A Fast Initial Response Approach to Sequential Financial Surveillance

Consider the problem of financial surveillance of a heavy-tailed time series modeled as a geometric random walk with log-Student's t increments assuming a constant volatility. Our proposed sequential testing method is based on applying the recently developed taut string (TS) univariate process monitoring scheme to the gaussianized log-differenced process data. With the signal process given by a properly scaled total variation norm of the nonparametric taut string estimator applied to the gaussianized log-differences, the change point detection procedure is constructed to have a desired in-control (IC) average run length (ARL) assuming no change in the process drift. If a change in the process drift is imminent, the proposed approach offers an effective fast initial response (FIR) instrument for rapid yet reliable change point detection. This framework may be particularly advantageous for protection against imminent upsets in financial time series in a turbulent socioeconomic and/or political environment. We illustrate how the proposed approach can be applied to sequential surveillance of real-world financial data originating from Meta Platforms, Inc. (FB) stock prices and compare the performance of the TS chart to that of the more prominent CUSUM and CUSUM FIR charts at flagging the COVID-19 related crash of February 2020. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

How the real estate indexes have performed during the COVID-19 crisis? Multifractal analysis revisited with wavelet

Purpose: The purpose of this paper is to test the existence of stylized facts, such as the volatility clustering, heavy tails seen on financial series, long-term dependence and multifractality on the returns of four real estate indexes using different types of indexes: conventional and Islamic by comparing pre and during COVID-19 pandemic. Design/methodology/approach: Firstly, the authors examined the characteristics of the indexes. Secondly, the authors estimated the parameters of the stable distribution. Then, the long memory is detected via the estimation of the Hurst exponents. Afterwards, the authors determine the graphs of the multifractal detrended fluctuation analysis (MF-DFA). Finally, the authors apply the WTMM method. Findings: The results suggest that the real estate indexes are far from being efficient and that the lowest level of multifractality was observed for Islamic indexes. Research limitations/implications: The inefficiency behavior of real estate indexes gives us an idea about the prediction of the behavior of future returns in these markets on the basis of past informations. Similarly, market participants would do well to reassess their investment and risk management framework to mitigate new and somewhat higher levels of risk of their exposures during the turbulent period. Originality/value: To the authors' knowledge, this is the first real estate market study employing STL decomposition before applying the MF-DFA in the context of the COVID-19 crisis. Likewise, the study is the first investigation that focuses on these four indexes. © 2023, Emerald Publishing Limited.

Time series estimation of the dynamic effects of disaster-type shocks

This paper provides three results for SVARs under the assumption that the primitive shocks are mutually independent. First, a framework is proposed to accommodate a disaster-type variable with infinite variance into a SVAR. We show that the least squares estimates of the SVAR are consistent but have non-standard asymptotics. Second, the disaster shock is identified as the component with the largest kurtosis. An estimator that is robust to infinite variance is used to recover the mutually independent components. Third, an independence test on the residuals pre-whitened by the Choleski decomposition is proposed to test the restrictions imposed on a SVAR. The test can be applied whether the data have fat or thin tails, and to over as well as exactly identified models. Three applications are considered. In the first, the independence test is used to shed light on the conflicting evidence regarding the role of uncertainty in economic fluctuations. In the second, disaster shocks are shown to have short term economic impact arising mostly from feedback dynamics. The third uses the framework to study the dynamic effects of economic shocks post-covid.

A new filtering inference procedure for a GED state-space volatility model

A vast amount of methods have been developed to make inferences for volatility data, taking into account the stylized facts of rate/return data. However, the common problem is that the latent parameters of many volatility models are high-dimensional and analytically intractable. Therefore, their inference procedure requires numerical approximations using intensive computational techniques, as the Markov chain Monte Carlo, Laplace, and particle filter methods. A common strategy to overcome this problem is model linearization. This approach consists of writing the stochastic volatility model as a linear Gaussian state-space model, leading to an approximated marginal likelihood using the Kalman filter, reducing the problem’s dimensionality. This paper proposes a new filtering inference procedure with an integrated likelihood for a Generalized Error Distribution (GED) state-space volatility model without model linearization. Also, we evaluate the quality of our method approximation and introduce an approximated smoothing procedure. We use the Bayesian methods for making the inference of static parameters and perform a simulation exercise to study the estimators’ properties. Our results show that the proposed model can be reasonably estimated, and the approximation of our method is reasonable. Furthermore, we provide a case study of the pound/dollar and real/dollar exchange rate to illustrate our approach’s performance. For the real/dollar time series, the model captures a high volatility pattern due to the COVID-19 pandemic.

Tail risks and infectious disease: Influenza mortality in the U.S., 1900-2018.

I use extreme values theory and data on influenza mortality from the U.S. for 1900 to 2018 to estimate the tail risks of mortality. I find that the distribution for influenza mortality rates is heavy-tailed, which suggests that the tails of the mortality distribution are more informative than the events of high frequency (i.e., years of low mortality). I also discuss the implications of my estimates for risk management and pandemic planning.