•We investigate the performance of behavioural investment strategies.•We propose a new behavioural investment objective function.•We compare the behavioural portfolio with rational and naïve ...portfolios.•We use both EUT-based and CPT-based evaluation criteria.
In this paper, we investigate the performance of behavioural portfolio strategies. We incorporate the short-term and long-term memory of the investor, thus recasting the behavioural portfolio choice process in a dynamic setting. We evaluate the out-of-sample performance of a behavioural investor in relation to both a naïve investor who invests in an equally weighted portfolio and a rational investor, who maximises expected mean-variance utility. We report a number of findings. First, from an expected utility perspective, neither the rational investor nor the CPT investor achieves a risk-adjusted return or certainty equivalent return that significantly outperforms that of the naïve investor. Second, from a CPT utility perspective, the behavioural investor outperforms both the rational and naïve investors. Third, the CPT investor typically displays highly concentrated, lottery-like asset allocations, low turnover and highly stable portfolio allocations. Fourth, the addition of the investor's memory into the portfolio choice process increases both diversification and turnover, leading to improved investment performance. Finally, by allocating more weight to positively skewed assets and increasing portfolio concentration, the probability weighting function has more impact than the utility function on the behavioural investor's performance. Our results are robust to the choice of reference return, estimation sample size, probability estimates, the probability weighting function and portfolio weight constraints.
In recent years, the surge in household indebtedness to historical heights has become a significant concern for developed economies. A similar trend has been witnessed in emerging market countries ...including Turkey. Our objective is to help further understand the dynamics of the recent growth in consumer loans and credit cards (CLCC) in Turkey. For this purpose, we investigate the long-term equilibrating relationships and short-term deviations from the equilibrium, and explore the determinants, directions, and strengths of causality relationships between CLCC and the selected macroeconomic variables, and analyze the dynamic interactions among the variables in the post-sample period by analyzing how CLCC responds to the shocks given to other macroeconomic variables and the contribution of each variable on the forecast variability of CLCC. We use monthly data for the period of January 2004-December 2013 of seven macroeconomic variables of money supply, interest rate, income, consumer confidence, inflation, stock market, and consumer goods imports. On empirical findings, we make suggestions about which policy tools should be used to influence, and if necessary to manage, the growth in CLCC.
Bitcoin replication using machine learning Harris, Richard D.F.; Mazibas, Murat; Rambaccussing, Dooruj
International review of financial analysis,
05/2024, Volume:
93
Journal Article
Peer reviewed
Open access
Cryptocurrencies are characterized by high volatility and low correlations with traditional asset classes, and present an intriguing investment opportunity. However, their inherent risks and ...regulatory uncertainties make direct investment challenging for many investors. This paper addresses this challenge by proposing a replication framework that employs machine learning to create synthetic portfolios that replicate the risk-adjusted return profile and diversification benefits of Bitcoin, by far the largest cryptocurrency by market share. We show that the synthetic portfolios offer a compelling alternative to direct investment in Bitcoin, delivering superior risk-adjusted returns net of trading costs while mitigating the risks that are associated with holding Bitcoin directly. Furthermore, the synthetic portfolios provide better diversification benefits and lower tail risk.
•We replicate the return distribution of Bitcoin using conventional assets.•We construct the replicating portfolios using machine learning.•We compare machine learning with conventional replication methods.•We evaluate the risk-adjusted return performance of the replicating portfolios.•We evaluate the diversification benefits of the replicating portfolios.
In this article, we develop one‐ and two‐component Markov regime‐switching conditional volatility models based on the intraday range and evaluate their performance in forecasting the daily volatility ...of the S&P 500 Index. We compare the performance of the models with that of several well‐established return‐ and range‐based volatility models, namely EWMA, GARCH, and FIGARCH models, the Markov regime‐switching GARCH model, the hybrid EWMA model, and the CARR model. We evaluate the in‐sample goodness of fit and out‐of‐sample forecast performance of the models using a comprehensive set of statistical and economic loss functions. To assess the statistical performance of the models, we use mean error metrics, directional predictive ability tests, forecast evaluation regressions, and pairwise and joint tests; and to appraise the economic performance of the models, we use value at risk coverage tests and risk management loss functions. We show that the proposed range‐based Markov switching conditional volatility models produce more accurate out‐of‐sample forecasts, contain more information about true volatility, and exhibit similar or better performance when used for the estimation of value at risk. Our results are robust to the choice of volatility proxy, estimation sample size, out‐of‐sample evaluation period, and alternative error distributions.
► We evaluate alternative optimization models for portfolios of hedge funds. ► The CVaR, CDaR and Omega models offer an improvement over the mean–variance model. ► We propose a semi-parametric ...approach to estimation of the optimal portfolio. ► The semi-parametric approach significantly outperforms existing approaches. ► Our results are robust to the target return, risk limit and estimation sample size.
In this article, we evaluate alternative optimization frameworks for constructing portfolios of hedge funds. We compare the standard mean–variance optimization model with models based on CVaR, CDaR and Omega, for both conservative and aggressive hedge fund investment strategies. In order to implement the CVaR, CDaR and Omega optimization models, we propose a semi-parametric methodology, which is based on extreme value theory, copula and Monte Carlo simulation. We compare the semi-parametric approach with the standard, non-parametric approach, used to compute CVaR, CDaR and Omega, and the benchmark parametric approach, based on both static and dynamic mean–variance optimization. We report two main findings. The first is that the CVaR, CDaR and Omega models offer a significant improvement in terms of risk-adjusted portfolio performance over the parametric mean–variance model. The second is that semi-parametric estimation of the CVaR, CDaR and Omega models offers a very substantial improvement over non-parametric estimation. Our results are robust to the choice of target return, risk limit and estimation sample size.
The research presented in this thesis addresses different aspects of dynamic portfolio construction and portfolio risk measurement. It brings the research on dynamic portfolio optimization, ...replicating portfolio construction, dynamic portfolio risk measurement and volatility forecast together. The overall aim of this research is threefold. First, it is aimed to examine the portfolio construction and risk measurement performance of a broad set of volatility forecast and portfolio optimization model. Second, in an effort to improve their forecast accuracy and portfolio construction performance, it is aimed to propose new models or new formulations to the available models. Third, in order to enhance the replication performance of hedge fund returns, it is aimed to introduce a replication approach that has the potential to be used in numerous applications, in investment management. In order to achieve these aims, Chapter 2 addresses risk measurement in dynamic portfolio construction. In this chapter, further evidence on the use of multivariate conditional volatility models in hedge fund risk measurement and portfolio allocation is provided by using monthly returns of hedge fund strategy indices for the period 1990 to 2009. Building on Giamouridis and Vrontos (2007), a broad set of multivariate GARCH models, as well as, the simpler exponentially weighted moving average (EWMA) estimator of RiskMetrics (1996) are considered. It is found that, while multivariate GARCH models provide some improvements in portfolio performance over static models, they are generally dominated by the EWMA model. In particular, in addition to providing a better risk-adjusted performance, the EWMA model leads to dynamic allocation strategies that have a substantially lower turnover and could therefore be expected to involve lower transaction costs. Moreover, it is shown that these results are robust across the low - volatility and high-volatility sub-periods. Chapter 3 addresses optimization in dynamic portfolio construction. In this chapter, the advantages of introducing alternative optimization frameworks over the mean-variance framework in constructing hedge fund portfolios for a fund of funds. Using monthly return data of hedge fund strategy indices for the period 1990 to 2011, the standard mean-variance approach is compared with approaches based on CVaR, CDaR and Omega, for both conservative and aggressive hedge fund investors. In order to estimate portfolio CVaR, CDaR and Omega, a semi-parametric approach is proposed, in which first the marginal density of each hedge fund index is modelled using extreme value theory and the joint density of hedge fund index returns is constructed using a copula-based approach. Then hedge fund returns from this joint density are simulated in order to compute CVaR, CDaR and Omega. The semi-parametric approach is compared with the standard, non-parametric approach, in which the quantiles of the marginal density of portfolio returns are estimated empirically and used to compute CVaR, CDaR and Omega. Two main findings are reported. The first is that CVaR-, CDaR- and Omega-based optimization offers a significant improvement in terms of risk-adjusted portfolio performance over mean-variance optimization. The second is that, for all three risk measures, semi-parametric estimation of the optimal portfolio offers a very significant improvement over non-parametric estimation. The results are robust to as the choice of target return and the estimation period. Chapter 4 searches for improvements in portfolio risk measurement by addressing volatility forecast. In this chapter, two new univariate Markov regime switching models based on intraday range are introduced. A regime switching conditional volatility model is combined with a robust measure of volatility based on intraday range, in a framework for volatility forecasting. This chapter proposes a one-factor and a two-factor model that combine useful properties of range, regime switching, nonlinear filtration, and GARCH frameworks. Any incremental improvement in the performance of volatility forecasting is searched for by employing regime switching in a conditional volatility setting with enhanced information content on true volatility. Weekly S&P500 index data for 1982-2010 is used. Models are evaluated by using a number of volatility proxies, which approximate true integrated volatility. Forecast performance of the proposed models is compared to renowned return-based and range-based models, namely EWMA of Riskmetrics, hybrid EWMA of Harris and Yilmaz (2009), GARCH of Bollerslev (1988), CARR of Chou (2005), FIGARCH of Baillie et al. (1996) and MRSGARCH of Klaassen (2002). It is found that the proposed models produce more accurate out of sample forecasts, contain more information about true volatility and exhibit similar or better performance when used for value at risk comparison. Chapter 5 searches for improvements in risk measurement for a better dynamic portfolio construction. This chapter proposes multivariate versions of one and two factor MRSACR models introduced in the fourth chapter. In these models, useful properties of regime switching models, nonlinear filtration and range-based estimator are combined with a multivariate setting, based on static and dynamic correlation estimates. In comparing the out-of-sample forecast performance of these models, eminent return and range-based volatility models are employed as benchmark models. A hedge fund portfolio construction is conducted in order to investigate the out-of-sample portfolio performance of the proposed models. Also, the out-of-sample performance of each model is tested by using a number of statistical tests. In particular, a broad range of statistical tests and loss functions are utilized in evaluating the forecast performance of the variance covariance matrix of each portfolio. It is found that, in terms statistical test results, proposed models offer significant improvements in forecasting true volatility process, and, in terms of risk and return criteria employed, proposed models perform better than benchmark models. Proposed models construct hedge fund portfolios with higher risk-adjusted returns, lower tail risks, offer superior risk-return tradeoffs and better active management ratios. However, in most cases these improvements come at the expense of higher portfolio turnover and rebalancing expenses. Chapter 6 addresses the dynamic portfolio construction for a better hedge fund return replication and proposes a new approach. In this chapter, a method for hedge fund replication is proposed that uses a factor-based model supplemented with a series of risk and return constraints that implicitly target all the moments of the hedge fund return distribution. The approach is used to replicate the monthly returns of ten broad hedge fund strategy indices, using long-only positions in ten equity, bond, foreign exchange, and commodity indices, all of which can be traded using liquid, investible instruments such as futures, options and exchange traded funds. In out-of-sample tests, proposed approach provides an improvement over the pure factor-based model, offering a closer match to both the return performance and risk characteristics of the hedge fund strategy indices.
The research presented in this thesis addresses different aspects of dynamic portfolio construction and portfolio risk measurement. It brings the research on dynamic portfolio optimization, ...replicating portfolio construction, dynamic portfolio risk measurement and volatility forecast together. The overall aim of this research is threefold. First, it is aimed to examine the portfolio construction and risk measurement performance of a broad set of volatility forecast and portfolio optimization model. Second, in an effort to improve their forecast accuracy and portfolio construction performance, it is aimed to propose new models or new formulations to the available models. Third, in order to enhance the replication performance of hedge fund returns, it is aimed to introduce a replication approach that has the potential to be used in numerous applications, in investment management.In order to achieve these aims, Chapter 2 addresses risk measurement in dynamic portfolio construction. In this chapter, further evidence on the use of multivariate conditional volatility models in hedge fund risk measurement and portfolio allocation is provided by using monthly returns of hedge fund strategy indices for the period 1990 to 2009. Building on Giamouridis and Vrontos (2007), a broad set of multivariate GARCH models, as well as, the simpler exponentially weighted moving average (EWMA) estimator of RiskMetrics (1996) are considered. It is found that, while multivariate GARCH models provide some improvements in portfolio performance over static models, they are generally dominated by the EWMA model. In particular, in addition to providing a better risk-adjusted performance, the EWMA model leads to dynamic allocation strategies that have a substantially lower turnover and could therefore be expected to involve lower transaction costs. Moreover, it is shown that these results are robust across the low - volatility and high-volatility sub-periods.Chapter 3 addresses optimization in dynamic portfolio construction. In this chapter, the advantages of introducing alternative optimization frameworks over the mean-variance framework in constructing hedge fund portfolios for a fund of funds. Using monthly return data of hedge fund strategy indices for the period 1990 to 2011, the standard mean-variance approach is compared with approaches based on CVaR, CDaR and Omega, for both conservative and aggressive hedge fund investors. In order to estimate portfolio CVaR, CDaR and Omega, a semi-parametric approach is proposed, in which first the marginal density of each hedge fund index is modelled using extreme value theory and the joint density of hedge fund index returns is constructed using a copulabased approach. Then hedge fund returns from this joint density are simulated in order to compute CVaR, CDaR and Omega. The semi-parametric approach is compared with the standard, non-parametric approach, in which the quantiles of the marginal density of portfolio returns are estimated empirically and used to compute CVaR, CDaR and Omega. Two main findings are reported. The first is that CVaR-, CDaR- and Omegabased optimization offers a significant improvement in terms of risk-adjusted portfolio performance over mean-variance optimization. The second is that, for all three risk measures, semi-parametric estimation of the optimal portfolio offers a very significant improvement over non-parametric estimation. The results are robust to as the choice of target return and the estimation period.
Dynamic hedge fund portfolio construction Harris, Richard D.F.; Mazibas, Murat
International review of financial analysis,
12/2010, Volume:
19, Issue:
5
Journal Article
Peer reviewed
Open access
In this paper, we provide further evidence on the use of multivariate conditional volatility models in hedge fund risk measurement and portfolio allocation, using monthly hedge fund index return data ...for the period 1990 to 2009. Building on Giamouridis and Vrontos (2007), we consider a broad set of multivariate GARCH models as well as the simpler exponentially weighted moving average (EWMA) estimator of RiskMetrics (1996). We find that while multivariate GARCH models provide some improvement in portfolio performance over static models, they are generally dominated by the EWMA model. In particular, in addition to providing better risk-adjusted performance, the EWMA model leads to dynamic allocation strategies that have substantially lower turnover and could therefore be expected to involve lower transaction costs. Moreover, we show that these results are robust across low-volatility and high-volatility sub-periods.
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