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Estimation of agent-based models in economics and finance confronts researchers with a number of challenges. Typically, the complex structures of such models do not allow to derive closed-form likelihood functions so that either numerical approximations to the likelihood or moment-based estimators have to be used for parameter inference. However, all these approaches suffer from extremely high computational demands as they typically work with simulations (of the agent-based model) embedded in (Monte Carlo) simulations conducted for the purpose of parameter identification. One approach that is very generally applicable and that has the potential of alleviating the computational burden is Approximate Bayesian Computation (ABC). While popular in other areas of agent-based modelling, it seems not to have been used so far in economics and finance. This paper provides an introduction to this methodology and demonstrates its potential with the example of a well-studied model of speculative dynamics. As it turns out, ABC appears to make more efficient use of moment-based information than frequentist SMM (Simulated Method of Moments), and it can be used for sample sizes of an order far beyond the reach of numerical likelihood methods.

We suggest a new value-at-risk (VaR) framework using EGARCH (exponential generalized autoregressive conditional heteroskedasticity) models with score-driven expected return, scale, and shape filters. We use the EGB2 (exponential generalized beta of the second kind), NIG (normal-inverse Gaussian), and Skew-Gen- t (skewed generalized- t ) distributions, for which the score-driven shape parameters drive the skewness, tail shape, and peakedness of the distribution. We use daily data on the Standard & Poor’s 500 (S&P 500) index for the period of February 1990 to October 2021. For all distributions, likelihood-ratio (LR) tests indicate that several EGARCH models with dynamic shape are superior to the EGARCH models with constant shape. We compare the realized volatility with the conditional volatility estimates, and we find two Skew-Gen- t specifications with dynamic shape, which are superior to the Skew-Gen- t specification with constant shape. The shape parameter dynamics are associated with important events that affected the stock market in the United States (US). VaR backtesting is performed for the dot.com boom (January 1997 to October 2020), the 2008 US Financial Crisis (October 2007 to March 2009), and the coronavirus disease (COVID-19) pandemic (January 2020 to October 2021). We show that the use of the dynamic shape parameters improves the VaR measurements.

We test for real interest rate parity using data from six European countries (France, Germany, Italy, the Netherlands, Spain, and the United Kingdom), Japan, and the United States over a period of more than two centuries. Our contribution is threefold. First, we implement a wavelet-based analysis, which examines both frequency and time information contained in a time series. Second, we employ the United States, the United Kingdom, and Germany as alternative base countries in the wavelet regressions to ascertain the sensitivity of the results to the choice of the base country. Third, we test the real interest rate parity over the entire period (1800–2018) and for several non-contiguous subperiods that hold historical significance and relative importance. Three subperiods link to the three globalization waves (1870–1914, 1944–1971, and 1989–2018), and four subperiods connect to the exchange rate regimes. The wavelet-based results suggest that the validity of the real interest rate parity is scale-dependent. The specific evidence in most cases supports the parity at lower frequencies but not at higher frequencies, which is consistent with the idea that the purchasing power parity and uncovered interest parity, the two main ingredients of the real interest rate parity, are mostly valid in the long run.

This paper investigates how different commodity prices are affected by unconventional monetary policies (UMP) implemented by the Federal Reserve of the United States as a response to the Global Financial Crisis. We analyze impulse responses using local projections and identify UMP shocks through high-frequency identification strategy. We show that forward guidance (FG) and large-scale asset purchase (LSAP) shocks lead to distinct responses when analyzing commodity prices. We find that asset-like commodities, such as gold and silver, respond to these UMP shocks most aggressively. While an easing FG shock leads to increases in their prices, an easing LSAP shock has the opposite effect. This differential response suggests that these asset-like commodities are being used as inflation and exchange rate hedges. In contrast, production-like and agricultural commodities respond to UMP shocks in the same way as conventional monetary policy shocks. Consistent with previous literature, we find that easing LSAP shocks, to some extent, signal a negative economic outlook. Policymakers can exploit the responses from the various commodity classes examined in this paper when evaluating the effectiveness of monetary policy in different sectors of the economy.

In this paper, we develop a simple multi-technology overlapping generations model that exhibits a wide variety of economic development patterns. In particular, our numerical simulations demonstrate that for a given set of parameter values, various types of development patterns such as the middle-income trap, the poverty trap, periodic or chaotic fluctuations, and high-income paths can coexist, and which pattern is realized depends only on the initial value of capital. For another set of parameter values, we show that, due to the pinball effect, an economy starting at a middle-income level can take off to the high-income state or get caught in the poverty trap in a seemingly random way after undergoing transient chaotic motions. Our results can explain observed complicated patterns of economic development in a unified manner.

The reversible-jump Markov chain Monte Carlo (RJMCMC) algorithm can generate a jump Markov chain in the parameter space of different dimensions, and select a suitable model effectively. In this paper, when the order of the double threshold variables autoregressive (DT-AR) is unknown, the RJMCMC method is designed to identify the order of the DT-AR model in this paper. The simulation experiments and the real example show that the proposed method works well in identifying the order and estimating the parameters of the DT-AR model simultaneously.

In this paper, statistical and volatility forecasting performances of the non-path-dependent score-driven multi-regime Markov-switching (MS) exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models are explored. Three contributions to the existing literature are provided. First, we use all relevant score-driven distributions from the literature - namely, the Student’s t -distribution, general error distribution (GED), skewed generalized t -distribution (Skew-Gen- t ), exponential generalized beta distribution of the second kind (EGB2), and normal-inverse Gaussian (NIG) distribution. We then introduce the score-driven Meixner (MXN) distribution-based EGARCH model to the literature on score-driven models. Second, proving the sufficient conditions of the asymptotic properties of the maximum likelihood (ML) estimator for non-path-dependent score-driven MS-EGARCH models is an unsolved problem. We provide a partial solution to that problem by proving necessary conditions for the asymptotic theory of the ML estimator. Third, to the best of our knowledge, this work includes the largest number of international stock indices from the G20 countries in the literature, covering the period of 2000–2022. We provide a discussion on the major events which caused common or non-common switching to the high-volatility regime for the G20 countries. The statistical performance and volatility forecasting results support the adoption of score-driven MS-EGARCH for the G20 countries.