Dr. Hinich's Table of Contents
     in chronological order

• Detecting intraday periodicities with application to high frequency exchange rates
  by Chris Brooks & Melvin J. Hinich
  Appl. Statist. (2006)
  55, Part 2, pp. 241–259
   
• Randomly Modulated Periodic Signals in Alberta's Electricity Market
  by Melvin J. Hinich & Apostolos Serletis
  May 17, 2006
   
• Episodic Nonlinearity and Nonstationarity in Alberta's Power and Natural Gas Markets
  by Danny Czamanski, Paul Dormaar, Melvin J. Hinich, & Apostolos Serletis
  April 29, 2005
   
• Non−linear Market Behavior: Events Detection in the Malaysian Stock Market
  by Kian-Ping Lim & Melvin J. Hinich
  Economics Bulletin, Vol. 7, No. 6 pp. 1−5
  July 22, 2005
   
• Statistical Inadequacy of GARCH Models for Asian Stock Markets: Evidence and Implications
  by Kian-Ping Lim, Melvin J. Hinich, & Venus Khim-Sen Liew
  JOURNAL OF EMERGING MARKET FINANCE, 4:3 (2005)
   
• Episodic Nonlinear Event Detection in the Canadian Exchange Rate
  by Melvin J. Hinich & Apostolos Serletis
  October 28, 2003
   
• Intraday Patterns in the Returns, Bid-ask Spreads, and Trading Volume of Stocks Traded on the New York Stock Exchange
  by Chris Brooks, Melvin J. Hinich, & Douglas M. Patterson
  October 2003
   
• Risk when Some States are Low Probability Events
  by Melvin J. Hinich
  Macroeconomic Dynamics 7, 636-643 (2003)
   
• Cross-correlations and cross-bicorrelations in Sterling exchange rates
  by Chris Brooks & Melvin J. Hinich
  Journal of Empirical Finance 6 (1999) 385–404
   
• Cross-Temporal Universality of Non-Linear Serial Dependencies: Evidence from Asian Stock Indices
  by Kian-Ping Lim & Melvin J. Hinich
   
• Episodic Nonlinearity in Latin American Stock Market Indices
  by Claudio A. Bonilla, Melvin J. Hinich, & Rafael Romero-Meza
   
• GARCH Inadequacy for Modelling Exchange Rates: Empirical Evidence from Latin America
  by Claudio A. Bonilla, Rafael Romero-Meza, & Melvin J. Hinich
   
• Model Identification Of ARCH/GARCH Using Non-Linearity Tests
  An Application On Asean-5 Foreign Exchange Markets
  by K.P. Lim, M. Azali, & Melvin J. Hinich
   
• STRUCTURAL CHANGE IN MACROECONOMIC TIME SERIES: A COMPLEX SYSTEMS PERSPECTIVE
  by Melvin J. Hinich, John Foster, & Phillip Wild
  
   

Detecting intraday periodicities with application to high frequency exchange rates
by Chris Brooks & Melvin J. Hinich
Cass Business School, London, UK & University of Texas at Austin, USA
Appl. Statist. (2006)
55, Part 2, pp. 241–259

Abstract
Many recent papers have documented periodicities in returns, return volatility, bid–ask spreads and trading volume, in both equity and foreign exchange markets. We propose and employ a new test for detecting subtle periodicities in time series data based on a signal coherence function.The technique is applied to a set of seven half-hourly exchange rate series. Overall, we find the signal coherence to be maximal at the 8-h and 12-h frequencies. Retaining only the most coherent frequencies for each series, we implement a trading rule that is based on these observed periodicities. Our results demonstrate in all cases except one that, in gross terms, the rules can generate returns that are considerably greater than those of a buy-and-hold strategy, although they cannot retain their profitability net of transactions costs.We conjecture that this methodology could constitute an important tool for financial market researchers which will enable them to detect, quantify and rank the various periodic components in financial data better.

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Randomly Modulated Periodic Signals in Alberta's Electricity Market
by Melvin J. Hinich & Apostolos Serletis
The University of Texas at Austin & University of Calgary
May 17, 2006

Abstract
This paper uses hourly electricity prices and MW hour demand for Alberta, Canada over the deregulated period after 1996 to test for randomly modulated periodicity. In doing so, we apply the signal coherence spectral analysis to the time series of hourly spot prices and megawatt-hours (MWh) demand from 1/1/1996 to 12/7/2003 using the FORTRAN 95 program developed by Hinich (2000). We detect relatively steady weekly and daily cycles in demand but very unstable cycles in prices.

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Episodic Nonlinearity and Nonstationarity in Alberta's Power and Natural Gas Markets
by Danny Czamanski, Paul Dormaar, Melvin J. Hinich, & Apostolos Serletis
April 29, 2005

Abstract
This paper uses a new method of testing for linear and nonlinear lead/lag relationships between time series, introduced by Brooks and Hinich (1999), on Albertas natural gas and power markets. The test, based on the concepts of cross-correlation and cross-bicorrelation, is used after pre-whitening of the data to test for the existence of residual nonlinearity as well as the episodic nature of the nonlinearity. Our evidence points to a relatively rare episodic nonlinearity within and across the two series, having important implications for forecasting these series.

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Non−linear Market Behavior: Events Detection in the Malaysian Stock Market
by Kian-Ping Lim & Melvin J. Hinich
Universiti Malaysia Sabah & University of Texas at Austin
Economics Bulletin, Vol. 7, No. 6 pp. 1−5
July 22, 2005

Abstract
This paper advocates a reverse from of event studies that is data−dependent to determine endogeneously the events that trigger non−linear market behavior. Using the Malaysian stock market as our case study, coupled with the ‘windowing’ approach proposed by Hinich and Patterson (1995), the present study is able to identify major political and economic events that contributed to the short bursts of non−linear behavior. The present framework can be extended to individual firm to examine the adjustment of its stock price to firm−specific events, which will provide deeper insight into issues on corporate finance.

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Statistical Inadequacy of GARCH Models for Asian Stock Markets: Evidence and Implications
by Kian-Ping Lim, Melvin J. Hinich, & Venus Khim-Sen Liew
JOURNAL OF EMERGING MARKET FINANCE, 4:3 (2005)

Abstract
This study employs the Hinich portmanteau bicorrelation test (Hinich 1996; Hinich and Patterson 1995) as a diagnostic tool to determine the adequacy of Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models for eight Asian stock markets. The bicorrelation test results demonstrate that this type of model cannot provide an adequate characterisation for the underlying process of all the selected Asian stock markets. Further investigation using the windowed test procedure reveals that the violation of the covariance stationarity assumption as required by the GARCH process is due to the presence of transient epochs of dependencies in the data. The inadequacy of GARCH models has strong implications for the pricing of stock index options, portfolios selection, development of optimal hedging techniques and risk management.

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Episodic Nonlinear Event Detection in the Canadian Exchange Rate
by Melvin J. Hinich & Apostolos Serletis
University of Texas at Austin and University of Calgary
October 28, 2003

Abstract
This paper uses daily observations for the Canadian dollar - U.S. dollar nominal exchange rate over the recent flexible exchange rate period and a new statistical technique, recently developed by Hinich (1996), to detect major political and economic events that have affected the exchange rate.

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Intraday Patterns in the Returns, Bid-ask Spreads, and Trading Volume of Stocks Traded on the New York Stock Exchange
by Chris Brooks, Melvin J. Hinich, & Douglas M. Patterson
Cass Business School, University of Texas at Austin, & Virginia Tech
October 2003

Abstract
Much research has demonstrated the existence of patterns in high-frequency equity returns, return volatility, bid-ask spreads and trading volume. In this paper, we employ a new test for detecting periodicities based on a signal coherence function. The technique is applied to the returns, bid-ask spreads, and trading volume of thirty stocks traded on the NYSE. We are able to confirm previous findings of an inverse J-shaped pattern in spreads and volume through the day. We also demonstrate that such intraday effects dominate day of the week seasonalities in spreads and volumes, while there are virtually no significant periodicities in the returns data. Our approach can also leads to a natural method for forecasting the time series, and we find that, particularly in the case of the volume series, the predictions are considerably more accurate than those from naïve methods.

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Risk when Some States are Low Probability Events
by Melvin J. Hinich
University of Texas at Austin
Macroeconomic Dynamics 7, 636-643 (2003)

Abstract
One usually assumes that the joint probability distribution is known or that agents will use Bayesian updating to estimate the true probabilities after a number of trials when the states of nature are finite in classical decision theory under uncertainty. If there are important states that have very low probabilities of occurrence then each agent must make a subjective assessment of the probability distribution until a sufficient number of outcomes are observed in order to generate a precise estimate of the probability distribution. If one assumes that all agents know the states and their payoffs, the probability distribution is stationary, and they observe all outcomes that unfold over time, it will take at least ten times the mean time between occurrences of the lowest probability event in order to generate enough outcomes so that all agents share the same objective knowledge of the distribution. The mean time of recurrence depends on both the probability distribution and the time unit used between recordings of the observations. The relationship between the mean time to obtain a sufficient sample of observed rare states and the precision of the estimation of the state probabilities has been overlooked in the application of Bayesian learning procedures. It will be shown that
subjectivity persists when agents must employ Bayesian updating of the subjective probabilities in decision problems when low probability states are possible. If at least one state has a low probability of occurrence then the time to convergence will exceed the period of stationarity of the process The importance of the time unit in the convergence to precise estimates of the likelihood of the rare events is an important aspect of modeling in financial markets.

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Cross-correlations and cross-bicorrelations in Sterling exchange rates
by Chris Brooks & Melvin J. Hinich
Journal of Empirical Finance 6 (1999) 385–404

Abstract
This paper proposes two new tests for linear and nonlinear leadrlag relationships between time series based on the concepts of cross-correlations and cross-bicorrelations, respectively. The tests are then applied to a set of Sterling-denominated exchange rates. Our analysis indicates that there existed periods during the post-Bretton Woods era where the temporal relationship between different exchange rates was strong, although these periods have become less frequent over the past 20 years. In particular, our results demonstrate the episodic nature of the nonlinearity, and have implications for the speed of flow of information between financial series. The method generalises recently proposed tests for nonlinearity to the multivariate context. © 1999 Elsevier Science B.V. All rights reserved.

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Cross-Temporal Universality of Non-Linear Serial Dependencies: Evidence from Asian Stock Indices
by Kian-Ping Lim & Melvin J. Hinich
Universiti Malaysia Sabah & University of Texas at Austin

Abstract
This study utilizes the Hinich portmanteau bicorrelation test in conjunction with the windowed testing procedure to examine the cross-temporal universality of non-linear serial dependencies in the returns for Asian stock market indices. As a whole, the results reveal that the non-linear serial dependencies do not appear to be persistent across time for all the markets.

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Episodic Nonlinearity in Latin American Stock Market Indices
by Claudio A. Bonilla, Melvin J. Hinich, & Rafael Romero-Meza
Universidad del Desarrollo, The University of Texas at Austin, & Universidad de Chile

Abstract
This letter applies the Hinich portmanteau bicorrelation test jointly with the windowed testing procedure to detect nonlinear behavior in the rate of returns series for seven Latin American stock market indices. Our results suggest that the nonlinear serial dependencies are episodic in nature. All the stock returns series are characterized by few brief periods of highly significant nonlinearity, followed by long time periods in which the returns follow a pure noise process. Our findings help explain why there are difficulties in forecasting asset returns.

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GARCH Inadequacy for Modelling Exchange Rates: Empirical Evidence from Latin America
by Claudio A. Bonilla, Rafael Romero-Meza, & Melvin J. Hinich
University of Chile & The University of Texas at Austin

Abstract
This paper checks for the adequacy of using GARCH models in exchange rate series. Using the Hinich portmanteau bicorrelation test, we find that a GARCH formulation or any of its variants fails to capture the data generating process of the main Latin American exchange rates. Our results highlight the potential of having misleading public policy when estimates are based in GARCH types of models. This paper also complements recent similar findings encountered in European and Asian economies.

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Model Identification Of ARCH/GARCH Using Non-Linearity Tests
An Application On Asean-5 Foreign Exchange Markets
by K.P. Lim, M. Azali, & Melvin J. Hinich

Abstract
This study provides an alternative framework for non-linear model identification / diagnostics by using a battery of non-linearity tests, with an application on the ASEAN-5 foreign exchange markets. To achieve that end, the differing power of the Brock-Dechert-Scheinkman (BDS) test and Hinich bispectrum test are utilized for they provide valuable identification information on the adequacy of the current framework of ARCH/GARCH models in capturing the non-linear dynamics in ASEAN-5 currencies. The results from the BDS test indicate strong evidences of non-linearity. However, this conveys little information on the nature of the detected non-linearity since the BDS test has high power against vast class of alternatives. Further application of the Hinich bispectrum test provides valuable non-linear identification information, in which the results provide strong evidences against the adequacy of the ARCH/GARCH models in explaining the non-linear dynamics in ASEAN-5 exchange rate returns series.

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STRUCTURAL CHANGE IN MACROECONOMIC TIME SERIES: A COMPLEX SYSTEMS PERSPECTIVE
by Melvin J. Hinich, John Foster, & Phillip Wild
University of Texas at Austin & University of Queensland

Abstract
We demonstrate that the process of generating smooth transitions can be viewed as a natural result of the filtering operations implied in the generation of discrete time series observations from the sampling of data from an underlying continuous time process that has undergone a process of structural change. In order to focus discussion, we utilize the problem of estimating the location of abrupt shifts in some simple time series models. This approach will permit us to address salient issues relating to distortions induced by the inherent aggregation associated with discrete-time sampling of continuous time processes experiencing structural change. We also address the issue of how time irreversible structures may be generated within the smooth transition processes.

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