In this paper, we focus on the 'Rachev ratio' proposed by Biglova et al. (2004) and propose a new smart beta index using it. This ratio has demonstrated its effectiveness as a performance measure; however, it is difficult to consider it to be a portfolio tool, particularly due to its non-convexity. Therefore, we propose a simple weighting method to construct a portfolio, transforming it into a new smart beta index. Moreover, we compare its performance with other smart beta indices in the global stock markets. The result of our empirical analysis, corroborate our contention that the proposed smart beta index using the Rachev ratio provides higher performance than other standard smart beta indices. Hence, we conclude that this smart beta is a new effective index that can be used in global stock markets.
This paper uses particle filter to estimate daily volatility in the Brazilian financial stocks market and obtain an optimal allocation of assets via Monte Carlo approach. Our volatility model outperforms the Kalman filter besides overcoming non-additivity and non-Gaussian disturbance pattern. The historical statistics use an optimist Black-Litterman priori view to systematise our analysis in a rolling window. Our proposed method has better out-of-sample metrics than Markowitz, Naive (equal assets weight) and Bovespa Index benchmark.
There has been tremendous growth in the assets managed through passive strategies both by institutional and private investors, especially over the last 20 years. This trend is not expected to abate anytime soon; on the contrary, it is projected to intensify, as investors have been increasingly rotating out of underperforming and higher cost active strategies into lower cost index-related ones. Besides cost, index-linked investing exhibits numerous benefits. There is, however, ample empirical evidence that shows that it is creating widespread price distortions that have considerable impact on individual security and market risk/return distributions. This 'dark side' of passive investing has sizeable repercussions for corporate investing and investor portfolio allocation decisions. In this environment, the presence of 'true' active managers is essential, as they could offset part of the distortions.
Recently, smart beta has become popular and its exchange-traded funds (ETFs) are now sold by asset management companies. Therefore, by using low-cost smart beta ETFs, we can easily conduct factor investing. We propose a market phase classification method based on market directions and cross-sectional volatility to explain the rates of return of smart beta indices and a conditional portfolio optimisation model to use the characteristics of these rates of return. Empirical analyses show that the proposed model achieves better performance than both the market index and a normal portfolio optimisation model used in Japanese and global markets.
Economists have long recognised that investors care more about downside risk than total variation, which includes upside gains. This paper develops a new technique, along the lines of Sharpe's (1990, 1992) asset-class model, to measure the performance of an actively managed portfolio with respect to downside risk. The new technique involves choosing a multiple asset-class benchmark with the highest possible correlation with the active strategy return subject to the constraint that the risks are the same. The risk-matched portfolio can be used to measure the performance of an active portfolio with respect to downside risk. Because, by construction the risk of the benchmark is the same as the active portfolio, it is a simple matter to compare the performance of the active to the benchmark using the Sortino ratio. To illustrate the new technique, we compare the performance of exchange traded funds that track S&P equal-weighted and value weighed sector indexes.
The downside risk (DSR) model for portfolio optimisation allows to overcome the drawbacks of the classical mean-variance model concerning the asymmetry of returns and the risk perception of investors. This optimisation model deals with a positive definite matrix that is endogenous with respect to the portfolio weights and hence yields to a non-standard optimisation problem. In this paper we develop a new method and an algorithm to solve this optimisation problem which typically yields to a smoother portfolio frontier. Our proposal is based on non-parametric estimation, using kernel methods of mean and median. An application to the French and Brazilian stock markets is given.
This paper investigates tactical investment strategies for investors to survive financial crises. Compared with the buy-and-hold strategy, the buy-and-sell strategy is much more effective in mitigating downside risk before, during, and after a crisis by restricting the left-tail volatility of portfolio returns through CVaR constraints. The paper also studies investors' optimal turnovers around a crisis under the buy-and-hold strategy. Considering investors' heterogeneous behaviours, we find the wealth-weighted average optimal turnover across all investors during a crisis is much higher than that before or after the crisis. This indicates investors who enter the market before a crisis may be better off by leaving their portfolios untouched during the market downturn. In addition, the downside risk control model can detect a market downturn earlier than the mean-variance model therefore it helps to 'spread out' the required asset adjustments over a longer horizon than the crisis period itself.
Representing continuously compounded returns in seven asset classes, by their four bilateral gamma parameter estimates, a multiclass classification support vector machine is trained, on a sample of less than one percent of the data, to predict the asset class from which the returns were obtained. The asset classes considered are equities, volatility, commodities, foreign exchange, credit and bond indices and returns of hedge funds. Linear classification is observed to perform poorly. The use of seven binary learners makes some improvement and twenty one, one on one, binary learners deliver a good classification algorithm, also performing well out of sample.
Investigating a much greater breadth of markets than ever before using daily data, we find no evidence of a value premium in either developed or emerging markets since the last studies were performed over a decade and a half ago, suggesting the previously documented anomaly has been exhaustively exploited. Also, using value-growth style investing preferences for the first time in examining the day-of-the-week effect, we find support for the previously documented anomaly in emerging markets only and primarily for value stocks. These results suggest that investment strategies based on the value premium no longer are effective and that day-of-the-week strategies should focus on emerging market value stocks only in the quest to generate risk adjusted profits.
The present study aims to highlight the beneficial virtues of diversification through internationalisation and distribution to multiple asset classes, traditional and alternative, for a Greek investor who exhibits a historically high local bias. A special reference is made to public pension funds that are 'forced' to invest, predominantly, in Greek assets, and, especially Greek Government securities, due to a very restrictive and binding legal framework. Applying modern portfolio theory to explore the potential benefits of holding international assets together with Greek assets, and monthly data covering the December 1998-March 2015 period, we confirm the benefits of international diversification. We find that if Greek investors allocate a substantial part of their risk budget in foreign markets, they can achieve significant reduction in portfolio risk and superior portfolio return. Local asset portfolios clearly represent a sub-optimal solution. The results hold for different stages of the economic cycle (recessions and/or expansions).
In this paper, we investigate the presence of multiple horizon causation from stock return volatility to the growth rates of industrial production in terms of a causal chain system, where monetary policy instruments and inflation operate as auxiliary processes. Multiple horizon non-causality is tested by implementing the test procedure proposed by Dufour et al. (2006) on data from four economies, namely USA, Germany, Japan and Italy. Our results reveal a large number of highly significant direct and indirect causality links of significant size running from stock return volatility to output growth at both short- and long-horizons in all four economies. A pseudo out-of-sample forecasting evaluation also shows how conditioning on such information yields better output growth predictions at different forecast periods.
We suggest the use of the wavelet-approach to determine optimal hedge ratios in order to adjust risk management positions to planning horizons. The wavelet-approach permits resolution of the signal in terms of the time scale of analysis. We analyse the wavelet correlations between several time series and find significant differences in short-term and long-term correlations between exchange rates and 'background risks'. At the same time, we do not find such a difference between spot and future rates for most exchange rates.
