The multi-scale analysis of dynamic transmission volatility of carbon prices

in Economics Bulletin (2022)

The implementation of the EU ETS in 2005 led to the establishment of a price that enables manufacturers to realize the impact of their activities on the environment clean. There are no items this day ... [more ▼]

The implementation of the EU ETS in 2005 led to the establishment of a price that enables manufacturers to realize the impact of their activities on the environment clean. There are no items this day, since the creation of the European carbon market, which has focused on analyzing volatility transmission between different investment horizons. The purpose of this paper is to fill this gap in the literature. we analyze the volatility of the price of carbon quota (EUA), by studying linear and nonlinear causal relationships of wavelet components between the different volatilities we captured at different time scales. we initially decomposed the EUA price volatility at different time-frequency intervals using a wavelet approach. Our study will be to examine whether the volatility is transmitted from the high-frequency structure of the carbon price in the low frequency. Our results show an intra-structural dependence on carbon price volatility. We detect instability in the volatility of carbon and observe the existence of a bidirectional relationship from high-frequency traders to low-frequency traders. Our study showed that high-frequency shocks yield carbon price can have a significant impact beyond their Frontiers and touch the low-frequency structure associated with long-term traders Keywords: Carbon market, EU ETS, Wavelet, time-scale, Granger Causality. [less ▲]

Investigating the nexus between Volatilities of Bitcoin, Gold, and American Stock Markets during the COVID-19 Pandemic: Evidence from ARMA-DCC-GARCH and NAR-NN Models

E-print/Working paper (2022)

The signature method : a new tool enhancing time series clustering

E-print/Working paper (2020)

Hedge Fund Return Dynamics: Long Memory and Regime Switching

in International Journal of Financial Research (2017)

Wavelet Dynamic Conditional Correlation GARCH model : WDCC-GARCH

E-print/Working paper (2016)

The effect of bank size on risk ratios: implications of banks' performance

in Procedia Economics and Finance (2015)

The aim of this study is to investigate the effects of bank capital and liquidity ratios on banks’ profitability. The analysis of these ratios makes it possible to observe the behaviour of the banks in ... [more ▼]

The aim of this study is to investigate the effects of bank capital and liquidity ratios on banks’ profitability. The analysis of these ratios makes it possible to observe the behaviour of the banks in terms of risk during the current period. The empirical analysis relates to a sample of 1270 European banks observed over the period 2005-2012. Three panels’ data are considered respectively large, medium and small banks in order to compare European banks according to their size. First, tests indicate homogeneity in behaviour of large banks. For the other samples, fixed effects regressions are implemented to insert individual specific effects in the models. To account for profitability persistence, we apply a dynamic panel model, using Generalized Methods of Moments (GMM). Estimation results show the evidence of positive and significant profitability persistence for medium sized bank. Finally, we find no real evidence of a positive relationship between greater efficiency and bank profitability. While capitalization levels increase bank profitability, liquidity risk depends on the size of the bank. [less ▲]

Hedge Fund Return Dynamics: long memory and regime switching

E-print/Working paper (2014)

This paper investigates the dynamics of hedge fund returns and their behavior of persistence in a unified framework through the Markov Switching ARFIMA model of Härdle and Tsay (2009). Major results based ... [more ▼]

This paper investigates the dynamics of hedge fund returns and their behavior of persistence in a unified framework through the Markov Switching ARFIMA model of Härdle and Tsay (2009). Major results based on the CSFB/Tremont hedge fund indexes monthly data during the period 1994-2012, highlight the importance of the long memory parameter magnitude i.e shocks in shaping hedge fund return dynamics and show that the hedge fund dynamics are characterized by two levels of persistence: in the first one, associated to low-volatility regime, hedge fund returns are a stationary long memory process whereas in the second one, associated to high-volatility regime, returns exhibit higher parameter of fractional integration. More precisely, in high volatility regime i.e periods of turmoil, the process tends to be non-stationary but still exhibits a mean-reverting behavior. The findings are interesting and enable us to establish a relationship between hedge fund return states and memory phenomenon. [less ▲]

Understanding Investment Funds- Insights from performance and risk analysis

Book published by Palgrave (2013)

A structural analysis of mutual fund performance: a comparative study for domiciliation places

in Journal of Index Investing (2011), 1(4), 81-91

The objective of this paper is to show similarities or differences of funds’ performance according to their domiciliation. Based on performance‐risk approach, it seems to be a little evidence of ... [more ▼]

The objective of this paper is to show similarities or differences of funds’ performance according to their domiciliation. Based on performance‐risk approach, it seems to be a little evidence of differences between groups of domicile using a classical descriptive analysis. In order to improve the consistency of our study, we implement a structural analysis in order to compare each domiciliation place. This structural analysis consists to construct fund synthetic indexes that capture the time structure of the mutual fund performance. Synthetic funds are funds portfolios which aim to duplicate a fund market in order to represent alternative benchmarks to compare the performance of investment funds. Risk analyses of indexes confirm some results obtained by the descriptive analysis and the gaps between the MSCI and fund synthetic indexes. We show that using the MSCI carelessly as a risk measure for much riskier market environments may lead the investor to severely underestimate downside risks and thus Value at Risk. [less ▲]

The fund Synthetic Index : An alternative benchmark for mutual funds

in Bankers, Markets, Investors [=BMI] (2011), 114

Evaluation of the performance of investment funds is a topic of considerable interest to practitioners and academic researchers. Performance indicators of fi nancial places have for long posed interesting ... [more ▼]

Evaluation of the performance of investment funds is a topic of considerable interest to practitioners and academic researchers. Performance indicators of fi nancial places have for long posed interesting challenges with regards to funds investors, but also to legal regulators authorities. The two major issues that need to be addressed in any performance study are how to choose an appropriate benchmark for comparison and how to adjust a fund’s return for risk. Indeed, investors desire information about representative market indexes as a norm to evaluate the performance of their portfolios. MSCI Indexes are frequently used by institutional investors around the world as benchmarks to decide allocation of funds across asset classes and regions. Despite this wide acceptance, MSCI country index has in its original application a number of drawbacks and limitations. The main problems can be traced to the presence of usual biases, such as sampling, survivorship and instant history biases (Fung and Shieh, 2002), involving problems into the aggregation procedure. Thus, one can explain why certain fi nancial places are less representative, specifi cally for funds distribution places. Some results also indicate problems related to misclassifi cation in mutual funds (Sharpe, 1992). Each of these phenomena can have a signifi cant impact on international diversifi cation for fund managers. Based on these empirical fi ndings, Ferreira, Miguel and Ramos (2007) examine cross-country mutual fund performance using several alternative benchmark models including a domestic and an international version of the Carhart (1997) four-factor model. Using multiple regressions, they obtain signifi cant determinants explaining funds performance, like the funds size, the fees, the management style… . In this article, we contribute to the existing discussion on alternative benchmarks to compare fi nancial places, by conducting an analysis on funds time variation structure. Contrary to previous literature, we propose to use directly the information contained in the NAV to extract performance characteristics of funds. Then, each domiciliation place is compared by constructing a fund synthetic index that will capture the time structure of mutual fund performance. Usual statistical approach consists to estimate fi nancial returns of each fund in each country, which involves dealing with huge data sets that may cause the calculation processes to become slow and cumbersome and the results diffi cult to be interpreted and used in further applications. To reduce data sets, and give conclusions for each fi nancial place, indicators of the mean of fund’s returns can be used. Then one can identify classes of domicile funds that are subject to common properties. But this classical approach, gives only approximate results because it is based on an aggregation of average performance and risk and a boxplot statistical format. The construction of fund synthetic portfolio avoids this issue. First, it avoids the logic of representativity through market capitalization that is often diffi cult to apply to the mutual fund universe. Second, it is based on factor analysis techniques to generate indexes that are able to capture a very large fraction of the information. More precisely, it permits to take into account the common properties of fund returns relative to their domicile while keeping the maximum of information given by the original data. Indeed, it may be very useful to use a transformation to form a simplifi ed data set retaining the characteristics of the original data set. Principal component analysis, abbreviated as PCA, is a method of statistical analysis useful in data reduction and interpretation of multivariate data sets by identifying factors of common behavior such that not much of the contained information is lost. In our context, we use this method to derive portfolio weights in order to construct a synthetic portfolio of each fi nancial place. For that, we propose to replace the matrix of returns and to derive an index which keeps the global representation of each fi nancial place. [less ▲]