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This paper aims to examine the asymmetric effects of exchange rate shocks on inflation for a small open economy, namely South Africa, over the period 1970Q1–2020Q1.

A threshold vector autoregressive model that allows parameters to switch according to whether a threshold variable crosses an estimated threshold is employed to address the objective of this paper. The threshold value is determined endogenously using the Hansen (1996) test. Generalized impulse responses introduced by Koop et al. (1996) are used to study the effects of exchange rate shocks on inflation depending on their size, sign and timing to the inflation cycle. The authors also employed a Cholesky decomposition identification scheme to identify exchange rate shocks in the non-linear model.

The results show that there is a non-linearity effect of the exchange rate shock on inflation. In particular, the effects of 1 or 2 standard deviations of positive (appreciation) or negative (depreciation) exchange rate shock on inflation are small in the long run but a bit larger in the high inflation regime than the low inflation regime.

This paper contributes to the literature on the non-linear effects of exchange rate pass-through (ERPT) to inflation for Sub-Saharan African economies in general and the South African economy in particular by incorporating the size and timing of the exchange rate shocks to the inflation cycle.

The purpose of this paper is to show the asymmetric effects of government spending shocks for South Africa over the period 1960Q1–2014Q2.

A threshold vector autoregressive model that allows parameters to switch according to whether a threshold variable crosses an estimated threshold is employed to address the objective of this paper. The threshold value is determined endogenously using Hansen (1996) test. Generalized impulse responses introduced by Koop et al. (1996) are used to study the effects of government spending shocks on growth depending on their size, sign and timing with respect to the economic cycle. The author also uses a Cholesky decomposition identification scheme in order to identify discretionary government spending shocks in the non-linear model.

The empirical findings support the state-dependent effects of fiscal policy. In particular, the effects of 1 or 2 standard deviations expansionary or contractionary government spending shock on output are very small both on impact and in the long run; and a bit larger in downturns but has only a very limited effect or no effect in times of expansion. This result gives support to the evidence in the recent literature that fiscal policy in developing countries is overwhelmingly procyclical.

It adds to the scarce empirical fiscal literature of the South African economy in particular and developing economies in general by allowing non-linearities to estimate the effect of government spending shocks over economic cycle.

This study investigates the mispricing dynamics of NIFTY 50 Index futures, drawing upon daily data spanning from January 2008 to July 2023.

The study employs both a single regime analysis and a tri-regime model to understand the fluctuations in NIFTY 50 Index futures mispricing.

The study reveals a complex interplay between various market factors and mispricing, including forward-looking volatility (measured by the NIFVIX index), changes in open interest, underlying index return, futures volume, index volume and time to maturity. Additionally, the relationships are regime-dependent, specifically identifying the regime-dependent nature of the relationship between forward-looking volatility and mispricing, the impact of futures volume on mispricing, the effect of open interest on mispricing, the varying influence of index volume and the influence of time to maturity across the three distinct regimes.

These findings offer valuable insights for policymakers and investors by providing a detailed understanding of futures market efficiency and potential arbitrage opportunities. The study emphasizes the importance of understanding market dynamics, transaction costs and timing, offering guidance to enhance market efficiency and capitalize on trading opportunities in the evolving Indian derivatives market.

The Vector Autoregression (VAR) and Threshold Vector Autoregression Regression (TVAR) models are deployed to disentangle the interrelationships between NIFTY 50 Index futures mispricing and related endogenous determinants.

This study investigates the Nifty 50 Index futures mispricing across three distinct market regimes.

We highlight how factors like volatility, futures volume, and open interest vary in their impact.

The study employs vector auto-regressive and threshold vector auto-regressive models to explore the complex relationships influencing mispricing.

We provide valuable insights for investors and policymakers on improving market efficiency and identifying potential arbitrage opportunities.

The purpose of this paper is to empirically analyze the impact of macroeconomic uncertainty on a large sample of 19 commodity markets.

The authors rely on Jurado et al.’s (2015) measure of macroeconomic uncertainty based on a wide range of monthly macroeconomic and financial indicators and estimate a threshold VAR model to assess whether the impact of macroeconomic uncertainty on commodity prices differs under the high- or low-uncertainty state.

The findings show that positive macroeconomic uncertainty shocks affect commodity prices returns negatively on average and the impact of macroeconomic uncertainty is generally higher in high-uncertainty states compared with low-uncertainty states. Besides, although the safe-haven role of precious metals is confirmed, energy and industrial markets are more sensitive to short-run and long-run macroeconomic uncertainty, respectively.

The findings in this study suggest that commodity prices reflect not only the level of economic fundamental but also the volatility of economic fundamental.

This study empirically analyzes and verifies the influence of macroeconomic uncertainty not only on oil prices but also on four groups of 19 raw materials. As for the methodological issues, the authors rely on a structural threshold vector autoregressive specification for modeling commodity price returns to account for potentially different effects depending on the macroeconomic uncertainty states.

The purpose of this paper is to investigate the impact of foreign capital shifts on economic activities and asset prices in South Korea.

The authors in this paper apply the Bayesian threshold vector autoregressive (TVAR) model to estimate the regimes of large and low inflows of foreign capital. Then, structural impulse-response analysis is used to check whether the responses of the variables differ across the estimated regimes. The model is estimated using quarterly data of foreign capital inflows, gross domestic product (GDP), consumer price index, credit to the private non-financial sector, real effective exchange rate (REER), stock returns and house prices.

The main findings suggest that large inflows of gross foreign capital, foreign direct investments (FDI) and foreign portfolio investments (FPI) are ineffective to boost economic growth, but large inflows of other foreign investments (OFIs) significantly contribute to GDP. The decreases in the foreign capital inflows are associated with larger depreciation of REER. The large inflows of gross foreign capital, FDI and OFIs are associated with further expansion of credit supply to private non-financial sectors.

The policy implications of foreign capital inflows are of particular importance to all the emerging markets alike. However, the empirical analysis is limited to the case of South Korea due to various reasons. The experience with international capital inflows among emerging markets is heterogeneous. Therefore, it would be better to take each case of emerging market individually. In addition, TVAR analysis requires a long data sample, which unfortunately is not available for most of the emerging markets.

The foreign capital inflows are shown to be procyclical and notoriously volatile in many studies. Nevertheless, this topic has commonly been studied using linear VAR models, which do not properly deal with the cyclical characteristics of foreign capital inflows. This study attempts to resolve these methodological limitations by examining a non-linear VAR model that is capable of capturing the structural breaks associated with the cyclical behaviors of foreign capital inflows.

This paper aims to find, which of the assets: gold, oil or bitcoin can be considered a safe-haven for investors in a crisis-driven Venezuela. The authors look also at the governmental change of approach towards the use and mining of cryptocurrencies being one of the assets and potential applications of bitcoin as (quasi) money.

The authors collected the daily data (a period from 01 May 2014 to 31 July 2018) on the development of the following magnitudes: Caracas Stock Exchange main index: Índice Bursátil de Capitalisación (IBC) index; gold price in US dollars, the oil price in US dollars and Bitcoin price in bolivar fuerte (VEF) (LocalBitcoins). The authors estimated a threshold VAR model between IBC and each of the possible safe-haven assets, where the trigger variable was the IBC; then the authors modelled the residuals from the TVAR model using MGARCH model with dynamic conditional correlation.

The results show that that gold is a better safe-haven than oil for Venezuelan investors, while bitcoin can be considered a weak safe haven. Still, bitcoin can perform (to a certain extent) money functions in a crisis-driven country.

Further research after the change of local currency from VEF into bolivar soberano might be looked at on the later stage.

The authors provide evidence on which of analysed asset is the best safe-haven for the investors acting in the time of the crisis. The evidence goes in line with other authors’ findings, thus, the results might bring implications for investors of more universal character. Additionally, the result might be helpful for governments and/or monetary authorities while projecting institutional frameworks and conducting monetary policy.

The unprecedented economic crisis in Venezuela was one of the factors that fuelled the mining and use of cryptocurrencies in the daily life of its citizens. Nowadays, the country is a leader in terms of the use of bitcoin and other cryptocurrencies in Latin America. The results show a potential application of bitcoin as a store of value or even means of payments in Venezuelan (or in other countries affected by the crisis).

The paper builds on the original data set collected by the authors and brings evidence from the models the authors constructed to verify, which asset is the best option for investors in hard times of the crisis. The authors add to the existing literature on financial assets, cryptocurrencies and behaviour of investors under different economic conditions.

Because of the growing financial market integration, China’s stock market’s volatility spillover effect has gradually increased. Traditional strategies do not capture stock volatility in dependence and dynamic conditions. Therefore, this study aims to find an effective stochastic model to predict the volatility spillover effect in the dynamic stock markets.

To assess the time-varying dynamics and volatility spillover, this study has used an integrated approach of dynamic conditional correlation model, copula and extreme-value theory. A daily log-returns of three leading indices of Pakistan Stock Exchange (PSX) and Shanghai Stock Exchange (SSE) from the period of 2009 to 2019 is used in the modeling of value-at-risk (VaR) for volatility estimation. The Student’s t copula has been selected based on maximum likelihood estimation and Akaike’s information criteria values of all the copulas using the goodness-of-fit test.

The model results show stronger dependency between all major portfolios of PSX and SSE, with the parametric value of 0.98. Subsequently, the results of dependence structure positively estimate the spillover effect of SSE over PSX. Furthermore, the back-testing results show that the VaR model performs well at 99% and 95% levels of confidence and gives more accurate estimates upon the maximum level of confidence.

This study is helpful for the investment managers to manage the risk associated to portfolios under dependence conditions. Moreover, this study is also helpful for the researchers in the field of financial risk management who are trying to improve the returns by addressing the issues of volatility estimations.

This study contributes to the body of knowledge by providing a practical model to manage the volatility spillover effect in dependence conditions between as well as across the financial markets.

The purpose of this paper is to examine whether futures markets play a dominant role in the price discovery process. The rate of convergence of information from one market to another is analyzed to infer the efficiency of futures as an effective hedging tool.

The paper uses a two‐regime threshold vector autoregression (TVAR) and a two‐regime threshold autoregression for six commodities. The regimes (or states) are defined around the expiration week of the futures contract.

This paper finds evidence for price discovery process happening in the futures market in five out of six commodities. However, the rate of convergence of information is slow, particularly in the non‐expiration weeks. For copper, gold and silver, the rate of convergence is almost instantaneous during the expiration week of the futures contract affirming the utility of futures contracts as an effective hedging tool. In case of chickpeas, nickel and rubber the convergence worsens during the expiration week indicating the non‐usability of futures contract for hedging.

This paper extends the framework developed by Garbade et al. by superimposing a two‐regime TVAR model to quantify the price discovery process. It is the first paper to analyze the differential impact of price discovery and convergence during expiration week (compared to non‐expiration weeks) for the Indian commodities market.

Introduction: Uncertainty plays an important role on economic stability and macroeconomic variables. Economic agents postpone decisions about investment and consumption in periods in which uncertainty is high. This situation affects economic growth negatively. Recently, uncertainty has focused on policy uncertainty. At this point, economic policy uncertainty (EPU) comes to the forefront. EPU is defined as conception that economic agents do not forecast consequences of economic policies adopted by policy makers and future economic policies. In terms of developing countries, statements presented by policy makers in the United States especially may appear as a source of uncertainty in developing economies.

Aim: Therefore, the aim of this study is to analyze the effects of US EPU on macroeconomic variables for Turkey and Brazil, Russia, India, China and South Africa for periods in which global risk perception is low and high.

Method: The authors used monthly data from January 1998 to December 2018. For this purpose, the authors used Threshold VAR. VIX index takes in consideration as global risk perception. The authors used US EPU index proposed by Baker’s vd. (2016) in order to measure EPU in the United States. Besides, the authors used macroeconomic variables such as industrial production index, inflation and exchange rate.

Findings: As is seen from the results of the analysis, for Turkey’s economy the macroeconomic variables significantly and strongly respond to the changes in the EPU index during the periods in which global risk perception is low; nonetheless, the so-called responses weaken due to the adopted policy of “wait and watch” by investors during the periods in which global risk perception is high.