Joann Jasiak
York University
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Featured researches published by Joann Jasiak.
Journal of Forecasting | 2006
Christian Gourieroux; Joann Jasiak
We introduce a class of autoregressive gamma processes with conditional distributions from the family of noncentred gamma (up to a scale factor). The paper provides the stationarity and ergodicity conditions for ARG processes of any autoregressive order p , including long memory, and closed-form expressions of conditional moments. The nonlinear state space representation of an ARG process is used to derive the filtering, smoothing and forecasting algorithms. The paper also presents estimation and inference methods, illustrated by an application to interquote durations data on an infrequently traded stock listed on the Toronto Stock Exchange (TSX). Copyright
Economics Letters | 2001
Christian Gourieroux; Joann Jasiak
Abstract We study how processes with infrequent regime switching may generate a long memory effect in the autocorrelation function. In such a case, the use of a strong fractional I(d) model for economic or financial analysis may lead to spurious results.
Social Science Research Network | 1999
Joann Jasiak
This paper examines long-term dependence in times between trades on financial markets. The autocorrelation functions of several intertrade duration series show a slow, hyperbolic rate of decay typical for long memory processes. For example, a shock to times between trades of the Alcatel stock on the Paris Stock Exchange (SBF Paris Bourse) may persist in the transactions time for a long period of 1000 or 2000 ticks. With an average duration of 52 seconds between transactions this may amount to sixteen or thirty two hours in calendar time. This paper introduces a fractionally integrated autoregressive conditional duration (FIACD) model for intertrade duration series. It also examines transformed duration processes representing times between consecutive returns to states of null, positive or negative returns. This approach captures the relationship between the duration persistence and return dynamics. The times elapsed between returns to various states feature very similar autocorrelation patterns and do not possess the long memory property. The persistence in durations is also determined by the times spent within specific states of returns. The average visiting time is state dependent, features intraday variation and may be considered as an instantaneous measure of state persistence. The long memory patterns are examined in data on the Alcatel and IBM stocks traded on the SBF Paris Bourse and NYSE.
Journal of Time Series Analysis | 2006
Serge Darolles; Christian Gourieroux; Joann Jasiak
This paper presents a new general class of compound autoregressive (Car) models for non-Gaussian time series. The distinctive feature of the class is that Car models are specified by means of the conditional Laplace transforms. This approach allows for simple derivation of the ergodicity conditions and ensures the existence of forecasting distributions in closed form, at any horizon. The last property is of particular interest for applications to finance and economics that investigate the term structure of variables and/or of their nonlinear transforms. The Car class includes a number of time-series models that already exist in the literature, as well as new models introduced in this paper. Their applications are illustrated by examples of portfolio management, term structure and extreme risk analysis.
Journal of Empirical Finance | 2008
Dingan Feng; Christian Gourieroux; Joann Jasiak
Information on the expected changes in credit quality of obligors is contained in credit migration matrices which trace out the movements of firms across ratings categories in a given period of time and in a given group of bond issuers. The rating matrices provided by Moody’s, Standard &Poor’s and Fitch became crucial inputs to many applications, including the assessment of risk on corporate credit portfolios (CreditVar) and credit derivatives pricing. We propose a factor probit model for modeling and prediction of credit rating matrices that are assumed to be stochastic and driven by a latent factor. The filtered latent factor path reveals the effect of the economic cycle on corporate credit ratings, and provides evidence in support of the PIT (point-in-time) rating philosophy. The factor probit model also yields the estimates of cross-sectional correlations in rating transitions that are documented empirically but not fully accounted for in the literature and in the regulatory rules established by the Basle Committee.
Handbook of Financial Econometrics: Tools and Techniques | 2010
Christian Gourieroux; Joann Jasiak
Publisher Summary This chapter is a survey of literature on the management, supervision, and measurement of extreme and infrequent risks in finance. Extreme risks are the risks of very large losses per dollar invested. As losses associated to extreme risks occur infrequently, investors tend to become less alert to these risks over time. A series of bank failures, due to mismanaged portfolios of corporate loans, real estate, and complex derivatives, was a painful reminder of the existence of extreme risks. It prompted new regulations and research on new instruments of risk protection. The implementation of the common guidelines for risk supervision was a very ambitious initiative designed to address a variety of risks. These risks can be divided into three categories: market risk, credit risk or risk of default, and liquidity risk or risk of counterparty. In 1995, the governors of Central Banks gathered in Basle (Switzerland) and adopted a mandatory risk measure called the Value at Risk to be calculated by all banks for each line of their balance sheets. Since then, banks have been required to report the Value at Risk to the regulators and update it daily and hold a sufficient amount of capital (the so-called required capital) as a hedge against extreme risks.
Economics Books | 2011
Christian Gourieroux; Joann Jasiak
The individual risks faced by banks, insurers, and marketers are less well understood than aggregate risks such as market-price changes. But the risks incurred or carried by individual people, companies, insurance policies, or credit agreements can be just as devastating as macroevents such as share-price fluctuations. A comprehensive introduction, The Econometrics of Individual Risk is the first book to provide a complete econometric methodology for quantifying and managing this underappreciated but important variety of risk. The book presents a course in the econometric theory of individual risk illustrated by empirical examples. And, unlike other texts, it is focused entirely on solving the actual individual risk problems businesses confront today. Christian Gourieroux and Joann Jasiak emphasize the microeconometric aspect of risk analysis by extensively discussing practical problems such as retail credit scoring, credit card transaction dynamics, and profit maximization in promotional mailing. They address regulatory issues in sections on computing the minimum capital reserve for coverage of potential losses, and on the credit-risk measure CreditVar. The book will interest graduate students in economics, business, finance, and actuarial studies, as well as actuaries and financial analysts.
Journal of Time Series Analysis | 2016
Christian Gourieroux; Joann Jasiak
This article discusses filtering, prediction and simulation in univariate and multivariate noncausal processes. A closed‐form functional estimator of the predictive density for noncausal and mixed processes is introduced that provides prediction intervals up to a finite horizon H. A state‐space representation of a noncausal and mixed multivariate vector autoregressive process is derived in two ways‐by the partial fraction decomposition or from the real Jordan canonical form. A recursive BHHH algorithm for the maximization of the approximate log‐likelihood function is proposed, which calculates the filtered values of the unobserved causal and noncausal components of the process. The new methods are illustrated by a simulation study involving a univariate noncausal process with infinite variance.
Annals of economics and statistics | 2005
Christian Gourieroux; Joann Jasiak
This paper introduces impulse response analysis for nonlinear processes based on the concept of nonlinear innovation. Our approach borrows from the traditional linear impulse response analysis in that we consider shocks to innovations of a process. It also extends the methods of nonlinear impulse response analysis proposed earlier in the literature, in that it eliminates the problem of serial correlation of error terms, allows to examine permanent shocks, i.e. shocks occurring repeatedly in time, and provides straightforward interpretation of transitory or symmetric shocks. In our approach, the impulse responses are represented by the joint distribution of the perturbed and unperturbed paths. The analysis can be applied to processes such as the popular GARCH, or ACD models, and can be used to study shock sensitivity of dynamic financial strategies. As an illustration, we show how impulse responses can determine the Value at Risk and the minimum capital requirement under a dynamic portfolio management.
Journal of Time Series Analysis | 2001
Christian Gourieroux; Joann Jasiak
We consider nonlinear state‐space models, where the state variable (ζt) is Markov, stationary and features finite dimensional dependence (FDD), i.e. admits a transition function of the type: π(ζt|ζt−1) =π(ζt)a′(ζt)b(ζt−1), where π(ζt) denotes the marginal distribution of ζt, with a finite number of cross‐effects between the present and past values. We discuss various characterizations of the FDD condition in terms of the predictor space and nonlinear canonical decomposition. The FDD models are shown to admit explicit recursive formulas for filtering and smoothing of the observable process, that arise as an extension of the Kitagawa approach. The filtering and smoothing algorithms are given in the paper. JEL. C4.