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In this paper, we search to evaluate the systemic risk of the Moroccan banking sector. Indeed, we concentrate on the analysis and the evaluation on transverse dimension of the systemic. From this point of view, two approaches were used. First is based on the estimate on value at risk conditional allowing to measure the systemic importance of each banking institution. In addition, the second approach uses the heteroscedasticity models in order to consider the conditional correlations, making it possible, to measure the dependence between the Moroccan banks and with the whole of the financial system. The results obtained with through these two approaches confirm that ATW, BMCI and the BMCE are the most systemic banks in Moroccan banking system and who can initiate a systemic crisis. On another register and by using the conditional correlations of each bank we built an index of systemic risk. Moreover, a macrofinancial model was developed, connecting the index of the systemic risk and the principal macroeconomic variables. This model affirmed that the contagion dimension of systemic risk is procyclical.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

This paper studies the pricing of collateralized debt obligation (CDO) using Monte Carlo and analytic methods. Both methods are developed within the framework of the reduced form model. One-factor Gaussian Copula is used for treating default correlations amongst the collateral portfolio. Based on the two methods, the portfolio loss, the expected loss in each CDO tranche, tranche spread, and the default delta sensitivity are analyzed with respect to different parameters such as maturity, default correlation, default intensity or hazard rate, and recovery rate. We provide a careful study of the effects of different parametric impact. Our results show that Monte Carlo method is slow and not robust in the calculation of default delta sensitivity. The analytic approach has comparative advantages for pricing CDO. We also employ empirical data to investigate the implied default correlation and base correlation of the CDO. The implication of extending the analytical approach to incorporating Levy processes is also discussed.

The study provides medium-term estimates of recovery paths for Indian economy using a dynamic factor (DF)-based approach that employs data on high-frequency indicators à la Bhadury, Ghosh, and Kumar (2020). The DFs are used to analyze the post-pandemic recovery and convergence with its pre-COVID-19 trend for India between March 2021 and March 2022. A broad sectoral assessment of the impact of COVID-19 is also conducted. In addition, forward-looking measures based on stock returns are used to analyze the transmission of additional banking sector risks to the real sectors by constructing daily delta conditional value-at-risk (CoVaR) estimates. Our estimates based on the DFs suggest that the aggregate economic activities may catch up to the estimated pre-COVID trend by March 2021 predominantly driven by the growth in services sector. The industrial sector and consumer goods sector continue to show moderate signs of recovery. Our CoVaR estimates corroborate these findings. Banking sector transmission risk is among the lowest for services such as healthcare and information technology (IT), for both the lockdown period between March 25 and June 8, 2020, and for the latter months. The transmission risk continues to remain high for metal, oil and gas, and capital goods sector. Broadly, the evidence on forward-looking banking sector risk transmission for major sectors is in alignment with our finding on their recovery based on DF models, after easing of COVID-19 lockdown.

This chapter investigates the presence of a difference in the systemic risk level between Islamic and conventional banks in Bangladesh. The authors compare systemic resilience of three types of banks: fully fledged Islamic banks, purely conventional banks (CB), and CB with Islamic windows. The authors use the market-based systemic risk measures of marginal expected shortfall and systemic risk to identify which type is more vulnerable to a systemic event. The authors also use ΔCoVaR to identify which type contributes more to a systemic event. Using a sample of observations on 27 publicly traded banks operating over the 2005–2014 period, the authors find that CB is the least resilient sector to a systemic event, and is the one that has the highest contribution to systemic risk during crisis times.

This chapter studies the dynamic responses of the conditional quantiles and their applications in macroeconomics and finance. The authors build a multi-equation autoregressive conditional quantile model and propose a new construction of quantile impulse response functions (QIRFs). The tool set of QIRFs provides detailed distributional evolution of an outcome variable to economic shocks. The authors show the left tail of economic activity is the most responsive to monetary policy and financial shocks. The impacts of the shocks on Growth-at-Risk (the 5% quantile of economic activity) during the Global Financial Crisis are assessed. The authors also examine how the economy responds to a hypothetical financial distress scenario.

In this chapter we concentrate at the most popular model for convertible bond (CB) valuation in a one-factor, stochastic underlying stock price setting. Through the last decade, the Tsiveriotis–Fernandes model (1998) has become a widely commented model that involves the state of default of the issuer of the CB. A routine approach to the solution of this model is the usage of methods of finite difference schemes (FDS). However, for many people trained in finance these methods are not very intuitive and they tend to ignore them and prefer to use binomial-tree approach as more intuitive technique. For that reason, our primary focus is to highlight the answer of the so far unanswered question: Does the binomial-tree approach to CBs provide accurate pricing, hedging, and risk assessment? We show on a set of representative examples that by using binomial-tree methodology one is unable to provide a consistent analysis of the pricing, hedging, and risk assessment. We start the chapter with the basics of CBs and CB market. We then explain the implementation of TF model within binary-tree approach. We conclude the chapter with performance valuation of binomial-tree approach showing unexpected behavior in practice areas such as pricing (profile of CB's price versus underlying stock price), hedging (performance of CB's delta, gamma, and convertible arbitrage strategy versus underlying stock), and risk assessment (Monte Carlo VaR with respect to the underlying).