This study analysed the impact of credit risk management on the financial performance of commercial banks and also attempted to establish if there exists any relationship between the credit risk management determinants by use of CAMEL indicators and financial performance of commercial banks in Kenya. A causal research design was undertaken in this study and this was facilitated by the use of secondary data which was obtained from the Central Bank of Kenya publications on banking sector survey. The study used multiple regression analysis in the analysis of data and the findings have been presented in the form of tables and regression equations. The study found out that there is a strong impact between the CAMEL components on the financial performance of commercial banks. The study also established that capital adequacy, asset quality, management efficiency and liquidity had weak relationship with financial performance (ROE) whereas earnings had a strong relationship with financial performance. This study concludes that CAMEL model can be used as a proxy for credit risk management. Key Words: Credit Risk, Management, Financial Performance, Commercial Banks, Kenya

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The Impact of Credit Risk Management on Financial Performance of Commercial Banks in Kenya

This study investigates the presence of deterministic dependencies in international stock markets using recurrence plots and recurrence quantification analysis (RQA). The results are based on a large set of free float-adjusted market capitalization stock indices, covering a period of 15 years. The statistical tests suggest that the dynamics of stock prices in emerging markets is characterized by higher values of RQA measures when compared to their developed counterparts. The behavior of stock markets during critical financial events, such as the burst of the technology bubble, the Asian currency crisis, and the recent subprime mortgage crisis, is analyzed by performing RQA in sliding windows. It is shown that during these events stock markets exhibit a distinctive behavior that is characterized by temporary decreases in the fraction of recurrence points contained in diagonal and vertical structures.

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Recurrence quantification analysis of global stock markets

Financial data has been extensively studied for correlations using Pearson’s cross-correlation coefficient ρ as the point of departure. We employ an estimator based on recurrence plots — the correlation of probability of recurrence ( C P R ) — to analyze connections between nine stock indices spread worldwide. We suggest a slight modification of the C P R approach in order to get more robust results. We examine trends in C P R for an approximately 19-month window moved along the time series and compare them to trends in ρ . Binning C P R into three levels of connectedness (strong, moderate, and weak), we extract the trends in number of connections in each bin over time. We also look at the behavior of C P R during the dot-com bubble by shifting the time series to align their peaks. C P R mainly uncovers that the markets move in and out of periods of strong connectivity erratically, instead of moving monotonically towards increasing global connectivity. This is in contrast to ρ , which gives a picture of ever-increasing correlation. C P R also exhibits that time-shifted markets have high connectivity around the dot-com bubble of 2000. We use significance tests using twin surrogates to interpret all the measures estimated in the study.

On interrelations of recurrences and connectivity trends between stock indices

The existing empirical literature on banking crises has not produced agreement on their causes. Using a sample of 75 emerging markets in 1975-1997, we attempt to determine what we know about banking crises by establishing which previous results are robust. Among the robust causes of emerging-market banking crises are rapid domestic credit growth, large bank liabilities relative to reserves, and deposit-rate decontrol. On the other hand, there is no compelling evidence of any particular relationship between exchange rate regimes and crises. Finally, the evidence that deposit insurance or a weak institutional environment heighten crisis risk appears to be fragile.

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Banking Crises in Emerging Markets: Presumptions and Evidence - eScholarship

Analysis of temporal pattern, causal interaction and predictive modeling of financial markets using nonlinear dynamics, econometric models and machine learning algorithms

Recurrence Plots are graphical tools based on Phase Space Reconstruction. Recurrence Quantification Analysis (RQA) is a statistical quantification of RPs. RP and RQA are good at working with non-stationarity and noisy data, in detecting changes in data behavior, in particular in detecting breaks, like a phase transition and in informing about other dynamic properties of a time series. Endogenous Stock Market Crashes have been modeled as phase changes in recent times. Motivated by this, we have used RP and RQA techniques for detecting critical regimes preceding an endogenous crash seen as a phase transition and hence give an estimation of the initial bubble time. We have used a new method for computing RQA measures with confidence intervals. We have also used the techniques on a known exogenous crash to see if the RP reveals a different story or not. The analysis is made on Nifty, Hong Kong AOI and Dow Jones Industrial Average, taken over a time span of about 3 years for the endogenous crashes. Then the RPs of all time series have been observed, compared and discussed. All the time series have been first transformed into the classical momentum divided by the maximum Xmax of the time series over the time window which is considered in the specific analysis. RPs have been plotted for each time series, and RQA variables have been computed on different epochs. Our studies reveal that, in the case of an endogenous crash, we have been able to identify the bubble, while in the case of exogenous crashes the plots do not show any such pattern, thus helping us in identifying such crashes.

/pdf/using-recurrence-plot-analysis-to-distinguish-between-46q2rzsr7u.pdf

Using recurrence plot analysis to distinguish between endogenous and exogenous stock market crashes

The experiences of the global financial crisis reveal that the spillover effects of the current global financial imbalances undermine the financial stability of different countries. In this emerging scenario, country-specific studies for identifying leading indicators of financial crisis appear important. This paper sets out an approach that develops an early warning model for banking crisis prediction in India, based on the ‘signals’ approach and multivariate probit regression model. Initially, using the Index method for identifying monthly crisis dates, four episodes of systemic banking crisis have been found to have occurred during 1994-2007. Investigating these crisis episodes by the ‘signals’ approach, results revealed increase in the spread between Bank Rate and 91-day T-Bill rate, increase in the ratio of short-term foreign debt to foreign exchange reserves, expansion of base money supply, economic slowdown, REER overvaluation and hike in LIBOR as some of the ‘leading’ indicators for banking crisis. The study also finds that if the value of the weighted composite indicator, constructed with the ‘leading’ indicators, exceeds 0.205, then the probability of banking crisis increases alarmingly in future months. Using probit regression, the identified indicators, determined by the ‘signals’ approach, have been found to be robust and the early warning model reflects satisfactory performance, both in-sample and out-of-sample. The paper also identifies the relevant variables associated with low, medium and high states of banking fragility based on an ordered probit regression model.Overall, the results confirm the significance of global economic conditions working upon domestic macroeconomic variables and the sensitivity of the banking sector to crisis. Further, most of the identified indicators, in the study, started emitting sufficient early signals of an upcoming banking fragility, which could be indicative of necessary pre-emptive measures on the part of the authorities.

https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=1906576

Forewarning Indicator System for Banking Crisis in India

The paper undertakes a macroprudential analysis of the credit risk of Public Sector Banks during the liberalization period. Using the Vector Autoregression methodology, the paper investigates the dynamic impact of changes in the macroeconomic variables on the default rate, the Financial Stability Indicator of banks by simulating interactions among all the variables included in the model. Feedback effects from the banking sector to the real economy are also estimated. The impact of variations in different Monetary Policy Instruments such as Bank Rate, Repo Rate and Reverse Repo Rate on the asset quality of banks is examined using three alternative baseline models. Impulse Response Functions of the estimated models are augmented by conducting sensitivity and scenario stress testing exercises to assess the banking sector’s vulnerability to credit risk in the face of hypothetically generated adverse macroeconomic shocks. 
Results indicate the absence of cyclicality and pro-cyclicality of the default rate. Adverse shocks to output gap, Real Effective Exchange Rate appreciation above its trend value, inflation rate and policy-induced monetary tightening significantly affect bank asset quality. Of the three policy rates, Bank Rate affects bank soundness with a lag and is more persistent while the two short-term rates impact default rate instantaneously but is much less persistent. Scenario stress tests reveal default rate of Public Sector Banks could increase on an average from 4% to 7% depending on the type of hypothetical macroeconomic scenario generated. An average buffer capital of 3% accumulated during the period under consideration could thus be inadequate for nearly twice the amount of Non-Performing Assets generated if macroeconomic conditions worsened. 
An important policy implication of the paper is that as the Indian economy moves gradually to Full Capital Account Convertibility, the banking sector is likely to come under increased stress in view of the exchange rate volatility with adverse repercussions on interest rates and bank default rates. In this emerging scenario, monetary policy stance thus emerges as an important precondition for banking stability. The study also highlights the inadequacy of existing capital reserves should macroeconomic conditions deteriorate and the urgency to strengthen the buffer capital position.

https://mpra.ub.uni-muenchen.de/30263/1/MPRA_paper_30263.pdf

Macroeconomic Stress Testing and the Resilience of the Indian Banking System: A Focus on Credit Risk

The relationship between the temporal evolution of the commodity market and the stock market has long term implications for policy makers, and particularly in the case of emerging markets, the economy as a whole. We analyze the complex dynamics of the daily variation of two indices of stock and commodity exchange respectively of India. To understand whether there is any difference between emerging markets and developed markets in terms of a dynamic correlation between the two market indices, we also examine the complex dynamics of stock and commodity indices of the US market. We compare the daily variation of the commodity and stock prices in the two countries separately. For this purpose we have considered commodity India along with Dow Jones Industrial Average (DJIA) and Dow Jones-AIG Commodity (DJ-AIGCI) indices for stock and commodities, USA, from June 2005 to August 2008. To analyse the dynamics of the time variation of the indices we use a set of analytical methods based on recurrence plots. Our studies show that the dynamics of the Indian stock and commodity exchanges have a lagged correlation while those of US market have a lead correlation and a weaker correlation.

Understanding the Interrelationship Between Commodity and Stock Indices Daily Movement Using ACE and Recurrence Analysis

This paper undertakes a Macro Prudential Analysis (MPA) of credit risk, the predominant risk category of Indian Public Sector Banks (PSBs). Assuming bank-specific shocks to be nil at the micro level, the paper employs a recursive Vector Auto-Regression (VAR) methodology to examine the transmission of shocks from major macroeconomic variables on the default rate of banks. The VAR model is applied to monthly data from the period 1994-2003. This is the first attempt in the Indian context that studies the links between asset quality and macroeconomic shocks using the VAR model. There is no evidence of cyclicality and procyclicality at one month lag as revealed by Granger causality tests. However Impulse Response Functions reveal the existence of cyclical and procyclical patterns over two months. Further shocks to exchange rate and monetary policy significantly affect bank asset quality. The implication of this study is that with the forthcoming Fuller Capital Account Convertibility (FCAC), banking sector in India is likely to be under increased stress in view of the exchange rate volatility and consequent rise in interest rates. In this scenario monetary policy emerges as an important precondition for banking stability. It may be suggested that the authorities need to take a balanced overview of financial stability in aggregate rather than focusing on price stability alone.

Basabi Bhattacharya

Papers

Using recurrence plot analysis to distinguish between endogenous and exogenous stock market crashes

Forewarning Indicator System for Banking Crisis in India

Macroeconomic Stress Testing and the Resilience of the Indian Banking System: A Focus on Credit Risk

Understanding the Interrelationship Between Commodity and Stock Indices Daily Movement Using ACE and Recurrence Analysis

Macroeconomic Determinants of Asset Quality of Indian Public Sector Banks: A Recursive VAR Approach