Bursts and Regularization in Financial Markets: Deterministic or Stochastic?

We analyze empirical finance data, such as the Financial Stress Index, a number of asset classes (swaps, equity and bonds), market (emerging vs. developed), issuer (corporate vs. government bond), maturity (short vs. long) data, asking whether the recently observed alternations between calm periods and financial turmoil can be modelled in a low-dimensional deterministic manner, or whether they demand for their description a stochastic model. We find that a deterministic model performs at least as well as one of the best stochastic models, but may provide additional insight into the essential mechanisms that drive financial markets.

THE CONSTRUCTION AND APPLICATION OF FINANCIAL STRESS INDEX IN CHINA

Maintaining the stability of the financial system is of great significance to a country’s economic security, and accurate measurement of systemic financial risks is the basic premise. This paper selects nine important factor index data in China’s five markets, uses the credit weight method and extreme value method to construct a time-sensitive China financial stress index. It is concluded that the change of the financial stress index precedes the change of the economic development trend, which proves the guiding significance of constructing the stress index to economic development. Finally, reasonable suggestions were put forward to relevant departments.

DYNAMIC CONNECTEDNESS OF FINANCIAL STRESS ACROSS ADVANCED AND EMERGING ECONOMIES: EVIDENCE FROM TIME AND FREQUENCY DOMAINS

This study analyzes the dynamic connectedness (i.e., spillovers and spillbacks) of financial stress across advanced and emerging economies. As proxy for financial stress, we reconstruct the financial stress index (FSI) for 16 advanced economies and 15 emerging economies from January 1997 to August 2020. The constructed FSIs reflect combined stress level in banking sectors, equity markets, capital markets and exchange rate markets. Using frameworks proposed by Diebold and Yilmaz (Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66) and Baruník and Křehlík (Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16(2), 271–296), we find that there is strong connectedness of financial stress across economies. Moreover, the connectedness of the financial stress is stronger after the global financial crisis and during the COVID-19 pandemic. Although the spillover of shocks is strongest in the short-term horizon, the spillovers in the longer-term horizons are not trivial. Our results also show that the US is the largest shock transmitter as well as one of the largest shock receivers. Our results also suggest that shocks originating in advanced economies have strong effects on other economies, but shocks originating in emerging economies also play an increasing role. Global factors such as global economic policy uncertainty and geopolitical risks influence the magnitude of the spillover of financial stress.

Analysis of the Literature of Financial Stress Index and Proposed Model for Pakistan.

In the recent past, research in developed markets has aimed at developing various indices that can be applied to apprehend the impact that financial stress can have on a country’s economic activity. This article reviews the literature on financial stress and its impact on economic activities. It further presents a proposed methodology for constructing financial stress index and its implications for macroeconomic performance in relation to Pakistan. A review of the literature was conducted discussing (a) the formulation of the financial stress index and (b) its impact on the country’s economic activity indicators. The analysis of the literature made it possible to identify the current themes and remaining gaps in the literature on the financial stress index. It is concluded that FSI is beneficial for monitoring and evaluating the usefulness of government economic policies in crisis as well as normal times. Moreover, the index grabs the stresses in the form of financial stress, which is not only from various market sectors but also from their connection through a contagious effect internationally. Therefore, the paper concludes that the FSI could be established and used as an effective tool to measure and monitor financial stress level and its impact on various economic activity indicators in Pakistan. Keywords: Financial Stress Index, GDP, Industrial Production, Foreign Trade, PCA, Emerging Markets, Pakistan.

Analysis of Financial Stability Determinants in Indonesia

Financial Stress Index (FSI) is one of the indices to measure financial stress which can lead to a financial crisis. Quantitative analysis was conducted to some banking sector performance indicator which impacts financial stability with FSI as a proxy. Data population was taken from banking company listed in Indonesian Stock Exchange, sampling using purposive sampling of 38 banks. Using pooled data regression analysis was founded that NPL, CAR, and ROA positively significant to financial stability, while NIM negative but not significant to financial stability. The research found that NPL and NIM are not in line with the hypothesis. NPL is an indicator for bad debt, which means that increase in NPL will make financial stability vulnerable, but the research shows that an increase in NPL causes financial stability incline to increased, this could have happened if any other factors maintain financial stability tend to increase. On the other hand, NIM is decreasing which means the productivity of banks decreased but financial stability tends to increase because other factors that maintain financial stability tend to increase.

The link between financial stress index and economic activity: prominent Granger causalities across frequencies in Luxembourg

PurposeIn spite of the certain risk imposed by financial stress on the real economy, the relationship between financial stress and economic activity is complicated and underresearched, meaning that important gaps still remain in the authors’ understanding of this critical relationship. Therefore, the current study aims to answer the significant question regarding whether a stressful financial sector has predictive power on the real sector and vice versa. Hence, the study examines the causal interrelationship between financial stress index (FSI) and economic activity in Luxembourg as a sample country.Design/methodology/approachIn this study, accompanying the time domain Granger causality framework of Hacker and Hatemi-J (2012), the authors utilize the spectral causality technique of Breitung and Candelon (2006), which is based on the study of Geweke (1982) and Hosoya (1991). This method enables the researcher to measure the degree of a particular variation in time series. Moreover, it allows considering the nonlinearities and causality cycles. The authors further apply the recent method of Farné and Montanari (2018) that is a bootstrap framework on Granger-causality spectra, which allows for disambiguation in causalities.FindingsThe time-domain approach finds evidence of bidirectional causation between the variables. However, the spectral causality results indicate the causal linkages between the series are only valid under the medium-run frequency. This study’s findings emphasize covering the frequency causality to deliver a more comprehensive picture of the interrelationship between the variables.Originality/valueThere are many studies in this area that examine the nexus between financial stress and economic activity. However, the authors believe this paper is the first study in the context of Luxemburg. The authors focus on this country since its financial sector is designated as the most important pillar for the economy. Thus, a careful and reliable examination of the relationship between the financial sector and economic activity is likely to be of considerable interest to policymakers and researchers in this field.

TOURISM INDUSTRY STRESS INDEX AND ITS RELATIONSHIP TO THE FINANCIAL STRESS INDEX

Purpose – development of the Tourism Industry Stress Index (TSI) and the Financial Stress Index (FSI) followed by an examination of their interaction. Design – The TSI, which aggregates tourist arrivals, overnight stays and net occupancy, was tested on data for Finland, Italy, Germany and Spain between 1993 and 2020. The FSI was composed of the S&P500 index, Brent oil futures, and the real effective exchange rate of the euro. Methodology / Approach – Both stress indices were calculated as the difference between the moving standard deviation and the moving average of the monthly growth rate of the selected indicators. We aggregated them by applying two alternative techniques: arithmetic mean and nonnormalized principal component analysis. The Granger causality test was utilised to assess the dependence between the indices. Findings – We identified periods of increased volatility in the European tourism market and described its connection to financial crises. The causality test of the FSI-TSI model showed that financial turmoil led to increased tourism market stress with an average lag of three months and a marginal effect of 0.2. Originality of the research – We recommend the Financial Stress Index as a predictor of the Tourism Industry Stress Index in the business cycle.

Financial Stress Index and Economic Activity in South Africa: New Evidence

The importance of a sound and stable financial system and by extension economic stability was brought to the fore by the global financial crisis (GFC). The economic and social costs of the GFC have renewed the commitment of stakeholders in the financial sector including central banks to develop instruments and methodologies that will be useful in monitoring financial stress within the financial system and the real economy. This study contributes to the growing literature by developing a financial stress indicator for the South African financial market. The financial stress indicator (FSI) is a single aggregate indicator that is constructed to reflect the systemic nature of financial instability and also to measure the vulnerability of the financial system to both internal and external shocks. Using the principal component analysis (PCA), the results show that financial stress can be identified by the financial stress indicator. Furthermore, using a recursive Vector Autoregression (VAR) model to estimate the impact of financial stress on output and investment, the result shows that financial stress has a negative impact on economic growth and investment, though not immediately. FSI is very useful for gauging the effectiveness of government measures to mitigate the impact of financial stress. Concerted effort to stimulate investment and domestic production by relevant stakeholders is necessary to mitigate the impact of financial stress. This will go a long way to alleviating the impact of the financial stress on industrial production, employment and the economy at large.