Joann Jasiak

Joann Jasiak
York University · Department of Economics

PhD

About

110
Publications
12,380
Reads
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2,172
Citations
Citations since 2017
36 Research Items
548 Citations
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2017201820192020202120222023020406080100
2017201820192020202120222023020406080100
2017201820192020202120222023020406080100
Introduction
My website at econ.yorku.ca has been shut down. For working papers and references go to www.jjstats.com.

Publications

Publications (110)
Article
Full-text available
This paper introduces a representation theorem for a mixed VAR(p) process by distinguishing its causal and noncausal components. That representation is used to discuss the advantages and limitations of second-order identification in a mixed VAR. We show that it is possible to find the numbers of causal or noncausal components of the process from it...
Article
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This paper examines noncausal order misspecification in noncausal and mixed processes. We consider the constrained maximum likelihood (ML) estimators of autoregressive parameters obtained when noncausal order s is fixed and potentially different from the true order s0. The effect of such noncausal order misspecification on the constrained ML estima...
Article
Full-text available
The martingale hypothesis is commonly tested in financial and economic time series. The existing tests of the martingale hypothesis aim at detecting some aspects of nonstationarity, which is considered an inherent feature of a martingale process. However, there exists a variety of martingale processes, some of which are nonstationary like the well-...
Preprint
Full-text available
We consider a multivariate system Y t = AX t , where the unobserved components (the sources) X t are independent AR(1) processes and the number of sources is larger than the number of observed outputs (undetermined system). We demonstrate that the mixing matrix A, the autoregressive coefficients and the distributions of sources can be identified. T...
Article
Full-text available
This paper examines the performance of nonlinear short‐term forecasts of noncausal processes from closed‐form functional predictive density estimators. The processes considered have mixed causal‐noncausal MAR(1,1) dynamics and non‐Gaussian distributions with either finite or infinite variance. The quality of point forecasts is affected by spikes an...
Preprint
Full-text available
As Canada and other major countries investigate implementing "digital money" or Central Bank Digital Currencies (CBDC), important questions need to be answered relating to the effect of demographic and geographic factors on the population's digital literacy. This paper uses the Canadian Internet Use Survey (CIUS) 2020 and survey versions of Lasso i...
Preprint
Full-text available
This paper examines the dynamics of Tether, the stablecoin with the largest market capitalization. We show that the distributional and dynamic properties of Tether/USD rates have been evolving from 2017 to 2021. We use local analysis methods to detect and describe the local patterns, such as short-lived trends, time-varying volatility and persisten...
Preprint
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This paper considers nonlinear dynamic models where the main parameter of interest is a nonnegative matrix characterizing the network (contagion) effects. This network matrix is usually constrained either by assuming a limited number of nonzero elements (sparsity), or by considering a reduced rank approach for nonnegative matrix factorization (NMF)...
Article
We consider a class of semi-parametric dynamic models with independent identically distributed errors, including the nonlinear mixed causal-noncausal Vector Autoregressive (VAR), Double-Autoregressive (DAR) and stochastic volatility models. To estimate the parameters characterizing the (nonlinear) serial dependence, we introduce a generic Generaliz...
Article
We consider a multivariate system Yt = AXt, where the unobserved components Xt are independent AR(1) processes and the number of sources is greater than the number of observed outputs. We show that the mixing matrix A, the AR(1) coefficients and distributions of Xt can be identified (up to scale factors of Xt), which solves the dynamic deconvolutio...
Preprint
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We introduce the closed-form formulas of nonlinear forecasts and non-linear impulse response functions (IRF) for the mixed causal-noncausal (Structural) Vector Autoregressive (S)VAR models. We also discuss the identification of nonlinear causal innovations of the model to which the shocks are applied. Our approach is illustrated by a simulation stu...
Article
New flexible-form and semi-parametric autoregressive non-linear count models for panel data are developed to analyse the spread and containment of the COVID-19 pandemic. The models are based on a discrete time form of the SIR model. These methods lead naturally to estimators of the infection process and daily reproduction numbers by jurisdiction. T...
Article
This paper examines the individual records of patients treated for COVID‐19 during the early phase of the pandemic in Ontario. We trace out daily transitions of patients through medical care of different intensity and address the right truncation in the database. We also examine the sojourn times and reveal duration dependence in the treatments for...
Preprint
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This paper introduces a local-to-unity/small sigma process for a stationary time series with strong persistence and non-negligible long run risk. This process represents the stationary long run component in an unobserved short- and long-run components model involving different time scales. More specifically, the short run component evolves in the c...
Preprint
We introduce the Maximum Composite Likelihood (MCL) estimation method for the stochastic factor ordered Probit model of credit rating transitions of firms. This model is recommended to banks and financial institutions as part of internal credit risk assessment procedures under the Basel III regulations. However, its exact likelihood function involv...
Preprint
Full-text available
The parametric estimators applied by rolling are commonly used in the analysis of time series with nonlinear features, such as structural change due to time varying parameters and local trends. This paper examines the properties of rolling estimators in the class of Temporally Local Maximum Likelihood (TLML) estimators. We study the TLML estima-tor...
Preprint
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We consider a class of semi-parametric dynamic models with strong white noise errors. This class of processes includes the standard Vector Autoregressive (VAR) model, the nonfundamental structural VAR, the mixed causal-noncausal models, as well as nonlinear dynamic models such as the (multivariate) ARCH-M model. For estimation of processes in this...
Article
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A considerable number of individuals infected by COVID-19 died in self-isolation. This paper uses a graphical inference method to examine if patients were endogenously assigned to self-isolation during the early phase of COVID-19 epidemic in Ontario. The endogeneity of patient assignment is evaluated from a dependence measure revealing relationship...
Article
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We introduce new methods of filtering and forecasting for the causal‐noncausal convolution model. This model represents the dynamics of stationary processes with local explosions, such as spikes and bubbles, which characterise the time series of commodity prices, cryptocurrency exchange rates and other financial and macroeconomic variables. The con...
Article
Full-text available
A major difficulty in the analysis of Covid-19 transmission is that many infected individuals are asymptomatic. For this reason, the total counts of infected individuals and of recovered immunized individuals are unknown, especially during the early phase of the epidemic. In this paper, we consider a parametric time varying Markov process of Corona...
Preprint
Full-text available
A considerable number of individuals infected by COVID-19 died in self-isolation. This paper uses a graphical inference method to examine if patients were endogenously assigned to self-isolation during the early phase of COVID-19 epidemic in Ontario. The endogeneity of patient assignment is evaluated from a dependence measure revealing relationship...
Preprint
Full-text available
The growing literature on the propagation of COVID-19 relies on various dynamic SIR-type models (Susceptible-Infected-Recovered) which yield model-dependent results. For transparency and ease of comparing the results, we introduce a common representation of the SIR-type stochastic epidemiological models. This representation is a discrete time trans...
Preprint
Full-text available
A major difficulty in the analysis of propagation of the coronavirus is that many infected individuals show no symptoms of Covid-19. This implies a lack of information on the total counts of infected individuals and of recovered and immunized individuals. In this paper, we consider parametric time varying Markov processes of Coronavirus propagation...
Article
Full-text available
A linear rational expectation model with current expectations admits a unique linear stationary dynamic equilibrium only under specific restrictions on the parameter values. This paper shows that, in general, there is a multiplicity of stationary dynamic equilibria due to the existence of nonlinear stationary equilibria. These nonlinear stationary...
Preprint
Full-text available
This paper introduces a new approach to the modelling of a stationary long run component, which is an autoregressive process with near unit root and small sigma innovation. We show that a combination of a noise and a long run component can explain the long run predictability puzzle pointed out in Fama-French (1988). Moreover in the presence of a lo...
Preprint
Full-text available
This paper examines the performance of nonlinear short-term forecasts of noncausal processes from closed-form functional predictive density estimators. The processes considered have mixed causal-noncausal MAR(1,1) dynamics and non-Gaussian distributions with either finite or infinite variance. The forecast assessments are based on the forecast erro...
Preprint
Full-text available
We consider a multivariate system $Y_t = A X_t$, where the unobserved components (the sources) $X_t$ are independent AR(1) processes and the number of sources is larger than the number of observed outputs (undetermined system). We demonstrate that the mixing matrix $A$, the autoregressive coefficients and the distributions of the sources can be ide...
Research
Full-text available
This paper examines the performance of nonlinear forecasts of noncausal processes from closed-form functional predictive density estimators. The processes considered have the mixed causal-noncausal MAR(1,1) dynamics and various non-Gaussian distributions with finite and infinite variance. The forecasts are assessed based on the forecast error behav...
Preprint
Full-text available
The martingale hypothesis is commonly tested in financial and economic time series. The existing tests of the martingale hypothesis aim at detecting some aspects of nonstationarity, which is considered an inherent feature of a mar-tingale process. However, there exists a variety of martingale processes, some of which are nonstationary like the well...
Data
Full-text available
Presentation
Full-text available
presents the working paper with the same title
Article
We derive a coherent multifactor model for pricing various derivatives written on the same underlying (potentially nontradable) asset. We show the difference between a case in which the underlying asset is self-financed and tradable and a case in which it is not. In the first case, an additional arbitrage condition must be introduced, which implies...
Research
Full-text available
This paper introduces consistent semi-parametric estimation methods for mixed causal/noncausal multivariate non-Gaussian processes. We show that in the VAR(1) model, the second-order identification is feasible to some limited extent, contrary to the common belief that non-Gaussian processes are not second-order identifiable. In general, in the mixe...
Research
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This paper examines the consequences of estimating a (past-dependent) causal AR model from data generated by a stationary (future-dependent) noncausal process. We show that the outcomes of that estimation depend on the noncausal persistence as follows: When the noncausal persistence is strong, the (pseudo)-ML estimator of the misspecified causal mo...
Conference Paper
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This paper presents the methodds of filtering, prediction and simulation for univariate and multivariate noncausal processes. A closed-form functional estimator of the predictive density of noncausal and mixed processes is introduced for forecasting up to a finite horizon H. A state space representation of a noncausal and mixed multivariate VAR pro...
Article
We derive a coherent multi-factor model to price various derivatives such as forwards, futures and European options written on a same underlying asset which is potentially non-tradable. We consider both cases when the underlying asset is self-financed and tradable and when it is not, and show the difference between both cases. When the underlying a...
Article
Let us assume that \(\hat{A}_T\) is a consistent, asymptotically normal estimator of a matrix A (where T is the sample size), this paper shows that test statistics used in empirical work to test 1) the noninvertibility of A, i.e. det A = 0, 2) the positivite semi-definiteness A > > 0, have a different asymptotic distribution in the case where A = 0...
Article
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This paper examines granularity adjustments to parameter estimators in a default risk model with cohorts. The model is an extension of the Vasicek model (Vasicek, 1991) and includes a general factor and cohort specific factors. The granularity adjustments derived in the paper concern the mean and/or the variance of observed default frequencies and...
Chapter
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Theoretical research on long-term relationships between economic time series has a history that spans several decades during which various linear and nonlinear comovements were unveiled, such as the Phillips curve, and the purchasing power parity. In contrast, the econometric analysis of long-term relationships is much more recent, and has been con...
Chapter
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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, du...
Article
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This paper presents a new nonparametric method for computing the conditional Value-at-Risk, based on a local approximation of the conditional density function in a neighborhood of a predetermined extreme value for univariate and multivariate series of portfolio returns. For illustration, the method is applied to intraday VaR estimation on portfolio...
Article
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The Wishart Autoregressive (WAR) process is a dynamic model for time series of multivariate stochastic volatility. The WAR naturally accommodates the positivity and symmetry of volatility matrices and provides closed-form non-linear forecasts. The estimation of the WAR is straighforward, as it relies on standard methods such as the Method of Moment...
Article
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This paper introduces a new parametric fund performance measure, called the L-performance. The L-performance is an alternative to the Sharpe performance, which is commonly used in practice despite its inability to account for skewness and heavy tails of unconditional return distributions. The L-performance improves upon the Sharpe measure in this r...
Article
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This paper introduces the Dynamic Additive Quantile (DAQ) model that ensures the monotonicity of conditional quantile estimates. The DAQ model is easily estimable and can be used for computation and updating of the Value-at-Risk. An asymptotically efficient estimator of the DAQ is obtained by maximizing an objective function based on the inverse KL...
Article
Full-text available
This paper introduces a new parametric fund performance measure, called the L-performance. The L-performance is an alternative to the Sharpe performance, which is commonly used in practice despite its inability to account for skewness and heavy tails of unconditional return distributions. The L-performance improves upon the Sharpe measure in this r...
Chapter
INTRODUCTIONDURATION VARIABLESPARAMETRIC MODELSSEMIPARAMETRIC MODELSDURATION TIME SERIES
Article
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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,...
Article
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 distribut...
Article
Full-text available
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,...
Article
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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 sta...
Article
We introduce the multivariate Jacobi process as a representation for the dynamics of a stochastic discrete probability distribution. Its domain of application is dynamic analysis of switching regimes in asset return volatility, business cycle and corporate credit ratings. The paper shows how the multivariate Jacobi process is derived from the multi...
Article
Full-text available
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...
Article
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The bonus-malus scheme shows how the history of claim arrivals determines the dynamics of insurance premium. It is important to distinguish to what extent changes in the insurance premium are explained by lagged claim counts introduced among explanatory variables and by unobservable heterogeneity included in the model, which needs to be regularly u...
Article
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We propose a class of two factor dynamic models for duration data and related risk analysis in finance and insurance. Empirical findings suggest that the conditional mean and (under) overdispersion of times elapsed between stock trades feature various patterns of temporal dependence. Therefore durations seem to be driven jointly by movements of two...
Article
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...
Article
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We propose a semi-nonparametric method of identification and estimation for Gaussian autoregressive processes with stochastic autoregressive coefficients. The autoregressive coefficient is considered as a latent process with either a moving average or regime switching representation. We develop a consistent estimator of the distribution of the auto...
Article
Full-text available
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, i...
Article
Full-text available
A number of structural questions encountered in risk analysis are naturally written in terms of conditional real Laplace transform (moment generating function) of the process of interest. Such a Laplace transform associates with a real argument u the quantity E[exp(--uYt+l)[Yt], where Y denotes the process of interest and Yt -- (Yt,Yt-1, ...) the i...
Article
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....
Article
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This paper introduces nonlinear dynamic factor models for various applications related to risk analysis. Traditional factor models represent the dynamics of processes driven by movements of latent variables, called the factors. Our approach extends this setup by introducing factors defined as random dynamic parameters and stochastic autocorrelated...
Article
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We introduce a class of nonlinear dynamic processes, called compound au- toregressive (CAR), and characterized by the conditional log-Laplace trans- forms which are aÆne functions of the lagged values of the process. The CAR processes resemble the linear autoregressive processes in that their forecasting distributions at all horizons admit analytic...
Article
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We propose exact tests and confidence sets for various structural models typically estimated by IV methods, such as models with unobserved regressors, which remain valid despite the presence of identification problems or weak instruments. Two approaches are considered: (1) an instrument substitution method, which generalizes the Anderson-Rubin proc...
Article
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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.
Article
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Matching university places to students is not as clear cut or as straightforward as it ought to be. By investigating the matching algorithm used by the German central clearinghouse for university admissions in medicine and related subjects, we show that a procedure designed to give an advantage to students with excellent school grades actually harm...
Article
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jectories of the process fy t g, where: THIS VERSION: September 7, 2000 2 y t = (1 Gamma L) d ffl t : In the first experiment the process is generated from an i.i.d. standard normal sequence fffl t g representing a strong white noise process, ffl t = ffl t . Thus our first sample contains realizations of y t = (1 Gamma L) d ffl t . In the second ex...
Article
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Nonlinear Autocorrelograms; an Application to Inter-Trade Durations The paper presents a study of temporal dependence in nonlinear transformations of time series. We examine the effects of parametric transformations on autocorrelation values and the persistence range with special emphasis on long memory processes. We derive an invariance property f...
Article
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Introduction Nonlinear dynamics of macroeconomic or financial variables is often examined and modelled by using standard tools of time series analysis. However the instruments and concepts such as the autocorrelation and partial autocorrelation functions, spectral densities, unit root tests, cointegrating vectors and E.C.M. representation [Granger...
Article
We propose finite sample tests and confidence sets for models with unobserved and generated regressors as well as various models estimated by instrumental variables method. We study two distinct approaches for various models considered by Pagan (1984). The first one is an instrument substitution method which generalizes an approach proposed by Ande...
Article
Full-text available
Factor Markov Models with Finite Dimensional Dependence We consider nonlinear factor models, where the factor (i t ) is Markov and features finite dimensional dependence (FDD), i.e. admits a transition function of the type: (i t ji tGamma1 ) = (i t )a 0 (i t )b(i tGamma1 ), with a finite number of cross effects between the present and past values....
Article
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Local Likelihood Density Estimation, and Value at Risk. In this paper we fit locally a parametric model to observations lying in a neighborhood of a predetermined value c. This approach provides an instrument of tail analysis, called the local parameter function, which represents the dependence of the estimated parameters on c. As well, a new local...
Article
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Estimation of Autoregressive Processes with Heterogenous Persistence We propose a semi-nonparametric method of identification and estimation for a gaussian autoregressive process with stochastic autoregressive coefficient. The autoregressive coefficient is considered as a latent process with either a moving average, or regime switching representati...
Article
Matching university places to students is not as clear cut or as straightforward as it ought to be. By investigating the matching algorithm used by the German central clearinghouse for university admissions in medicine and related subjects, we show that a procedure designed to give an advantage to students with excellent school grades actually harm...
Article
Full-text available
This paper introduces a notion on nonlinear innovation for the ananlysis of nonlinear dynamics. We show that nonlinear processes can be represented as functions of current and lagged values of nonlinear innovations.
Article
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In a nonlinear framework the magnitude of temporal dependence may depend on the transformation applied to time series. The aim of this paper is to examine in detail various forms of persistence and the corresponding transformations. For multivariate series the persistence analysis is used to reveal nonlinear comovements between the components, call...
Article
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We develop a class of ARCH models for series sampled at unequal time intervals set by trade or quote arrivals. Our approach combines insights from the temporal aggregation for GARCH models discussed by Drost and Nijman (1993) and Drost and Werker (1994), and the autoregressive conditional duration model of Engle and Russell (1996) proposed to model...
Article
The purpose of the paper is to propose an autocorrelogram estimation procedure for irregularly spaced data which are modelled as subordinated continuous time-series processes. Such processes, also called time-deformed stochastic processes, have been proposed in a variety of contexts. Before entertaining the possibility of modelling such time series...
Article
This paper examines causality between the series of returns and transaction volumes in high frequency data. The dynamics of both series is restricted to transitions between a finite number of states. Depending on the state selection criteria, this approach approximates the dynamics of varying market regimes, or in a broader sense reflects the time...
Article
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The paper presents a study of dependencies between the autocorrelation function and selected nonlinear transformations of time series. We examine parametric transformations and introduce an analysis of nonlinear canonical correlations. We also propose various methods of testing the autocorrelation function for normality of the serially correlated p...