Risk Everywhere: Modeling and Managing Volatility

Risk Everywhere: Modeling and Managing Volatility

Cross-Asset Skew

10 - Dynamic risk analysis and risk model evaluation

PurposeThe purpose of this paper is to provide an analysis of the dependence structure between returns from real estate investment trusts (REITS) and a stock market index. Further, the aim is to illustrate how copula approaches can be applied to model the complex dependence structure between the assets and for risk measurement of a portfolio containing investments in REIT and equity indices.Design/methodology/approachThe usually suggested multivariate normal or variance‐ covariance approach is applied, as well as various copula models in order to investigate the dependence structure between returns of Australian REITS and the Australian stock market. Different models including the Gaussian, Studentt, Clayton and Gumbel copula are estimated and goodness‐of‐fit tests are conducted. For the return series, both the Gaussian and a non‐parametric estimate of the distribution is applied. A risk analysis is provided based on Monte Carlo simulations for the different models. The value‐at‐risk measure is also applied for quantification of the risks for a portfolio combining investments in real estate and stock markets.FindingsThe findings suggest that the multivariate normal model is not appropriate to measure the complex dependence structure between the returns of the two asset classes. Instead, a model using non‐parametric estimates for the return series in combination with a Studenttcopula is clearly more suitable. It further illustrates that the usually applied variance‐covariance approach leads to a significant underestimation of the actual risk for a portfolio consisting of investments in REITS and equity indices. The nature of risk is better captured by the suggested copula models.Originality/valueTo the authors', knowledge, this is one of the first studies to apply and test different copula models in real estate markets. Results help international investors and portfolio managers to deepen their understanding of the dependence structure between returns from real estate and equity markets. Additionally, the results should be helpful for implementation of a more adequate risk management for portfolios containing investments in both REITS and equity indices.

This study examines the impact of farmland investments on the risk-efficiency of mixed asset portfolios. Traditional asset classes considered available for investment include various equity market indices, commercial REITs, corporate bonds of investment- and sub investment grade, government bonds and treasury bills, corporate bonds, ex-U.S. equity indices, short term interest rate indexes, and commodity investments. Unlevered farmland returns were constructed at the state level as the sum of cash rent and capital gains less property taxes as a fraction of asset values. In addition, a unique, high quality data set comprised of the returns to all managed farmland properties in the NCREIF Farmland Index was also considered. A traditional optimal E-V frontier is first identified considering optimal financial-asset only portfolios in the absence of the farmland asset class. Results show that, relative to financial-asset only portfolios, the inclusion of farmland significantly improves the risk-efficiency of the optimal E-V frontier. To address potential aggregation and smoothing biases, farmland returns are systematically penalized through reduced returns and increased variability. While the mix and shares of farmland investments under these restrictions are reduced, the fundamental result remains that farmland investments significantly improve the risk-efficiency of mixed-asset portfolios.

Short-term momentum (almost) everywhere

Bitcoin an Asset Class Market Risk Assessment for Indian Investors

PurposeThe purpose of this paper is to examine the use of financial information and valuation methods among private equity funds in Europe and India. The authors analyze differences in the choice of valuation methods and how the use of financial information differs among funds in the UK, Pan Europe and India.Design/methodology/approachA survey approach was utilized in collecting proprietary data from European and Indian private equity funds. The data were classified according to fund type, country grouping, size, risk profile, labor cost and industry structure and analyzed using MANOVA and ANOVA.FindingsThe results show that the use of valuation models is relatively homogeneous across countries and that the use of financial information appears to be driven to a large extent by fund type and fund focus. The use of audited financial statements appears to increase as firms mature. Significant differences were found in standard financial adjustments between the two fund types and between the country groupings. Results based on labor cost are weakly significant whereas industry structure does not appear to have an impact on how fund managers evaluate investments.Research limitations/implicationsThe results indicate that fund managers adapt their decision‐making behavior according to investment type and risk. The authors argue that understanding asymmetrical and structural issues may potentially improve investment decision‐making processes. The main conclusion for researchers is that buy‐out and venture capital funds should not be combined as one asset class. Since a survey approach was used, the study is subject to the belief that fund managers do not internalize decisions well, which could reduce the effectiveness of the research design.Originality/valueThere are few studies in the areas covered by this paper due to the proprietary nature of the private equity industry. The results are important because they help in understanding how fund managers use decision aids such as financial statements and valuation techniques. A better understanding of current practices will help fund managers and fund sponsors in devising improved decision aids and processes, which ultimately may lead to fewer non‐performing investments. This is especially important in private equity since investment decisions are often irreversible and binary.

3 - Correlation Modeling

Value and momentum returns and combinations of them across both countries and asset classes are explained by their loadings on global macroeconomic risk factors. These loadings describe why value and momentum have positive return premia, although being negatively correlated. The global macroeconomic risk factors also perform well in capturing the returns on other characteristic-based portfolios. The findings identify a global macroeconomic source of the common variation in returns across countries and asset classes.

Individual Account Investment Options and Portfolio Choice: Behavioral Lessons from 401(k) Plans

Femoral stemmed total knee arthroplasty (FS TKA) may be used in patients deemed higher risk for periprosthetic fracture (PPF) to reduce PPF risk. However, the cost effectiveness of FS TKA has not been defined. Using a risk modeling analysis, we investigate the cost effectiveness of FS in primary TKA compared with the implant cost of revision to distal femoral replacement (DFR) following PPF. A model of risk categories was created representing patients at increasing fracture risk, ranging from 2.5 to 30%. The number needed to treat (NNT) was calculated for each risk category, which was multiplied by the increased cost of FS TKA and compared with the cost of DFR. The 50th percentile implant pricing data for primary TKA, FS TKA, and DFR were identified and used for the analysis. FS TKA resulted in an increased cost of $2,717.83, compared with the increased implant cost of DFR of $27,222.29. At 50% relative risk reduction with FS TKA, the NNT for risk categories of 2.5, 10, 20, and 30% were 80, 20, 10, and 6.67, respectively. At 20% risk, FS TKA times NNT equaled $27,178.30. A 10% absolute risk reduction in fracture risk obtained with FS TKA is needed to achieve cost neutrality with DFR. FS TKA is not cost effective for low fracture risk patients but may be cost effective for patients with fracture risk more than 20%. Further study is needed to better define the quantifiable risk reduction achieved in using FS TKA and identify high-risk PPF patients.

In minimal risk portfolios, costs associated with transactions are essential in calculating the net performance. The transaction costs of maintaining such portfolios are predominantly negative due to the fact that traditional portfolio optimization strategies, which focus solely on risk and return, neglecting transaction costs typically incurred through rebalancing. This research analyzes the impact of costs associated with transactions while constructing minimum risk portfolios centered around risk parity models and provides a way to control those costs. We investigate the performance of portfolios under fixed and flexible costs and include these parameters in the optimization model to achieve more realistic results. Applying real-world data on conventional stock portfolios and highly volatile cryptocurrency markets, we demonstrate the performance of mean–variance optimization (M-V), risk parity with standard deviation (RP-Std), and risk parity with Conditional Value at Risk (RP-CVaR) through empirical data for both stock portfolios and cryptocurrencies. We found that potential transaction costs can cause portfolio returns to change by anywhere between 0.5 to 2% per year depending on how often one trades, and market conditions. By highlighting how crucial it is to incorporate transaction costs into the decision-making process, this study contributes to the expanding literature of research on portfolio optimization. For investors looking to create and manage their portfolios in a way that balances risk, return, and cost effectiveness, our findings offer useful insights. Future studies might investigate adaptive models that dynamically adapt to shifting cost structures and market situations, or they could generalize these findings to other asset classes.

Cost-effectiveness estimates of the Mwanza sexually transmitted diseases intervention

Realized hedge ratio: Predictability and hedging performance