Hedging Housing Risk in London

This paper investigates the benefits of allowing households to compensate the portfolio distortion due to their housing consumption through investments in housing price derivatives. Focusing on the London market, we show that a major loss from over-investment in housing is that households are forced to hold a very risky portfolio. However, the strong performance of the London housing market means that little is lost in terms of expected returns. Even households with limited wealth are better off owning their home rather than renting and investing in financial assets, as long as they are willing to face the financial risk involved. In this context, access to housing price derivatives would benefit most poor homeowners looking to limit their risk exposure. It would also benefit wealthier investors looking for the high returns provided by housing investments without the costs of direct ownership of properties. Comparisons with French, Swedish and U.S. data provide a broader perspective on our findings.     

Price Linkages in Asian Equity Markets: Evidence Bordering the Asian Economic, Currency and Financial Crises

This paper examines price linkages among Asian equity markets in the period surrounding the recent Asian economic, financial and currency crises. Three developed markets (Hong Kong, Japan and Singapore) and six emerging markets (Indonesia, Korea, Malaysia, the Philippines, Taiwan and Thailand) are included in the analysis. Multivariate cointegration and level VAR procedures are conducted to examine causal relationships among these markets. The results indicate that there is a stationary relationship and significant causal linkages between the Asian equity markets. Nevertheless, lower causal relationships that exist between the developed and emerging equity markets suggest that opportunities for international portfolio diversification in Asian equity markets still exist.     

Investment Horizon and Composition of Optimal Portofolio: International Evidence

Little attention has been paid in the literature to the impact of different investment horizons on the portfolio compositiondespite its importance to portfolio managers. One exception isthe study by Gunthorpe and Levy (1994) on the U.S. stock market.Our paper extends the same study to the stock markets of Japan,Hong Kong and Korea. Using 40 individual stocks in each market,our results support those of Gunthorpe and Levy (1994) in thatthe composition of an optimal portfolio depends heavily on theinvestment horizon. When the investment horizon lengthens, theproportion of defensive stocks becomes larger while that ofaggressive stocks becomes smaller.     

Pricing and hedging power options

In this paper we study the pricing and hedging of options whose payoff is a polynomial function of the underlying price at expiration; so-called ‘power options’. Working in the well-known Black and Scholes (1973) framework we derive closed-form formulas for the prices of general power calls and puts. Parabola options are studied as a special case. Power options can be hedged by statically combining ordinary options in such a way that their payoffs form a piecewise linear function which approximates the power option's payoff. Traditional delta hedging may subsequently be used to reduce any residual risk.     

Real estate return correlations: Real-world limitations on relationships inferred from NCREIF data

Correlation estimates for returns between individual properties are subject to large inherent uncertainties due to limits on the amount of data that is likely to be available for the foreseeable future. After allowance for correlation sampling error, it is impossible to distinguish on an ex ante basis between the risk-reduction capabilities of mean-variance portfolio selection models and naive diversification without regard to property type or geographical location. The naive portfolio diversification strategies of typical institutional real estate portfolio managers are rational responses to limitations on the informational content of statistical analyses of historical real estate data.     

Scientific methods in finance

Computer software can easily produce many financial models with technical skill, but investigators need to interpret and adapt computer output. Such analysis requires a deep knowledge of scientific fundamentals. We argue that an evidential perspective is more appropriate than a decision framework for model selection. We discuss four necessary conditions for any scientific investigation: observables, interpretables, replicables, and robustness in the context of examples familiar to finance professionals. Carefully planning the design of an experiment is the best way to address many econometric maladies. Furthermore, we emphasize the need for more sensitivity testing and for independent replication of empirical results within the finance profession.     

Exploring Portfolio Diversification Opportunities Through Venture Capital Financing: Evidence from MGARCH-DCC,Markov Switching,and Wavelet Approaches

Islamic financial institutions are being pressurized by critics to offer profit and loss sharing (PLS) financing, such as venture capital (VC) financing, for the purpose of entrepreneurial development aligned to the principle of equity risk sharing. Our study aims to link PLS investments with portfolio optimization opportunities for the Islamic asset managers. Using portfolio analysis with dynamic conditional correlation, Markov switching, and maximal overlap discrete wavelet transformation, our findings tend to indicate that there is indeed a portfolio optimization opportunity in investment universe for the fund managers who invested in PLS investments in the context of VC asset class over the long run.     

Learning minimum variance discrete hedging directly from the market

Option hedging is a critical risk management problem in finance. In the Black–Scholes model, it has been recognized that computing a hedging position from the sensitivity of the calibrated model option value function is inadequate in minimizing variance of the option hedge risk, as it fails to capture the model parameter dependence on the underlying price (see e.g. Coleman et al., J. Risk, 2001, 5(6), 63–89; Hull and White, J. Bank. Finance, 2017, 82, 180–190). In this paper, we demonstrate that this issue can exist generally when determining hedging position from the sensitivity of the option function, either calibrated from a parametric model from current option prices or estimated nonparametricaly from historical option prices. Consequently, the sensitivity of the estimated model option function typically does not minimize variance of the hedge risk, even instantaneously. We propose a data-driven approach to directly learn a hedging function from the market data by minimizing variance of the local hedge risk. Using the S&P 500 index daily option data for more than a decade ending in August 2015, we show that the proposed method outperforms the parametric minimum variance hedging method proposed in Hull and White [J. Bank. Finance, 2017, 82, 180–190], as well as minimum variance hedging corrective techniques based on stochastic volatility or local volatility models. Furthermore, we show that the proposed approach achieves significant gain over the implied BS delta hedging for weekly and monthly hedging.     

领导力在项目组合管理中的影响研究

项目组合是连接战略和项目的桥梁。区别于以往有关项目管理中领导力的研究,针对高管和项目经理两种层次的领导角色,界定了领导力的内涵,从战略视角出发构建了领导力对项目组合绩效的影响作用模型,并运用偏最小二乘法和实证方法对理论模型进行检验。研究结果表明,高管及项目经理的领导力均对项目绩效、组合绩效有积极的影响,但项目绩效对组合绩效的作用并不显著。结论强化了领导力在项目组合管理中的关键作用,为研究组合绩效的提升机制提供了新的思路和方法。     

Predicting uncertainty: the impact of risk measurement on value of real estate portfolios

This paper presents a theoretical framework for an assessment and valuation of real estate assets and funds, based on modern stochastic discounted cash flow (DCF) models, which accurately captures the nature of related risks. We show that an accurate risk-adjusted valuation is particularly difficult for real estate investments, due to practical limits to diversification and difficulties in approximating total risk with systematic risk. We develop a risk assessment framework that includes idiosyncratic risk but focuses on insolvency risk related to a specific cash flow profile. We also present a methodology of rating this risk, using forecasts and simulations. We conclude that simulation techniques are a valuable tool in property risk assessment. Further, we show that cost of capital and value of assets depend on diversification of specific risks, investors can achieve in their portfolios.