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The purpose of this paper is to investigate the impact of changes in credit stock on real and financial indicators in Turkey with a focus on conditional and time-varying dynamics.

In addition to lag-augmented vector autoregression (LA-VAR) based time-varying Granger causality tests, threshold models and a research setting that identifies high/low states of credit growth based on 24-month moving averages are used to explore regime-dependent behavior. For investigating the asymmetric dynamics, the authors use a methodology that identifies good/bad news in credit growth based on 24-month moving averages and standard deviations.

Results strongly suggest that the impact of changes in credit stock induces conditional responses. Moreover, we find evidence for asymmetric responses. In the case of Turkey, efforts to spur growth through credit produce a strong negative byproduct, a depreciation in the exchange rate. The authors also find that changes in credit stock have become more relevant for uncertainties in inflation and exchange rate expectations, particularly in the era after mid-2018 in which credit growth volatility has increased noticeably.

This study provides a comprehensive analysis of time-varying and conditional responses to a change in credit stock in a major emerging economy. Using a moving threshold based only on the available information in the analysis of state-dependency represents a new approach.

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 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.

This study aims to examine the transmission mechanism of factors on the characteristic fluctuation of Vietnamese coffee bean export price (PVN).

Applying Markov switching–vector autoregressive model.

Significantly, the empirical results showed that the transmission of independent variables on PVN is non-linear, and the fluctuation of PVN is affected by many factors, especially PVN in the previous period. In addition, the effect of Robusta coffee price was the greatest with coefficient is 0.28785, and the correlation between PVN and it was also the highest in both regimes with coefficients are 0.5317 and 0.3959, respectively.

These obtained results are in accordance with reality, as Vietnam is the largest exporter of Robusta coffee in the world.

This survey focuses on two families of nonlinear vector time series models, the family of vector threshold regression (VTR) models and that of vector smooth transition regression (VSTR) models. These two model classes contain incomplete models in the sense that strongly exogeneous variables are allowed in the equations. The emphasis is on stationary models, but the considerations also include nonstationary VTR and VSTR models with cointegrated variables. Model specification, estimation and evaluation is considered, and the use of the models illustrated by macroeconomic examples from the literature.

This chapter presents the portfolio optimization problem formulated as a multi-criteria mixed integer program. Weighting and lexicographic approach are proposed. The portfolio selection problem considered is based on a single-period model of investment. An extension of the Markowitz portfolio optimization model is considered, in which the variance has been replaced with the Value-at-Risk (VaR). The VaR is a quantile of the return distribution function. In the classical Markowitz approach, future returns are random variables controlled by such parameters as the portfolio efficiency, which is measured by the expectation, whereas risk is calculated by the standard deviation. As a result, the classical problem is formulated as a quadratic program with continuous variables and some side constraints. The objective of the problem considered in this chapter is to allocate wealth on different securities to maximize the weighted difference of the portfolio expected return and the threshold of the probability that the return is less than a required level. The auxiliary objectives are minimization of risk probability of portfolio loss and minimization of the number of security types in portfolio. The four types of decision variables are introduced in the model: a continuous wealth allocation variable that represents the percentage of wealth allocated to each asset, a continuous variable that prevents the probability that return of investment is not less than required level, a binary selection variable that prevents the choice of portfolios whose VaR is below the minimized threshold, and a binary selection variable that represents choice of stocks in which capital should be invested. The results of some computational experiments with the mixed integer programming approach modeled on a real data from the Warsaw Stock Exchange are reported.

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.

The purpose of this study is to investigate the effects of uncertainty, namely, macroeconomic uncertainty (MU) and financial uncertainty (FU) on foreign exchange market stability, specifically on foreign exchange market pressure (EMP) and jump risk (RJV).

The latent threshold time-varying parameter VAR (LT-TVP-VAR) econometric approach is used in estimations to solve structural breaks.

The relationship of uncertainties and China's foreign exchange market stability is latent threshold nonlinear dynamic time-varying. In China's renminbi (RMB) appreciation stage, both MU and FU weaken the appreciation pressure of RMB. Moreover, MU and FU significantly increase the RJV, while MU significantly affects the RJV of the foreign exchange market. In the RMB depreciation stage, both MU and FU strengthen the EMP.

Findings based on data in China's foreign exchange market can be considered for other global markets in future research.

An increase in MU and FU has a negative effect on foreign exchange stability. Regulators can prevent the economic system uncertainty shocks on foreign exchange market stability through observation and judgment of MU and FU, which helps prevent and relieve financial risks. Investors can reduce foreign exchange risk as the exchange rate rebounds after hedging behavior during high uncertainty periods.

The effect of MU on the foreign exchange market stability is greater than that of FU, regardless of whether EMP or RJV occurs in the foreign exchange market.

The Basel Committee regulations require the estimation of value-at-risk (VaR) at 99 percent confidence level for a ten-trading-day-ahead forecasting horizon. The paper provides a multivariate modelling framework for multi-period VaR estimates for leptokurtic and asymmetrically distributed real estate portfolio returns. The purpose of the paper is to estimate accurate ten-day-ahead 99%VaR forecasts for real estate markets along with stock markets for seven countries across the world (the USA, the UK, Germany, Japan, Australia, Hong Kong and Singapore) following the Basel Committee requirements for financial regulation.

A 14-dimensional multivariate Diag-VECH model for seven equity indices and their relative real estate indices is estimated. The authors evaluate the VaR forecasts over a period of two weeks in calendar time, or ten-trading-days, and at 99 percent confidence level based on the Basle Committee on Banking Supervision requirements.

The Basel regulations require ten-day-ahead 99%VaR forecasts. This is the first study that provides successful evidence for ten-day-ahead 99%VaR estimations for real estate markets. Additionally, the authors provide evidence that there is a statistically significant relationship between the magnitude of the ten-day-ahead 99%VaR and the level of dynamic correlation for real estate and stock market indices; a valuable recommendation for risk managers who forecast risk across markets.

Risk managers, investors and financial institutions require dynamic multi-period VaR forecasts that will take into account properties of financial time series. Such accurate dynamic forecasts lead to successful decisions for controlling market risks.

This paper is the first approach which models simultaneously the volatility and VaR estimates for real estate and stock markets from the USA, Europe and Asia-Pacific over a period of more than 20 years. Additionally, the local correlation between stock and real estate indices has statistically significant explanatory power in estimating the ten-day-ahead 99%VaR.