Time-varying hedge ratios: an application to the Indian stock futures market

This paper investigates time-varying optimal hedge ratios in individual stock futures markets in India. The analysis employs data on individual stock futures from an unexplored but highly traded (both in terms of volume and quantity) emerging market. The hedge ratios derived in this study incorporate mean reversion in volatility, which is an important extension of the bivariate BEKK-GARCH model of Engle and Kroner. This extension generates improved optimal hedge ratios over the traditional BEKK-GARCH model and static error correction type alternatives.

Time varying hedge ratios : an application to the Indian stock futures market

This paper investigates time-varying optimal hedge ratios in individual stock futures markets in India. The analysis employs data on individual stock futures from an unexplored but highly traded (both in terms of volume and quantity) emerging market. The hedge ratios derived in this study incorporate mean reversion in volatility, which is an important extension of the bivariate BEKK-GARCH model of Engle and Kroner. This extension generates improved optimal hedge ratios over the traditional BEKK-GARCH model and static error correction type alternatives.

Distributed power control in cellular mobile radio systems with time-varying link gains

A simple distributed power control algorithm for communication systems with mobile users and unknown time-varying link gains is proposed. We prove that the proposed algorithm is exponentially converging. Furthermore, we show that the algorithm significantly outperforms the well-known Foschini and Miljanic algorithm in the case of quickly moving mobile users.

Distributed power control in cellular mobile radio systems with time-varying link gains

A simple distributed power control algorithm for communication systems with mobile users and unknown timevarying link gains is proposed. We prove that the proposed algorithm is exponentially converging. Furthermore, we show that the algorithm significantly outperforms the well-known
Foschini and Miljanic algorithm in the case of quickly moving mobile users.

Estimation of basis frequencies for time-varying SIMO channels : a second-order method

The complex exponential basis expansion model (CE-BEM) provides an accurate description for the time-varying (TV) channels encountered in mobile communications. Many blind channel identification and equalization approaches based on the CE-BEM require precise knowledge of the basis frequencies of TV channels. Existing methods for basis frequency estimation usually resort to the higher-order statistics of channel outputs and impose strict constraints on the source signal. In this paper, we propose a novel method to estimate the basis frequencies for blind identification and equalization of time-varying single-input multiple-output (SIMO) finite-impulse-response (FIR) channels. The proposed method exploits only the second-order statistics of channel outputs and does not require strong conditions on the source signal. As a result, it exhibits superior performance to the existing basis frequency estimation methods. The validity of our method is demonstrated by numerical simulations.

CM-based blind equalization of time-varying SIMO-FIR channel with single pulsation estimation

It is known that the constant modulus (CM) property of the source signal can be exploited to blindly equalize time-invariant single-inputmultiple-output (SIMO) and finite-impulse-response (FIR) channels. However, the time-invariance assumption about the channel cannot be satisfied in several practical applications, e.g., mobile communication. In this paper, we show that, under some mild conditions, the CM criterion can be extended to the blind equalization of a time-varying channel that is described by the complex exponential basis expansion model (CE-BEM). Although several existing blind equalization methods that are based on the CE-BEM have to employ higher order statistics to estimate all nonzero channel pulsations, the CM-based method only needs to estimate one pulsation using second-order statistics, which yields better estimation results. It also relaxes the restriction on the source signal and is applicable to some classes of signals with which the existing methods cannot deal.

Time-varying herding behavior, global financial crisis, and the Chinese stock market

In this paper, we examine the evidence of herding behavior on the Chinese stock market. Our main findings are as follows. First, we find strong evidence of herding behavior on both the Shanghai and Shenzhen stock exchanges. Second, we document evidence of asymmetric herding behavior with greater magnitude of herding behavior on up markets than on down markets. Third, our findings suggest that herding behavior is sector-specific and predominant in the industrial and properties sectors. Finally, we unravel strong evidence suggesting that herding behavior is time-varying and in some sectors time-varying herding behavior is more prevalent than in other sectors.

Channel estimation for time-varying MIMO relay systems

In this paper, we investigate the channel estimation problem for multiple-input multiple-output (MIMO) relay communication systems with time-varying channels. The time-varying characteristic of the channels is described by the complex-exponential basis expansion model (CE-BEM). We propose a superimposed channel training algorithm to estimate the individual first-hop and second-hop time-varying channel matrices for MIMO relay systems. In particular, the estimation of the second-hop time-varying channel matrix is performed by exploiting the superimposed training sequence at the relay node, while the first-hop time-varying channel matrix is estimated through the source node training sequence and the estimated second-hop channel. To improve the performance of channel estimation, we derive the optimal structure of the source and relay training sequences that minimize the mean-squared error (MSE) of channel estimation. We also optimize the relay amplification factor that governs the power allocation between the source and relay training sequences. Numerical simulations demonstrate that the proposed superimposed channel training algorithm for MIMO relay systems with time-varying channels outperforms the conventional two-stage channel estimation scheme.

A study on time-varying quantile and its applications

This Thesis is the result of my Master Degree studies at the Graduate School of Economics, Getúlio Vargas Foundation, from January 2004 to August 2006. am indebted to my Thesis Advisor, Professor Luiz Renato Lima, who introduced me to the Econometrics' world. In this Thesis, we study time-varying quantile process and we develop two applications, which are presented here as Part and Part II. Each of these parts was transformed in paper. Both papers were submitted. Part shows that asymmetric persistence induces ARCH effects, but the LMARCH test has power against it. On the other hand, the test for asymmetric dynamics proposed by Koenker and Xiao (2004) has correct size under the presence of ARCH errors. These results suggest that the LM-ARCH and the Koenker-Xiao tests may be used in applied research as complementary tools. In the Part II, we compare four different Value-at-Risk (VaR) methodologies through Monte Cario experiments. Our results indicate that the method based on quantile regression with ARCH effect dominates other methods that require distributional assumption. In particular, we show that the non-robust method ologies have higher probability to predict VaRs with too many violations. We illustrate our findings with an empirical exercise in which we estimate VaR for returns of São Paulo stock exchange index, IBOVESPA, during periods of market turmoil. Our results indicate that the robust method based on quantile regression presents the least number of violations.

Brazilian indexing and inertial inflation: evidence from time-varying estimates of an inflation transfer function

This paper is a theoretica1 and empirica1 study of the re1ationship between indexing po1icy and feedback mechanisms in the inflationary adjustment process in Brazil. The focus of our study is on two policy issues: (1) did the Brazilian system of indexing of interest rates, the exchange rate, and wages make inflation so dependent on its own past values that it created a significant feedback process and inertia in the behaviour of inflation in and (2) was the feedback effect of past inf1ation upon itself so strong that dominated the effect of monetary/fiscal variables upon current inflation? This paper develops a simple model designed to capture several "stylized facts" of Brazi1ian indexing po1icy. Separate ru1es of "backward indexing" for interest rates, the exchange rate, and wages, reflecting the evolution of po1icy changes in Brazil, are incorporated in a two-sector model of industrial and agricultural prices. A transfer function derived irom this mode1 shows inflation depending on three factors: (1) past values of inflation, (2) monetary and fiscal variables, and (3) supply- .shock variables. The indexing rules for interest rates, the exchange rate, and wages place restrictions on the coefficients of the transfer function. Variations in the policy-determined parameters of the indexing rules imply changes in the coefficients of the transfer function for inflation. One implication of this model, in contrast to previous results derived in analytically simpler models of indexing, is that a higher degree of indexing does not make current inflation more responsive to current monetary shocks. The empirical section of this paper studies the central hypotheses of this model through estimation of the inflation transfer function with time-varying parameters. The results show a systematic non-random variation of the transfer function coefficients closely synchronized with changes in the observed values of the wage-indexing parameters. Non-parametric tests show the variation of the transfer function coefficients to be statistically significant at the time of the changes in wage indexing rules in Brazil. As the degree of indexing increased, the inflation feadback coefficients increased, while the effect of external price and agricultura shocs progressively increased and monetary effects progressively decreased.

Um modelo "time-varying markov-Switching" para crescimento econômico e algoritmo de estimação

Este trabalho elabora um modelo para investigação do padrão de variação do crescimento econômico, entre diferentes países e através do tempo, usando um framework Markov- Switching com matriz de transição variável. O modelo desenvolvido segue a abordagem de Pritchett (2003), explicando a dinâmica do crescimento a partir de uma coleção de diferentes estados – cada qual com seu sub-modelo e padrão de crescimento – através dos quais os países oscilam ao longo do tempo. A matriz de transição entre os diferentes estados é variante no tempo, dependendo de variáveis condicionantes de cada país e a dinâmica de cada estado é linear. Desenvolvemos um método de estimação generalizando o Algoritmo EM de Diebold et al. (1993) e estimamos um modelo-exemplo em painel com a matriz de transição condicionada na qualidade das instituições e no nível de investimento. Encontramos três estados de crescimento: crescimento estável, ‘milagroso’ e estagnação - virtualmente coincidentes com os três primeiros de Jerzmanowski (2006). Os resultados mostram que a qualidade das instituições é um importante determinante do crescimento de longo prazo enquanto o nível de investimento tem papel diferenciado: contribui positivamente em países com boa qualidade de instituições e tem papel pouco relevante para os países com instituições medianas ou piores.

A time-varying markov-switching model for economic growth

This paper investigates economic growth’s pattern of variation across and within countries using a Time-Varying Transition Matrix Markov-Switching Approach. The model developed follows the approach of Pritchett (2003) and explains the dynamics of growth based on a collection of different states, each of which has a sub-model and a growth pattern, by which countries oscillate over time. The transition matrix among the different states varies over time, depending on the conditioning variables of each country, with a linear dynamic for each state. We develop a generalization of the Diebold’s EM Algorithm and estimate an example model in a panel with a transition matrix conditioned on the quality of the institutions and the level of investment. We found three states of growth: stable growth, miraculous growth, and stagnation. The results show that the quality of the institutions is an important determinant of long-term growth, whereas the level of investment has varying roles in that it contributes positively in countries with high-quality institutions but is of little relevance in countries with medium- or poor-quality institutions.