Forecasting Intraday Volatility and Value-at-Risk with High-Frequency Data

In this paper, we develop modeling tools to forecast Value-at-Risk and volatility with investment horizons of less than one day. We quantify the market risk based on the study at a 30-min time horizon using modified GARCH models. The evaluation of intraday market risk can be useful to market participants (day traders and market makers) involved in frequent trading. As expected, the volatility features a significant intraday seasonality, which motivates us to include the intraday seasonal indexes in the GARCH models. We also incorporate realized variance (RV) and time-varying degrees of freedom in the GARCH models to capture more intraday information on the volatile market. The intrinsic tail risk index is introduced to assist with understanding the inherent risk level in each trading time interval. The proposed models are evaluated based on their forecasting performance of one-period-ahead volatility and Intraday Value-at-Risk (IVaR) with application to the 30 constituent stocks. We find that models with seasonal indexes generally outperform those without; RV can improve the out-of-sample forecasts of IVaR; student GARCH models with time-varying degrees of freedom perform best at 0.5 and 1 % IVaR, while normal GARCH models excel for 2.5 and 5 % IVaR. The results show that RV and seasonal indexes are useful to forecasting intraday volatility and Intraday VaR.     

New evidence on the first financial bubble

The Mississippi Bubble, South Sea Bubble and the Dutch Windhandel of 1720 together represent the world's first global financial bubble. We hand-collect cross-sectional price data and investor account data from 1720 to test theories about market bubbles. Our tests suggest that innovation was a key driver of bubble expectations. We present evidence against the currently prevailing debt-for-equity conversion hypothesis and relate stock returns to innovations in Atlantic trade and insurance. We find evidence consistent with the innovation-driven bubble dynamics documented by Pastor and Veronesi (2009) for new economy stocks. Our evidence seems inconsistent with clientele-based theories that emphasize bubble-riding and short-sales restrictions.     

A FRAMEWORK FOR ANALYZING CHANGES IN STRATEGIC PERFORMANCE

In this paper we present a framework for analyzing changes in strategic performance. Traditional measures for comparing the strategic performance across firms or over time have been return on investment (ROI) and its component ratio, return on sales (ROS). We decompose the ROS ratio into four separate ratios that capture the impact of changes in a firm's productivity, price recovery, product mix and capacity utilization on its profitability. These ratios help to highlight the micro sources of strategic success or failure. They can be used to assess changes in the performance of a firm compared to itself over time, or to other firms in its industry group. This framework can also be used to evaluate changes in the dynamic performance of an industry as a whole. We illustrate the use of these ratios with a 4-year analysis of the performance of a large manufacturing company. We also demonstrate how the technique can be applied to an industry with an evaluation of the performance of U.S. telecommunications firms between 1975 and 1987, a period during which the industry experienced a progressive increase in competitive pressure.     

Boundedly complete families which are not complete

Summary Completeness of a family of probability distributions implies its bounded completeness but not conversely. An example of a family which is boundedly complete but not complete was presented by Lehmann and Scheffe [5]. This appears to be the only such example quoted in the statistical literature. The purpose of this note is to provide further examples of this type. It is shown that any given family of power series distributions can be used to construct a class containing infinitely many boundedly complete, but not complete, families. Furthermore, it is shown that the family of continuous distributions , is boundedly complete, but not complete, whereU denotes the uniform distribution on [a, b] and {P _ϑ,ϑ ∈ IR}, is a translation family generated by a distributionP ₀ with mean value zero, which is continuous with respect to the Lebesgue measure.     

On the System Setup and the Scheduling Problem in a Flexible Manufacturing System (FMS)

In this paper we present a model which unifies several existing models with respect to two phases of the planning process of a Flexible Manufacturing System. These phases are the system setup phase and the scheduling phase and in literature they usually are considered separately. We give a mathematical formulation encompassing both phases. From this formulation several existing approaches can be deduced. We also describe some heuristic methods for our model and present the computational results.     

Affirmative action: Federal support,supreme court decisions,and human resource management

This article explores the historical background of affirmative action in the United States, and reviews haw federal support for it has changed since the Civil War. Several recent US Supreme Court cases deciding affirmative action issues are discussed, with speculation about the future of affirmative action in light of these decisions. The article also discusses the implications of these rulings for human resource practitioners.     

Regression with errors in variables: estimators based on third order moments

In this paper consistent and, in a well–defined sense, optimal moment–estimators of the regression coefficient in a simple regression model with errors in variables are derived. The asymptotic variance and other asymptotic properties of these estimators are given. As is known for a long time, serious estimation problems exist in this model. There are two ways out of this problem: using either additional assumptions or additional information in the data. A lot of attention has been paid to the use of additional assumptions. However, quite often this leads to rather unrealistic models. In this paper we use additional information in the data. That means here that, besides first and second order moments, third order moments are formulated as functions of the model parameters. Besides theoretical derivations a small study with generated data is discussed. This study shows that for samples larger than 50 the estimates we consider behave nicely.     

Residential development in an open city

This paper investigates the residential development process using a dynamic model of an open city. The model is used to analyze how the density and timing of development at various sites respond to changes in exogenous trajectories and functions. In particular, the impact of income and equilibrium utility trajectory variations as well as transportation and construction cost changes are evaluated for the general model with no specific functional forms assumed. Sufficient conditions for predicted responses are derived and interpreted.     

On a Morgenstern-type bivariate gamma distribution

Summary In this paper a Morgenstern-type bivariate gamma distribution has been studied. Its moment generating function has been derived. The distribution of the product and quotient are derived in terms of the modified Bessel function. The results for the independent case follow as special cases. Further the regression function has been analysed, in terms of its deviation from linear regression function.This research was initiated while the first author was a U.N. Consultant under the Statistical Training Program for Africa, visiting the University of Ghana.