Road to recovery: Managing an epidemic

Without widespread immunization, the road to recovery from the current COVID-19 lockdowns will optimally follow a path that finds the difficult balance between the social and economic benefits of liberty and the toll from the disease. We provide an approach that combines epidemiology and economic models, taking as given that the maximum capacity of the healthcare system imposes a constraint that must not be exceeded. Treating the transmission rate as a decreasing function of the severity of the lockdown, we first determine the minimal lockdown that satisfies this constraint using an epidemiology model with a homogeneous population to predict future demand for healthcare. Allowing for a heterogeneous population, we then derive the optimal lockdown policy under the assumption of homogeneous mixing and show that it is characterized by a bang–bang solution. Possibilities such as the capacity of the healthcare system increasing or a vaccine arriving at some point in the future do not substantively impact the dynamically optimal policy until such an event actually occurs.     

Geopolitical risk and stock market volatility in emerging markets: A GARCH – MIDAS approach

In this study, we examine the connection between geopolitical risk (GPR) and stock market volatility in emerging economies. Our motivation for this study is premised on the need to assess both the predictability and the associated economic gains in relation to the subject in order to offer more useful insights to investors and practitioners. To the best of our knowledge, this is the first study that jointly considers these objectives. Consequently, we employ the GARCH-MIDAS framework which accommodates mixed data frequencies thereby circumventing information loss or any associated bias. We find that emerging stock market volatility responds more positively to geopolitical risks although the act-related GPR index offers better out-of-sample forecasts than the threat-related GPR. We also find that accounting for global economic factors in the predictability analysis is crucial for robust outcomes. Finally, we provide some utility gains of including GPR in the predictive model of stock market volatility while also highlighting some useful implications of our findings for investment and policy decisions.     

Does economic globalization affect the level and volatility of labor demand by skill? New insights from the Tunisian manufacturing industries

The central aim of this paper is to assess the effects of economic globalization on the level and volatility of labor demand for different skill groups in Tunisia. Using a panel dataset covering six manufacturing industries between 1983 and 2009, three main findings are reported. First, exports and imports exert a positive impact only on the semi-skilled and skilled labor demand while foreign direct investment flows increase the demand for semi-skilled and unskilled workers. Second, the regional analysis suggests that exports to the European Union boost the demand for the semi-skilled and skilled labor. Imports from the rest of the world exert similar effects on the demand for these two categories of workers. It emerges also that imports from the European Union lead to a higher demand for skilled labor, which gives support to the validity of the skill-enhancing trade hypothesis in Tunisian industries. Finally, our findings suggest that both exports and imports rise the employment volatility associated with skilled workers. On the other hand, there is a weak evidence of increased employment volatility as a result of foreign direct investment flows.     

Estimation of Asymmetric Stochastic Volatility Models: Application to Daily Average Prices of Energy Products

Energy supply and demand, and as a consequence energy prices, are likely to represent one of the biggest challenges of the 21st century. Commodity markets exhibit increased volatility when there is little or no underutilized supply capability to meet natural fluctuations in demand. In the case of energy markets, the large capital requirements and significant lead times associated with energy production and delivery make them more susceptible to the imbalances in supply capability and demand. Energy price volatility has destructive impact on market agents, and this impact is intensified when the prices exhibit asymmetric volatility. This article pursues two aspects of the issue. First we consider general aspects, especially the asymmetric pattern of volatility of daily returns of different types of energy products. Then, we analyze the behaviour of daily returns by using traditional models of volatility that include AGARCH, TGARCH, EGARCH, and ARSV strategies, as well as a threshold asymmetric autoregressive stochastic volatility (TA-ARSV) model that we propose. The energy products considered in this analysis are probably the most relevant energy products for the economic activity of the nations and the economic relations between countries: Crude Oil (OPEC reference basket and London Brent index), Gasoline, Natural Gas, Butane, and Propane. We use spot prices and the time reference ranges from 1986–1993 to 2009 depending on the product.     

The impact of idiosyncratic uncertainty when investment opportunities are endogenous

This paper develops a general equilibrium model to study the impact of aggregate fluctuations in idiosyncratic volatility that incorporates the endogenous determination of investment opportunities. By making investment options more valuable, an increase in volatility encourages the creation of new investment options. I find the response of the economy to a volatility shock depends on how investment opportunities are obtained. If potential entrants are allowed to invest in new idiosyncratic technologies, thereby acquiring options for further investment, the volatility shock increases overall investment and results in an economic boom. On the other hand, if such an investment in option creation is precluded and investment opportunities are exogenously given, the volatility shock decreases aggregate investment.     

News shocks and asset price volatility in general equilibrium

We study equity price volatility in general equilibrium with news shocks about future productivity and monetary policy. As West (1988) shows, in a partial equilibrium present discounted value model, news about the future cash flow reduces asset price volatility. We show that introducing news shocks in a canonical dynamic stochastic general equilibrium model may not reduce asset price volatility under plausible parameter assumptions. This is because, in general equilibrium, the asset cash flow itself may be affected by the introduction of news shocks. In addition, we show that neglecting to account for policy news shocks (e.g., policy announcements) can potentially bias empirical estimates of the impact of monetary policy shocks on asset prices.     

Supply constraints and housing market dynamics

Although the volatility of house prices is often ascribed to demand-side factors, constraints on housing supply have important and little-studied implications for housing dynamics. I illustrate the strong relationship between the volatility of house prices and the regulation of new housing supply. I then employ a dynamic structural model of housing investment to investigate the mechanisms underlying this relationship. I find that supply constraints increase volatility through two channels: First, regulation lowers the elasticity of new housing supply by increasing lags in the permit process and adding to the cost of supplying new houses on the margin. Second, geographic limitations on the area available for building houses, such as steep slopes and water bodies, lead to less investment on average relative to the size of the existing housing stock, leaving less scope for the supply response to attenuate the effects of a demand shock. My estimates and simulations confirm that regulation and geographic constraints play critical and complementary roles in decreasing the responsiveness of investment to demand shocks, which in turn amplifies house price volatility.     

The Impact of Short‐ and Long‐run Exchange Rate Uncertainty on Investment: A Panel Study of Industrial Countries*

We examine the relationship between aggregate investment and exchange rate uncertainty in the G7, using panel estimation and decomposition of volatility derived from the components generalized autoregressive conditionally heteroscedastic (GARCH) model. Our dynamic panel approach takes account of potential cross‐sectional heterogeneity, which can lead to bias in estimation. We find that for a poolable subsample of European countries, it is the transitory and not the permanent component of volatility which adversely affects investment. To the extent that short‐run uncertainty in the CGARCH model characterizes higher frequency shocks generated by volatile short‐term capital flows, these are most deleterious for investment.     

Time-consistent investment policies in Markovian markets: A case of mean–variance analysis

The optimal investment policy for a standard multi-period mean–variance model is not time-consistent because the variance operator is not separable in the sense of the dynamic programming principle. With a nested conditional expectation mapping, we develop an investment model with time consistency in Markovian markets. Furthermore, we examine the differences of the investment policies with a riskless asset from those without a riskless asset. Analytical solutions for time-consistent optimal investment policies and the resulting mean–variance efficient frontier are obtained. Finally, using numerical examples, we show that the optimal investment policy derived from our model is more efficient than that of the standard mean–variance model in which the trade-off is determined between the mean and variance of the terminal wealth.     

An empirical model of mainframe computer investment

This paper introduces a dynamic model of investment decisions in mainframe computer systems. I estimate and test the model using detailed micro data from a company in the telecommunications industry. The model accounts for ‘technological depreciation’ which distinguishes computers from other investment goods where physical depreciation is typically a key factor underlying replacement investment decisions. The company increased its installed mainframe computer capacity by over 30‐fold over the 10‐year sample period. Part of this growth was undoubtedly due to the huge increase in performance and the corresponding drop in the per unit cost of processing capacity of mainframes, a consequence of ‘Moore's law’. However, there was also tremendous growth in the need for computers for billing, account processing and other tasks, due to the rapid growth in the telecommunications industry over this same time period. I estimate the unknown parameters of the investment model using a nonlinear least squares–nested fixed‐point algorithm (NLS‐NFXP), which solves the Bellman equation underlying the dynamic model of investment and replacement of mainframe computers by nonlinear least squares. I demonstrate that it is feasible to estimate this model on an ordinary PC, whereas standard discretization approaches to solving the firm's optimal investment policy might not even be feasible using supercomputers. I show that the estimated model fits the data very well, and accurately captures the large growth in installed mainframe capacity, the timing and magnitude of replacement investment, as well as periodic upgrades of existing mainframe units. I use the model to decompose how much of the 30‐fold increase in mainframe computer capacity is due to ‘Moore's law’ (i.e. the huge drop in the unit cost of installed mainframe capacity), and how much is due to the growth in demand for services of mainframes, due to the rapid growth in demand for telecommunications services (particularly cell phone accounts) by the firm's customers. Copyright © 2010 John Wiley & Sons, Ltd.     

供应商参与产品创新的供应链协调研究

考虑供应商对产品创新的投入会影响未来需求,分析了初始需求为确定型和随机型两种情况下,供应商的最优创新投入策略和制造商的最优订货策略,给出了实现供应链协调的条件。研究表明,在确定初始需求下,要实现供应链的协调,只需要制造商给予创新失败的供应商一定的创新补贴;而在随机初始需求下,创新补贴与其他机制同时使用才能实现供应链协调。     

Optimal investment and reinsurance policies for an insurer with ambiguity aversion

In this paper, we study the optimal investment and reinsurance problem for an insurer based on the variance premium principle, in which three cases are considered. First, we assume that the financial market does not exist. The insurer only holds an insurance business, and the optimal reinsurance problem is studied. Subsequently, we assume that there exists a financial market with an accurately modeled risky asset. The optimal investment and reinsurance problem is investigated under these conditions. Finally, we consider the general case in which the insurer is concerned about the model ambiguity of both the insurance market and the financial market. In all three cases, the value function is set to maximize the expected utility of terminal wealth. By employing the dynamic programming principle, we derive the Hamilton–Jacobi–Bellman (HJB) equations, which are satisfied by the value functions and obtain closed-form solutions for optimal reinsurance and investment policies and the value functions in all three cases. Most interestingly, we elucidate how investment improves the insurer’s utility and find that the existence of ambiguity can significantly affect the optimal policies and value functions. We also compare the ambiguities in the two markets and find that ambiguity in the insurance market has much more significant impact on the value function than the ambiguity in the financial market. It implies that it is more valuable for insurer to precisely evaluate the insurance risk. We also provide some numerical examples and economic explanations to illustrate our results.     

News,disaster risk,and time-varying uncertainty

Standard real business cycle models are often unable to replicate three empirical facts: positive output in response to good news, stochastic volatility of macro variables, and asymmetric business cycles. This paper proposes a unified basis for understanding these facts in a tractable dynamic stochastic general equilibrium (DSGE) model, in which the key is the interaction of information flows and disaster risk. Information flows fluctuate between two regimes with different precision levels for signals regarding future economic fundamentals. A shift in forecast precision changes the probability of entering an economic disaster. High disaster risk leads to low expected capital returns and a decline in hours, investment, and output. Changing information structures results in different volatility and skewness over the business cycle. Simple theory makes the two expectation effects through information flows and disaster risk transparent. Quantitatively, the model suggests that the interaction of the two expectation effects plays a significant role in accounting for the higher-order moments of the business cycle.     

Global Engagement and Returns Volatility

This paper finds that a greater reliance on foreign market sales increases the volatility of firms’ stock returns, using high‐frequency data for publicly listed Japanese manufacturing firms over the period 2000–10. The two margins of global engagement we consider, namely, exports and sales via foreign affiliates (horizontal foreign direct investment), have both a positive and economically significant effect on firm‐level volatility. We find, however, that increasing the intensity of sales through foreign affiliates has a stronger effect on volatility than a similar change in export intensity. We also uncover evidence consistent with the notion that firms’ need to use external finance to cover the substantial costs involved in reaching foreign consumers can be an important channel through which firms’ participation in international markets increases their exposure to economic uncertainty.     

Out-of-sample prediction of Bitcoin realized volatility: Do other cryptocurrencies help?

Inspired by cross-market information flows among international stock markets, we incorporate external predictive information from other cryptocurrency markets to forecast the realized volatility (RV) of Bitcoin. To make the most of such external information, we employ six widely accepted approaches to construct predictive models based on multivariate information. Our results suggest that the scaled principal component analysis (SPCA) approach steadily improves the predictive ability of the prevailing heterogeneous autoregressive (HAR) benchmark model considering both the model confidence set (MCS) test and the Diebold–Mariano (DM) test based on three widely accepted loss functions. The forecasting performance is persistent to various robustness checks and extensions. Notably, a mean–variance investor can obtain steady positive economic gains if the investment portfolio is constructed on the basis of the forecasts from the HAR-SPCA model. The results of this study show that external predictive information is statistically and economically important in forecasting Bitcoin RV.     

Illiquidity,transaction cost,and optimal holding period for real estate: Theory and application

Choosing the optimal holding period is an important part of real estate investment decisions, because “when to sell” affects “whether to buy”. This paper presents a theoretical model for such decision making. Our model indicates that the optimal holding period is affected by both systematic and non-systematic factors—market conditions (illiquidity and transaction cost) and property performance (return and return volatility). Other things being equal, higher illiquidity and transaction costs lead to longer holding periods, while higher return volatility implies shorter holding periods. Our empirical application suggests that the optimal holding period based on our model is quite consistent with previous empirical findings. In addition, we find that when illiquidity risk is incorporated the true real estate risk is significantly higher than the conventional risk estimate. Therefore, the current practice of real estate valuation, which is naively borrowed from finance theory, substantially underestimates real estate risk.     

Lock-in situations in supply chains: A social exchange theoretic study of sourcing arrangements in buyer–supplier relationships

Social exchange theory is used to gain a better understanding of the relationship between a buyer and a supplier that is characterized by lock-in situations. We begin by reviewing the theoretical foundations of social exchange theory. Next, we use an illustrative multinational business example from a Danish Business Group to demonstrate the complexities of the lock-in situation. Conjectures related to lock-in behaviors are initially developed and then examined by means of a game-theoretic model. The analysis provides a basis for the development of propositions which are examined employing a behavioral experiment. The results indicate that the optimal pricing strategy of the supplier is to lower the price with increasing demand and the optimal investment intensity of the buyer decreases with increasing demand. The paper concludes by presenting directions for future research.     

Bertrand competition with capacity constraints: mergers among parking lots

To analyze the effects of mergers among firms facing capacity constraints, we develop a numerical model of price-setting behavior among multi-product firms differentiated by location and capacity. We perform a number of computational experiments designed to inform merger policy, with specific reference to the Central Parking–Allright merger of 1999. The experiments show that capacity constraints on merging firms attenuate merger effects by much more than capacity constraints on non-merging firms amplify them. The experiments also highlight the dependence of merger welfare effects on parking demand. In preparation for further industry consolidation, we propose estimators of parking demand to more precisely estimate the costs and benefits of future mergers.     

Optimal harvesting under resource stock and price uncertainty

We analyze partial and complete depletion harvesting policy under resource stock and price uncertainty and risk neutrality. We state a set of weak conditions under which the optimal policy can be characterized by a single threshold and show that the value can be expressed in a separable form where price volatility affects the value through the risk adjusted growth rate. Both higher price and stock volatility decrease the value when the correlation between the driving Brownian motions is negative. With no correlation the optimal policy is independent of price volatility while higher stock volatility increases the harvesting threshold.     

需求驱动下的经济增长及增长中的价格水平波动——基于投资与消费本质属性分析框架的阐释

基于投资与消费本质属性的理论框架和实证分析表明:(1)消费需求是经济增长的最初需求动力,对经济增长的驱动作用由强变弱再变强,受消费需求导向的投资需求对经济增长的驱动作用由弱变强再变弱;(2)均衡增长状态下,产出与投资、消费同步增长,价格水平基本稳定;(3)相对于消费需求而言,投资需求的"投资滞后性"和"投资惯性"的存在使投资常与消费非同步增长,导致经济失衡和价格水平规律性波动,即经济发展早期阶段的价格水平上升多为消费需求拉动,经济发展中后期阶段的价格水平上升多为成本推动,而结构性价格水平上升会随着消费结构升级而成为一种常态;(4)1978年以来,中国经济增长的总体态势良好,但长期高投资率导致的强"投资惯性"致使当前产能过剩及通货膨胀预期并存,对中国经济持续稳定增长形成较大威胁。政府应通过以消费结构升级为导向扩大投资,努力提高农村居民收入水平,推进农村居民消费结构升级和稳定粮食价格等措施进行化解。。