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The purpose of this paper is to present an alternative approach to equity trading that is based on cointegration. If there are long-run equilibria among financial assets, a cointegration-based trading strategy can exploit profitable opportunities by capturing mean-reverting short-run deviations.

First, the author introduces an equity indexing technique to form cointegration tracking portfolios that are able to replicate an index effectively. The author later enhances this tracking methodology in order to construct more complex portfolio-trading strategies that can be approximately market neutral. The author monitors the performance of a wide range of trading strategies under different specifications, and conducts an in-depth sensitivity analysis of the factors that affect the optimal portfolio construction. Several statistical-arbitrage tests are also carried out in order to examine whether the profitability of the cointegration-based trading strategies could indicate a market inefficiency.

The author shows that under certain parameter specifications, an efficient tracking portfolio is able to produce similar patterns in terms of returns and volatility with the market. The author also finds that a successful long-short strategy of two cointegration portfolios can yield an annualized return of more than 8 percent, outperforming the benchmark and also demonstrating insignificant correlation with the market. Even though some cointegration-based pairs-trading strategies can consistently generate significant cumulative profits, yet they do not seem to converge to risk-less arbitrages, and thus the hypothesis of market efficiency cannot be rejected.

The primary contribution of the research lies within the detailed analysis of the factors that affect the tracking-portfolio performance, thus revealing the optimal conditions that can lead to enhanced returns. Results indicate that cointegration can provide the means to successfully reproducing the risk-return profile of a benchmark and to implementing market-neutral strategies with consistent profitability. By testing for statistical arbitrage, the author also provides new evidence regarding the connection between the profit accumulation of cointegration-based pairs-trading strategies and market efficiency.

The purpose of this paper is to establish an optimum mix of liquid, publicly traded assets that may be added to a real estate portfolio, such as those held by open-ended funds, to provide the liquidity required by institutional investors, such as UK defined contribution pension funds. This is with the objective of securing liquidity while not unduly compromising the risk-return characteristics of the underlying asset class. This paper considers the best mix of liquid assets at different thresholds for a liquid asset allocation, with the performance then evaluated against that of a direct real estate benchmark index.

The authors employ a mean-tracking error optimisation approach in determining the optimal combination of liquid assets that can be added to a real estate fund portfolio. The returns of the optimised portfolios are compared to the returns for portfolios that employ the use of either cash or listed real estate alone as a liquidity buffer. Multivariate generalised autoregressive models are used along with rolling correlations and tracking errors to gauge the effectiveness of the various portfolios in tracking the performance of the benchmark index.

The results indicate that applying formal optimisation techniques leads to a considerable improvement in the ability of the returns from blended real estate portfolios to track the underlying real estate market. This is the case at a number of different thresholds for the liquid asset allocation and in cases where a minimum return requirement is imposed.

The results suggest that real estate fund managers can realise the liquidity benefits of incorporating publicly traded assets into their portfolios without sacrificing the ability to deliver real estate-like returns. However, in order to do so, a wider range of liquid assets must be considered, not just cash.

Despite their importance in the real estate investment industry, comparatively few studies have examined the structure and operation of open-ended real estate funds. To the authors’ knowledge, this is the first study to analyse the optimal composition of liquid assets within blended or hybrid real estate portfolios.

The purpose of this paper is to develop a model of international capital market equilibrium where investors exhibit home‐country bias due to their desire to hedge real consumption.

This paper posits a two‐stage process of portfolio choice for the representative investor of a country. In the first step, the investor's benchmark portfolio is determined, whereas in the second step, his optimal portfolio is chosen. The latter portfolio maximizes the expected portfolio rate of return minus the risk tolerance weighted variance of tracking error. The market equilibrium implications of the portfolio optimality conditions are determine via aggregation across all investors and countries.

A revised security market line is derived that differs from the traditional security market line in terms of vertical intercept, slope, and beta coefficient. It is demonstrated that the derived model may be interpreted as a multi‐country generalization of the Chen‐Boness extension of the capital asset pricing model under uncertain inflation.

This paper presents an innovative application of Roll's tracking portfolio paradigm. Another novel feature is the derivation of the international capital market equilibrium implications of such portfolio choice behaviour.

The measurement of property portfolio performance is an important issue that, superficially, appears very straightforward. All that is required is an index of property market movements which can then be used as a reference point for comparing performance. Problems can arise, however, if the statistical characteristics of the index are different from the portfolio being analysed. This is not a trivial issue as the difference can be large enough to obscure the true performance of the portfolio and can lead to an inaccurate diagnosis of investment skill. Draws on recent research into index construction and examines some of the issues surrounding these problems. Discusses tracking errors and benchmarking issues.

Using a portfolio comprising liquid global stocks and bonds, this study aims to limit absolute risk to that of a standardised benchmark and determine whether this has a significant impact on expected return in both high volatility period (HV) and low volatility period (LV).

Using a traditional benchmark comprising 40% equity and 60% bonds, a constant tracking error (TE) frontier was constructed and implemented. Portfolio performance for different TE constraints and different economic periods (expansion and contraction) was explored.

Results indicate that during HV, replicating benchmark portfolio risk produces portfolios that outperform both the maximum return (MR) portfolio and the benchmark. MR portfolios outperform those with the same risk as that of the benchmark in LV. The MR portfolio weights assets to obtain the highest return on the TE frontier. During HV, the benchmark replicated risk portfolio obtained a higher absolute risk value than that of the MR portfolio because of an inefficient benchmark. In HV, the benchmark replicated risk portfolio favoured intermediate maturity treasury bills.

There is a dearth of literature exploring the performance of active portfolios subject to TE constraints. This work addresses this gap and demonstrates, for the first time, the relative portfolio performance of several standard portfolio choices on the frontier.

In this paper, we analyze the hedging risk intrinsic in the auto call step down equity linked securities (ELS) based on two underlying indices including HSCEI, which are major products of the ELS market. And we also propose new hedging strategies based on Conditional Value at Risk (CVaR) using stocks portfolio and futures. Due to the non-symmetric bimodal distribution for return of ELS, which comes from the Knock-In (KI) property inherent in step down ELS structure, and inherent shortfall risk in the ELS structure, a local delta hedging strategy has a limit. In addition, hedging using futures are difficult because of 1) frequent roll-over related with HSCEI futures, 2) price difference between underlying index and futures and 3) lack of futures liquidity caused by excessive ELS issue based on HSCEI. As a way to manage these problems, this paper proposes new hedging strategies : First, construct stocks portfolio tracking index using method suggested by Rockafellar and Uryasev (2002), Alexander, Coleman and Li (2006). Second, do hedging by using this stocks portfolio and futures. This paper shows that 1) index-tracking stocks portfolio based on CVaR has a better performance and lower shortfall risk than index by comparing market ratio, information ratio and Sharpe Ratio, and 2) hedging using stocks portfolio is better than futures. As the policy proposals, if ETF, which tracks the underlying indices of ELS based on CVaR, is to be listed on the exchange (KRX), various kinds of product structures for mid-risk-mid-return structured products will be able to develop further, as well as offer more convenience with hedging.

In this paper we develop a new semi-parametric model for conditional correlations, which combines parametric univariate Generalized Auto Regressive Conditional Heteroskedasticity specifications for the individual conditional volatilities with nonparametric kernel regression for the conditional correlations. This approach not only avoids the proliferation of parameters as the number of assets becomes large, which typically happens in conventional multivariate conditional volatility models, but also the rigid structure imposed by more parsimonious models, such as the dynamic conditional correlation model. An empirical application to the 30 Dow Jones stocks demonstrates that the model is able to capture interesting asymmetries in correlations and that it is competitive with standard parametric models in terms of constructing minimum variance portfolios and minimum tracking error portfolios.

This paper looks at product development processes at companies as well as project “selection”.

The article analyses effective Portfolio Management and how companies aim for improved returns.

One of most effective ways for all companies to turn around poor product development performance is to focus on picking a “better” mix of projects; one that maximizes the benefits of the spend. This can be achieved through Portfolio Management. One company that has seen major improvements from the introduction of a more effective portfolio management approach is Chevron.

The new Portfolio Management process tracks portfolio and project value. It includes activities to understand the upper and lower boundaries of a project's value, which implies an understanding of the risks that could lead to different values.

The purpose of this paper is to provide a detailed framework of goals, processes and solutions that can serve as a starting point for instructors in designing their own student-managed investment fund (SMIF) course experience that is relevant for all undergraduate business majors.

The design is suitable for a wide audience without prior equity investment expertise, lead to equity portfolio management competency and concentrate heavily on the understanding of the elements of a competitive business model. One noteworthy aspect of the proposed pedagogy is that it does not require a text, uses only real-world resources and is flexible in its execution.

The proposed pedagogy has achieved long-term success by consistently exceeding performance expectations.

According to the extant literature, many SMIFs are restricted to only a few students, develop skills unevenly across class participants, or are not formally organized or executed. There is a lack of in-depth and specific resources available in the extant literature to assist course designers in an SMIF design and execution. This manuscript fills this void by providing a detailed framework of goals, processes and solutions that can serve as a starting point for instructors in designing their own SMIF course experience.

The purpose of this study is to understand the tracking errors of leveraged exchange-traded funds (LETFs) on gold and demonstrate improved tracking performance by dynamic portfolios of gold futures.

The author formulates and solves a constrained quadratic minimization problem to construct static replicating portfolios of both leveraged and unleveraged benchmarks in gold; a dynamic constant leveraged portfolio using gold futures is used to track the path of the leveraged gold benchmark.

The results suggest that market-traded LETFs do not track a leveraged position in gold effectively over a long horizon, and the dynamic leveraged futures portfolio achieves lower tracking errors over multiple years.

The research informs us that investors should consider alternative portfolios with gold futures, rather than holding a leveraged gold exchange-traded funds to achieve a desired leveraged exposure in spot gold.

The main contribution of the study is the use of gold futures to dynamically replicate a gold benchmark with any given leverage ratio and the detailed comparison of the tracking performance of LETFs versus optimal static and dynamic futures portfolios.