基于风险测度理论的VaR与CVaR的比较研究

本文分别在Artzner等(1999)提出的一致风险测度理论框架内和随机占优理论框架内比较VaR和CVaR的优劣,指出CVaR在性质上要优于VaR。但在椭圆分布假定下,VaR依然保持子可加性和二阶随机占优的一致性。由于椭圆分布包含诸如t分布以及帕累托分布等能够反映厚尾特征的分布,因此VaR依然可以刻画金融时间序列数据的尾部特征。此外,本文探讨了CVaR在风险管理和监管实践中遇到的问题,指出CVaR模型的事后检验不易实施。     

Optimal insurance contract under VaR and CVaR constraints

This study endogenously develops an optimal insurance contractual form for maximizing insured expected utility under VaR and CVaR constraints. We find that CVaR constraint does not affect the contractual form, but may increase minimum insurance premium requirement. Additionally, when the VaR constraint is binding, the optimal contract is a double deductible insurance. However, if the contract is restricted to a regular form (both indemnity schedule and retained loss schedule are continuously nondecreasing) for avoiding moral hazard problem, the optimal contract is a piecewise linear deductible insurance. Finally, we provide intuitive comparison between this study result and relevant studies.     

三种Copula-VaR计算方法与传统VaR方法的比较

金融风险测量VaR方法广泛应用于银行等金融机构,Copula技术以其处理非正态联合分布函数所具有的良好性质逐渐成为国内外研究的热点。本文将Copula理论应用于VaR的计算方法,并与传统的VaR方法进行比较,通过美元和欧元组合的实证研究,得到基于Copula的VaR方法能够更加有效地测量风险的结论。     

Adjusting MV-efficient portfolio frontier bias for skewed and non-mesokurtic returns

This study investigates the bias adjustment for mean–variance efficient portfolio frontiers due to population mean and variance estimation error in Taiwan stock market. Although Siegel and Woodgate (2007; Management Science, 53, 1005–1015) and Kan and Smith (2008; Management Science, 54, 1364–1380) suggested two portfolio frontiers that improved upon the out-of-sample performance of a traditional sample portfolio frontier. However, this study shows that, using the copula function and Gram-Charlier series, the two frontiers are theoretically biased toward the actual frontier unless returns behave normally, and the bias is related to the return skewness and kurtosis. Indeed, the two frontiers are empirically biased to the lower-left side of the actual ones, because the Taiwan stock returns are right-skewed and highly leptokurtic. Thus, this study thus proposes revised portfolio frontiers that are closer to the actual frontier than unrevised ones. This improvement may enhance the estimation accuracy of the capital market line, and hence this study can provide an effective investment reference.     

Long-term wealth growth portfolio allocation under parameter uncertainty: A non-conservative robust approach

The Kelly portfolio, which is documented to have the highest wealth growth rate of any other portfolio in the long run, has highly risky and unstable performance in the short term. This paper offers a hybrid approach to address this problem by integrating the concept of ridge regression and shrinkage estimation into a robustly modified Kelly portfolio. The proposed approach is a two-stage optimization process that not only takes into account the effect of estimation error but also solves the notoriously conservative problem introduced by the robust optimization method. By extending the worst-case scenarios considered by the robust Kelly portfolio, our approach significantly improves its out-of-sample performance without compromising risk reduction. In an extensive out-of-sample analysis with simulated and empirical data sets, we also characterize the impacts of the robustness level and the length of the rolling window on the final result. Moreover, we conduct a comparative study to confirm the validity of the proposed approach, and our model allows the investor to have a better risk-return trade-off than other traditional models.     

Corporate tax,financial leverage,and portfolio risk

We examine the theoretical implications of corporate income tax for a risky portfolio in a aggregate-endowment economy. In this model, corporate income tax affects the portfolio risk associated with the rebalancing motive during market clearance. An asset is defined as a portfolio of stocks and bonds whose portfolio weights are similar to financial leverage. Corporate tax can decrease after-tax consumption from dividends (increase leverage) and increase the tax shield that increases dividends (decrease leverage). Changes in dividends are responsible for the correlation between expected dividend growth and consumption growth and, thus, affect stock pricing and returns. Overall, the model is characterized by tax-induced portfolio risk associated with financial leverage.     

Value investing versus other investment strategies: A volatility spillover approach and portfolio hedging strategies for investors

Motivated by the incessant demand for portfolio diversification, this study examines the connectedness between value and diverse types of stocks (growth, momentum, ESG, high beta, classic S&P 500, volatility). The applied methodology encompasses the time-varying parameter vector autoregressive (TVP-VAR) extension of the Diebold and Yilmaz (2012) framework for the period from 03/31/2011 to 03/31/2021. Results show moderate volatility transmissions among the sampled assets, which tend to escalate during periods of turmoil, such as the European Sovereign Debt Crisis, the plunge in oil prices and the COVID-19 outbreak. Growth and ESG stocks play an indispensable part in the transmission mechanism. Moreover, we investigate the hedging ability of value stocks within a portfolio containing other stocks, by estimating hedge ratios and optimal weights with the usage of conditional variance estimates (DCC-GARCH). The empirical findings reveal that value stocks can adequately hedge against the risk deriving from the volatility of the remaining investment instruments, especially in the case of high beta and volatility stocks. Thus, this analysis provides portfolio managers and investors with valuable insights in order for them to hedge their stock portfolios effectively.     

Dynamic volatility transmission and portfolio management across major cryptocurrencies: Evidence from hourly data

This study used hourly data to examine the dynamic conditional correlations and hedging strategies in the main cryptocurrency markets: Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), and Ripple (XRP). Multivariate generalized autoregressive conditional heteroskedasticity family models provided evidence of significant positive dynamic conditional correlations among these markets. A weaker conditional correlation was observed for the LCT–XRP portfolio than for the BTC–ETH portfolio, which had the highest correlation value. The dynamic correlations intensified after the cryptocurrency crisis. The results of a portfolio risk analysis suggested that investors should hold less BTC than LTC, ETH, and XRP to minimize risk while maintaining consistent expected portfolio returns. Investors should hold less BTC than the other cryptocurrencies during a crisis. In addition, the cheapest hedge strategy is to hold long BTC and short XRP regardless of the period. Holding long BTC and short LTC was found to be the most expensive hedge strategy. Finally, the study showed that an optimally weighted diversified portfolio provides the greatest reduction in risk and downside risk for ETH and LTC. For XRP, portfolio hedging is the best mechanism for reducing risk.     

Portfolio management with robustness in both prediction and decision: A mixture model based learning approach

We develop in this paper a novel portfolio selection framework with a feature of double robustness in both return distribution modeling and portfolio optimization. While predicting the future return distributions always represents the most compelling challenge in investment, any underlying distribution can be always well approximated by utilizing a mixture distribution, if we are able to ensure that the component list of a mixture distribution includes all possible distributions corresponding to the scenario analysis of potential market modes. Adopting a mixture distribution enables us to (1) reduce the problem of distribution prediction to a parameter estimation problem in which the mixture weights of a mixture distribution are estimated under a Bayesian learning scheme and the corresponding credible regions of the mixture weights are obtained as well and (2) harmonize information from different channels, such as historical data, market implied information and investors׳ subjective views. We further formulate a robust mean-CVaR portfolio selection problem to deal with the inherent uncertainty in predicting the future return distributions. By employing the duality theory, we show that the robust portfolio selection problem via learning with a mixture model can be reformulated as a linear program or a second-order cone program, which can be effectively solved in polynomial time. We present the results of simulation analyses and primary empirical tests to illustrate a significance of the proposed approach and demonstrate its pros and cons.     

A novel two-stage method for well-diversified portfolio construction based on stock return prediction using machine learning

The performance of portfolio model can be improved by introducing stock prediction based on machine learning methods. However, the prediction error is inevitable, which may bring losses to investors. To limit the losses, a common strategy is diversification, which involves buying low-correlation stocks and spreading the funds across different assets. In this paper, a diversified portfolio selection method based on stock prediction is proposed, which includes two stages. To be specific, the purpose of the first stage is to select diversified stocks with high predicted returns, where the returns are predicted by machine learning methods, i.e. random forest (RF), support vector regression (SVR), long short-term memory networks (LSTM), extreme learning machine (ELM) and back propagation neural network (BPNN), and the diversification level is measured by Pearson correlation coefficient. In the second stage, the predictive results are incorporated into a modified mean–variance (MMV) model to determine the proportion of each asset. Using China Securities 100 Index component stocks as study sample, the empirical results demonstrate that the RF+MMV model achieves better results than similar counterparts and market index in terms of return and return–risk metrics.     

Supplier relationship portfolio management: A social exchange perspective

Despite the importance of relationship portfolios, it's unclear how a buying firm's differential investment in its suppliers affects the distribution of its supplier relationships and the supplier-provided benefits that result. Drawing from social exchange theory (SET), we assess the sequential linkages among supply management practices, supplier relationship sets that vary in closeness, and relational benefits. Empirically, we adopt a multi-methodological approach that combines abductive case-based and deductive survey-based research. In our case-based approach, interview responses from 34 professionals within a global Tier 1 automotive manufacturer (MFGR) and four of its suppliers, open-ended survey responses from 56 buyers and 86 engineers within MFGR, documentary evidence, and direct observations facilitate the operationalization of supply management practices and relationship closeness constructs. The survey-based study integrates case-based findings and uses response data from sales managers within 292 suppliers to MFGR and matched supplier performance data from MFGR to test a theoretical model of social exchange. In a multi-step process, we apply cluster analysis, multinomial logistic regression, ANOVA, and multiple regression to this aggregated dataset to (1) identify three distinct sets of supplier relationships that are distributed along a relationship closeness continuum, (2) show how specific supply management practices affect the composition of supplier relationship sets that comprise a buying firm's portfolio, and (3) demonstrate how supplier-provided benefits differ across supplier relationship sets. Our results validate the utility of SET as applied to supplier portfolio management and provide insights into buyers' actions that drive closer relationships, minimize risk, and maximize benefits across a supplier portfolio.     

A portfolio approach to endogenous growth: equilibrium and optimal policy

This paper develops a portfolio approach to modeling endogenous growth in continuous time that is especially suitable for addressing fiscal and financial issues in policy design. The analysis focuses on the equilibrium relationship between fiscal and financial policy, rates of return and wealth allocation. We analyze two models. The first is based on the Arrow-Romer model with increasing returns and an external effect of capital on labor productivity. The second draws on Barro's analysis of government spending and endogenous growth. In both models, we study the equilibrium allocation and discuss the optimal fiscal and financial policy.     

Credit risk and asymmetric information: A simplified approach

We present a simple model for risky, corporate debt. Debtholders and equityholders have incomplete information about the financial state of the debt issuing company. Information is incomplete because it is delayed for all agents, and it is asymmetrically distributed between debtholders and equityholders. We solve for the equityholders' optimal default policy and for the credit spreads required by debtholders. Delayed information accelerates the equityholders' optimal decision to default. Interestingly, this effect is small, implying only a small impact on credit spreads. Asymmetric information, however, has a major impact on credit spreads. Our model predicts high credit spreads for short-term debt, as observed empirically in credit markets.     

Forecasting extreme financial risk: A score-driven approach

This paper develops a new class of dynamic models for forecasting extreme financial risk. This class of models is driven by the score of the conditional distribution with respect to both the duration between extreme events and the magnitude of these events. It is shown that the models are a feasible method for modeling the time-varying arrival intensity and magnitude of extreme events. It is also demonstrated how exogenous variables such as realized measures of volatility can easily be incorporated. An empirical analysis based on a set of major equity indices shows that both the arrival intensity and the size of extreme events vary greatly during times of market turmoil. The proposed framework performs well relative to competing approaches in forecasting extreme tail risk measures.     

Forecasting Bitcoin risk measures: A robust approach

Over the last few years, Bitcoin and other cryptocurrencies have attracted the interest of many investors, practitioners and researchers. However, little attention has been paid to the predictability of their risk measures. This paper compares the predictability of the one-step-ahead volatility and Value-at-Risk of Bitcoin using several volatility models. We also include procedures that take into account the presence of outliers and estimate the volatility and Value-at-Risk in a robust fashion. Our results show that robust procedures outperform non-robust ones when forecasting the volatility and estimating the Value-at-Risk. These results suggest that the presence of outliers plays an important role in the modelling and forecasting of Bitcoin risk measures.     

A proactive approach to supply chain risk management: Shifting orders among suppliers to mitigate the supply side risks

Globalization, e-trade, advanced technologies and emerging production techniques have increased supply chains’ efficiency and added value. However, despite numerous advantages, these factors make supply chains more fragile and vulnerable to risks. For this reason, companies that perform supply chain risk management gain competitive advantage. In the past, supply chain managers mainly focused on reducing costs; but recently, they have begun to give importance to supply chain continuity and resiliency which have significant impacts on costs as well. Hence, conventional reactive planning has given way to proactive planning in supply chain risk management. In this study, the supply chain risk management process is investigated and a procedure is proposed in the risk mitigation phase. In the first stage of the proposed procedure, an initial procurement plan is obtained via a linear programming model, considering the cost criterion as the first priority. In the second stage, this plan is revised by including the risk criterion into the planning as the second priority. The aim of this procedure that enables proactive planning is to reduce the supply side risks. The model is tested with a hypothetical data set and the cost analysis is performed to evaluate the performance of the procedure. Finally, the whole supply chain risk management process including the proposed procedure is applied to an international automotive company.     

The development and application of a process model for R&D project management in a high tech firm: A field study

In R&;D organizations of high tech firms, multiple R&;D projects are executed concurrently and timeliness of project completion - i.e., developing the right products at the right times - is a matter of serious concern. Given that the priority of R&;D projects and the interdependencies between the projects in a high tech firm change dynamically, high tech R&;D project management is a complex and challenging endeavor. To improve the understanding and management of high tech R&;D projects, this paper reports the findings of a field study where we, first, develop and empirically estimate a model that relates project priority over time with the generative mechanisms of market pull and technical challenge associated with R&;D projects. Next, we develop and demonstrate the application of a process model within which the time-varying project priority model is embedded. The process model makes it possible to allocate fixed resources among competing projects with time-varying interdependencies, thereby improving the timeliness of project completion. This research was conducted in collaboration with a major U.S. high tech firm. The corporate R&;D center of the firm served as the research setting for the field study. We present an application of the process model to delineate the evolution of the R&;D organization with the merger of its (technology driven) parent firm with another (market driven) high tech manufacturing firm. The application of the process model generates theoretical insights that are used to develop testable propositions. Implications of the study findings and directions for future research are discussed.     

Modelling international sovereign risk information spillovers: A multilayer network approach

Applying the TVP-VAR model, we creatively construct multilayer information spillover networks containing return spillover layer, volatility spillover layer and extreme risk spillover layer among 23 countries in the G20 to explore international sovereign risk spillovers. From the perspective of system-level and country-level measures, this article explores the topological structures of static and dynamic multilayer networks. We observe that (i) at the system-level, multilayer measures containing uniqueness edge ratio and average edge overlap show each layer has unique network structures and spillover evolution behavior, especially for dynamic networks. Average connectedness strength shows volatility and extreme risk spillover layers are more sensitive to extreme events. Meanwhile, three layers have highly intertwined and interrelated relations. Notably, their spillovers all show a great upsurge during the crisis (financial and European debt crisis) and the COVID-19 pandemic period. (ii) At the country-level, average overlapping net-strength shows that countries’ roles are different during distinct periods. Multiplex participation coefficient on out-strength indicates we’ll focus on countries with highly heterogeneous connectedness among three layers during the stable period since their underestimated spillovers soar in extreme events or crises. Multilayer networks supply comprehensive information that cannot obtain by single-layer.     

Contagion risk in the Czech financial system: A network analysis and simulation approach

This paper examines the potential for contagion within the Czech banking system via the channel of interbank exposures of domestic banks, enriched by a liquidity channel and an asset price channel, over the period March 2007 to June 2012. A computational model is used to assess the resilience of the Czech banking system to interbank contagion, taking into account the size and structure of interbank exposures as well as balance sheet and regulatory characteristics of individual banks in the network. The simulation results suggest that the potential for contagion due to credit losses on interbank exposures was rather limited. Even after the introduction of a liquidity condition into the simulations, the average contagion was below 3.8% of the remaining banking sector assets, with the exception of the period from December 2007 to September 2008. Activation of the asset price channel further increases the losses due to interbank contagion, showing that the liquidity of government bonds would be essential for the stability of Czech banks in stress situations. Finally, the simulation results for both idiosyncratic and multiple bank failure shocks suggest that the potential for contagion in the Czech banking system has decreased since the onset of the global financial crisis.