Short Rate Dynamics and Regime Shifts*

We characterize the dynamics of the US short‐term interest rate using a Markov regime‐switching model. Using a test developed by Garcia, we show that there are two regimes in the data: In one regime, the short rate behaves like a random walk with low volatility; in another regime, it exhibits strong mean reversion and high volatility. In our model, the sensitivity of interest rate volatility to the level of interest rate is much lower than what is commonly found in the literature. We also show that the findings of nonlinear drift in Aït‐Sahalia and Stanton, using nonparametric methods, are consistent with our regime‐switching model.     

Modeling preference evolution in discrete choice models: A Bayesian state-space approach

We develop discrete choice models that account for parameter driven preference dynamics. Choice model parameters may change over time because of shifting market conditions or due to changes in attribute levels over time or because of consumer learning. In this paper we show how such preference evolution can be modeled using hierarchial Bayesian state space models of discrete choice. The main feature of our approach is that it allows for the simultaneous incorporation of multiple sources of preference and choice dynamics. We show how the state space approach can include state dependence, unobserved heterogeneity, and more importantly, temporal variability in preferences using a correlated sequence of population distributions. The proposed model is very general and nests commonly used choice models in the literature as special cases. We use Markov chain monte carlo methods for estimating model parameters and apply our methodology to a scanner data set containing household brand choices over an eight-year period. Our analysis indicates that preferences exhibit significant variation over the time-span of the data and that incorporating time-variation in parameters is crucial for appropriate inferences regarding the magnitude and evolution of choice elasticities. We also find that models that ignore time variation in parameters can yield misleading inferences about the impact of causal variables. This paper is based on the first author's doctoral dissertation.     

Are the dynamic linkages between the macroeconomy and asset prices time-varying?

We estimate a number of multivariate regime switching VAR models on a long monthly US data set for eight variables that include excess stock and bond returns, the real T-bill yield, predictors used in the finance literature (default spread and the dividend yield), and three macroeconomic variables (inflation, industrial production growth, and a measure of real money growth). Heteroskedasticity may be accounted for by making the covariance matrix a function of the regime. We find evidence of four regimes and of time-varying covariances. We show that the best in-sample fit is provided by a four state model in which the VAR(1) component fails to be regime-dependent. We interpret this as evidence that the dynamic linkages between financial markets and the macroeconomy have been stable over time. The four-state model can be helpful in forecasting applications and provides one-step ahead predicted Sharpe ratios.     

Using survival modeling for turn-time predictions in foodservice settings

Within the competitive foodservice industry, the ability to accurately measure the meal process known as turn-time is critical to the success of the firms in the industry. This is traditionally done through linear techniques such as multiple least squares (aka linear regression) or analysis of variance (ANOVA). However, linear techniques have theoretical properties that can potentially lead to bias in measurements of time duration variables, while survival models were designed for that purpose. This study utilized simulated data of a dine-in restaurant to test and compare the ability of linear regression to five survival models (proportional hazard models) for predicting the duration of turn-time. The results from the simulated trials show that while some of the survival models held incremental improvements, linear regression performed adequately for predicting the duration of turn-time even when taking the biased predictions into account. For operators who are in their infancy of developing restaurant revenue management systems, linear regression is recommended due to the practical ease of the models. On the other hand, operators who have well-established restaurant revenue management systems interested in incremental improvements should opt for survival models in predicting turn-time.     

Dove or Hawk? Characterizing monetary policy regime switches in India

The past two decades have witnessed a worldwide move by emerging markets to adopt explicit or implicit inflation targeting regimes. A notable and often discussed exception to this trend, of course, is China which follows a pegged exchange rate regime supported by capital controls. Another major exception is India. It is not clear how to characterize the monetary regime or identify the nominal monetary anchor in India. Is central bank policy in India following a predictable rule that is heavily influenced by a quasi inflation target? And how has the monetary regime been affected by the gradual process of financial liberalization in India? To address these points, we investigate monetary policy regime change in India using a Markov switching model to estimate a time-varying Taylor-type rule for the Reserve Bank of India. We find that the conduct of monetary policy over the last two decades can be characterized by two regimes, which we term ‘Hawk’ and ‘Dove.’ In the first of these two regimes, the central bank reveals a greater relative (though not absolute) weight on controlling inflation vis-à-vis narrowing the output gap. The central bank however was found to be in the “Dove” regime about half of our sample period, focusing more on the output gap and exchange rate targets to stimulate exports, rather than moderating inflation. India thus seems to be following its own direction in the conduct of monetary policy, seemingly not overly influenced by the emphasis on quasi-inflation targeting seen in many emerging markets.     

中国证券市场波动的区制关联性

应用单变量和二元向量SW-ARCH模型,本文发现我国证券市场收益率波动具有明显的"区制转移"特征,可以将其分为"高波动"和"低波动"两个区制.在"高波动区制"下,沪深两市间具有明显的波动"溢出"和"传染"特征,市场整体风险强;相反,在"低波动区制"下,市场间主要表现"低系统风险"特征,负相关的波动关联性使证券市场具有较强的风险分散功能和较高的资金配置效率.     

A direct solution method for pricing options in regime‐switching models

Pricing financial or real options with arbitrary payoffs in regime‐switching models is an important problem in finance. Mathematically, it is to solve, under certain standard assumptions, a general form of optimal stopping problems in regime‐switching models. In this article, we reduce an optimal stopping problem with an arbitrary value function in a two‐regime environment to a pair of optimal stopping problems without regime switching. We then propose a method for finding optimal stopping rules using the techniques available for nonswitching problems. In contrast to other methods, our systematic solution procedure is more direct as we first obtain the explicit form of the value functions. In the end, we discuss an option pricing problem, which may not be dealt with by the conventional methods, demonstrating the simplicity of our approach.     

Joint Optimization of Product Price, Display Orientation and Shelf-Space Allocation in Retail Category Management

We develop a model that jointly optimizes a retailer's decisions for product prices, display facing areas, display orientations and shelf-space locations in a product category. Unlike the existing shelf-space allocation models that typically consider only the width of display shelves, our model considers both the width and height of each shelf, allowing products to be stacked. Furthermore, as demand is influenced by each product's two-dimensional facing area, we consider multiple product orientations that capture three-dimensional product packaging characteristics. That enables our model to not only treat shelf locations as decision variables, but also retailers’ stacking patterns in terms of product display areas and multiple display orientations. Further, unlike the existing studies which consider a retailer's shelf-space allocation decisions independent of its product pricing decisions, our model allows joint decisions on both and captures cross-product interactions in demand through prices. We show how a branch-and-bound based MINLP algorithm can be used to implement our optimization model in a fast and practical way.     

The Volatility Behavior and Dependence Structure of Commodity Futures and Stocks

This study finds substantial risk diversification potential between certain commodity groups and stocks by exploring the dependence between their patterns of regime switching. None of the commodity groups share a common volatility regime with stocks, nor are the regime‐switching patterns of grains, industrials, metals, or softs, dependent on that of stocks. Simultaneous volatile regimes of commodity futures and stocks tend to be infrequent and short‐lived. In addition, in spite of financial contagion, animal products, grains, and softs typically demonstrate very low correlations with stocks even in simultaneous volatile regimes. © 2012 Wiley Periodicals, Inc. Jrl Fut Mark 34:93–101, 2014     

The cost of carry model and regime shifts in stock index futures markets: An empirical investigation

This article examines empirically the dynamic relationship between spot and futures prices in stock index futures markets employing a class of nonlinear, regime‐switching‐vector‐equilibrium‐correction models, which is novel in this context. Using data for the S&P 500 and the FTSE 100 over the post‐1987 crash period, it is shown that a long‐run relationship between spot and futures prices exists, which implies mean reversion of the basis. After providing strong evidence against the hypothesis of linear dynamics in the relationship under investigation, regime‐switching‐vector‐equilibrium‐correction models for spot and futures price movements are developed and shown to capture well the time‐series properties of our data, consistent with a large theoretical and empirical literature. © 2000 John Wiley & Sons, Inc. Jrl Fut Mark 20:603–624, 2000.     

A copula‐based regime‐switching GARCH model for optimal futures hedging

The article develops a regime‐switching Gumbel–Clayton (RSGC) copula GARCH model for optimal futures hedging. There are three major contributions of RSGC. First, the dependence of spot and futures return series in RSGC is modeled using switching copula instead of assuming bivariate normality. Second, RSGC adopts an independent switching Generalized Autoregressive Conditional Heteroscedasticity (GARCH) process to avoid the path‐dependency problem. Third, based on the assumption of independent switching, a formula is derived for calculating the minimum variance hedge ratio. Empirical investigation in agricultural commodity markets reveals that RSGC provides good out‐of‐sample hedging effectiveness, illustrating importance of modeling regime shift and asymmetric dependence for futures hedging. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 29:946–972, 2009     

Emerging market sovereign bond spreads and shifts in global market sentiment

The paper studies how the relationship between emerging market sovereign bond spreads, economic fundamentals and global financial market conditions differs across three regimes of global market sentiment. Following the identification of periods characterized by low, medium and high volatility in financial markets, we analyze the behavior of spreads from three different angles. First, we demonstrate that the cross-country correlation of spreads increases in high-volatility regimes, implying that countries cannot fully decouple from developments in other emerging markets during periods of distress. Second, using the interactions of several domestic and global variables with the probabilities of each regime prevailing in a given period as the explanatory variables of spreads, the fixed effects panel estimation shows that while country-specific fundamentals are important determinants of spreads in each regime, the importance of global financial conditions increases in high-volatility periods. Third, we show that countries can benefit from stronger fundamentals in the form of lower exposure of their sovereign spreads to unfavorable regime shifts in global market sentiment.     

THE IMPACT OF WIFE'S EMPLOYMENT AND EARNINGS ON FAMILY NET WORTH ACCUMULATION

The major focus of this research was the relationship between wife's earnings and employment and family net worth accumulation. Multiple regression analysis of data from the National Longitudinal Surveys found that, for the total sample, family income had a much greater impact than wife's earnings on change in net worth during the 1967–1972 period. Wife's employment was not related to the dependent variable once the effects of other factors were taken into account When the sample was segmented into four groups on the basis of 1967 net worth, independent variables had differential impacts on net worth change. For each group the multiple regression model explained a much greater proportion of total variance than it did for the sample as a whole. Among the two groups in the mid ranges of 1967 net worth, wife's employment was positively related to net worth change even after taking wife's earnings and other factors into account.     

FDI and Growth: What Causes What?

This paper examines the causal relationship between FDI and economic growth by using an innovative econometric methodology to study the direction of causality between the two variables. We apply our methodology, based on the Toda‐Yamamoto test for causality, to time‐series data covering the period 1969–2000 for three developing countries, namely Chile, Malaysia and Thailand, all of them major recipients of FDI with a different history of macroeconomic episodes, policy regimes and growth patterns. Our empirical findings clearly suggest that it is GDP that causes FDI in the case of Chile and not vice versa, while for both Malaysia and Thailand, there is a strong evidence of a bi‐directional causality between the two variables. The robustness of the above findings is confirmed by the use of a bootstrap test employed to test the validity of our results.     

The impact of exchange rate regimes on economic growth: Empirical study of a set of developing countries during the period 1974–2006

An increment in the quantity of services/goods manufactured per-head of the population over time denotes economic growth of a country. Exchange Rate Intermediate Regimes are unable to continue under conditions of capital movement. To examine the relationship between exchange rate regimes and economic growth. This study has kept its focus on the economic growth of a set of developing countries during the years (1974–2006). Fixed effects and pooled regression for 16 developing countries have been incorporated as the methodologies techniques for data. Analysis of data was performed through SPSS. A relationship between exchange rate regimes and economic growth has been identified through statistical approaches. The results indicated that as compared to flexible exchange regime, growth rate was higher by 1.2% when fixed exchange regime was adopted; and a growth rate of 0.64% was achieved under the intermediate regime when compared with the flexible regime. A positive impact has been identified in exchange rate regimes upon economic growth of the developing countries. Countries following the flexible exchange rates are facing scarcity for the existence of advanced financial systems, which deprives them of enjoying the benefits of flexible regime.     

Credit cycle dependent spread determinants in emerging sovereign debt markets

We address credit cycle dependent sovereign credit risk determinants. In our model, the spread determinants' magnitude is conditional on an unobservable endogenous sovereign credit cycle as represented by the underlying state of a Markov regime switching process. Our explanatory variables are motivated in the tradition of structural credit risk models and include changes in asset prices, interest rates, implied market volatility, gold price changes and foreign exchange rates. We examine daily frequency variations of U.S. dollar denominated Eurobond credit spreads of four major Latin American sovereign bond issuers (Brazil, Colombia, Mexico and Venezuela) with liquid bond markets during March 2000 to June 2011. We find that spread determinants are statistically significant and consistent with theory, while their magnitude remarkably varies with the state of the credit cycle. Crisis states are characterized by high spread change uncertainty and high sensitivities with respect to the spread change determinants. We further document that not only changes of local currencies, but also changes of the Euro with respect to the U.S. dollar are significant spread drivers and argue that this is consistent with the sovereigns' ability to pay.     

The digital distribution of music and its impact on the business models of independent musicians

This article analyses the impact of digital distribution of music on business models developed by independent musicians. More specifically, it aims to analyse the main changes in the network of relationships between musicians and record companies, and between musicians and consumers; and to analyse how independent musicians’ business models have been evolving as a response to increasing change in the distribution of music. The data was collected through semi-structured interviews with independent musicians, music journalists, and small independent record labels. The article discusses empirically the main results regarding changes in the network of relationships under study and analyses the evolution of business models, concluding that independent musicians follow a diversification strategy, aiming to capture the business opportunities provided by technological changes in the distribution of music. The implications of such an option, including its main challenges, are discussed.     

A random coefficient autoregressive Markov regime switching model for dynamic futures hedging

The random coefficient autoregressive Markov regime switching model (RCARRS) for estimating optimal hedge ratios, which generalizes the random coefficient autoregressive (RCAR) and Markov regime switching (MRS) models, is introduced. RCARRS, RCAR, MRS, BEKK‐GARCH, CC‐GARCH, and OLS are compared with the use of aluminum and lead futures data. RCARRS outperforms all models out‐of‐sample for lead and is second only to BEKK‐GARCH for aluminum in terms of variancereduction point estimates. White's data‐snooping reality check null hypothesis of no superiority is rejected for BEKK‐GARCH and RCARRS for aluminum, but not for lead. © 2006 Wiley Periodicals, Inc. Jrl Fut Mark 26:103–129, 2006     

Modeling and Forecasting the Sales of Technology Products

Managers in technology product markets require sales response models that provide substantive insights into the effects of marketing activities as well as reliable sales forecasts. Such markets are characterized by frequent introductions and withdrawals of multiple models by different companies. Thus, the data available on the performance of any individual model is scarce. A second characteristic is that the effects of product attributes and marketing activities could change over time as different types of consumers participate in the market at different points in time. Given sparse data, it becomes critical to specify a model that allows pooling of information across brand-models while at the same time providing brand-model specific parameters. We accomplish this via a hierarchical Bayesian model specification. Further, to capture the effects of changing consumer preferences over time, we specify a time varying parameter model. Our modeling framework therefore, integrates a hierarchical Bayesian model within a time varying parameter framework to develop a dynamic hierarchical Bayesian model. We employ data on digital cameras in the U.S. market to estimate the parameters of our proposed model. We use thirty-three months of national level data on the digital camera market with the data series beginning very close to the inception of this product category. We find that while there is little variation in reliance of benefits by early adopters, the second wave of adopters focus on Ease of Use followed by later adopters who rely on Storage and Image Quality. Looking at the elasticities of demand with respect to the various benefits, we find that at around the halfway point of our data series, the industry as a whole would have been better off investing in increasing image quality rather than storage if costs associated with the two are equal. However, at the end of the time horizon both benefits appear to have about equal impact. Further, the relative benefits of improving these attributes vary across brands and points in time. We then generate single period and multiple period ahead sales forecasts. We make different assumptions about information availability and find that the average (across brand-models and time) MAPE ranges from 7.5 to 14.5% for the model. We provide extensive comparisons of our model with 4 potential alternatives and find that our model outperforms these alternatives on the nature of substantive insights obtained as well as in forecasting out-of-sample especially when there is a very short time window of data.     

Technological Regimes and Firm Survival: Evidence Across Sectors and Over Time

In addition to the usual variables representing firm- and industry-specific features that impact the firm’s survival, this paper uses three R&D related variables to reflect two Schumpeterian technological regimes: creative destruction (the entrepreneurial regime) and creative accumulation (the routinized regime). After controlling for age, size, entry barriers, capital intensity, the profit margin, the concentration ratio, the profit-cost ratio and entry rates, the empirical results confirm the theoretical relationship between technological regimes and the survival rate of new firms: new firms are more likely to survive under the entrepreneurial regime. Moreover, this effect is larger within the younger cohorts of firms than within the older ones.