH-kernel-type models: Evidence from China and the U.S.

We propose three Realized-GARCH-Kernel-type models which do not make the distribution assumptions on the return disturbance terms. We use this type of model to predict the return volatilities of the 50ETF in China and the S&P500 index in the U.S. The semiparametric kernel density estimator of our models, which captures the skewness, asymmetry and fat-tail of financial assets, performs well both statistically and economically. Our models have more predictive power than other eight comparable volatility models that need to pre-specify the distribution of the disturbance terms. Our results are robust to eight measures of realized volatility. Using option straddle strategies, we show that our models generate larger trading profits and greater Sharpe ratios than the other competing models.     

On the predictive power of ARJI volatility forecasts for Bitcoin

Motivated by the recent literature on cryptocurrency volatility dynamics, this paper adopts the ARJI, GARCH, EGARCH, and CGARCH models to explore their capabilities to make out-of-sample volatility forecasts for Bitcoin returns over a daily horizon from 2013 to 2018. The empirical results indicate that the ARJI jump model can cope with the extreme price movements of Bitcoin, showing comparatively superior in-sample goodness-of-fit, as well as out-of-sample predictive performance. However, due to the excessive volatility swings on the cryptocurrency market, the realized volatility of Bitcoin prices is only marginally explained by the GARCH genre of employed models.     

Nonparametric Sharpe Ratio

Sharpe ratio is a widely accepted tool for comparing the portfolio performance. In this paper we have proposed a nonparametric measure of the Sharpe rule. The statistical properties of this nonparametric measure and the standard Sharpe ratio are then developed under both normality and non-normality of observations. Further thebias corrected measures are given. An empirical application is also provided.

     

Adjusting for risk:: An improved Sharpe ratio

This paper proposes a new rule for risk adjustment and performance evaluation. This rule is a generalization of the well-known Sharpe ratio criterion, and under normal conditions enables a manager to correctly assess alternative risky investments. The rule is superior to existing rules such as the standard Sharpe rule and the RAROC, and can make a substantial difference in estimates of required returns.     

Benchmarking superannuation funds based on relative performance

In this article, we assess fund performance using data envelopment analysis (DEA). For each inefficient fund, DEA provides a set of role model funds whose best practices may be emulated for performance improvement. We find that the role models of most inefficient funds consist entirely of funds different from their own type. To overcome this situation, we suggest a multi-step DEA procedure. The procedure starts by categorizing funds on a hierarchical basis. We establish the hierarchy based on the frequency of efficient funds that belong to each fund type. Thereafter, a set of role model funds for each inefficient fund is found by pooling the funds in its own category and the funds that belongs to the categories at the lower levels in the hierarchy and applying DEA. This procedure is repeated by augmenting the pool with funds at the next higher level and so on until all the sampled funds are included. At each step, a set of role models is identified. An inefficient fund can thus reach the efficient frontier in stages. Statistical evidence suggests that membership and proportion of risky assets may have a negative association, and the fund size may have a positive association with fund performance.     

The day-of-the-week pattern of price clustering in Bitcoin

Following Urquhart (2017) who finds evidence of price clustering in Bitcoin, we answer the question of whether the documented price clustering in Bitcoin is driven by any given day-of-the-week. We find evidence that Bitcoin prices cluster around whole numbers more on Fridays and least on Mondays. We also show that Bitcoin price clustering around the top three most frequent two-digit decimals is primarily a Friday phenomenon.