Dr Emese Lazar
Dr Emese Lazar
- Undergraduate Programme Area Director
- Programme Director: MSc Financial Engineering
- Programme Director: Financial Investment Banking
- Associate Professor of Quantitative Finance
Profile & Expertise
Emese received a PhD in Finance from the ICMA Centre, The University of Reading in 2006. Previously she obtained an MSc in Financial Engineering and Quantitative Analysis with a distinction from the ICMA Centre. She graduated from the Academy of Economic Studies in Bucharest, with a BSc in Finance and Banking. Also, she holds a BSc in Computer Science obtained from the University of Bucharest, Faculty of Mathematics. Her research interests include: risk measurement and management, model risk, volatility and correlation models and their applications in pricing structured products. Emese presently teaches Market Risk and Derivatives Modelling.
- Financial Econometrics
- Market Risk
- Volatility Modelling
Key publications, books, research & papers
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Rethinking capital structure arbitrage: a price discovery perspective
Avino, D. and Lazar, E.
The capital structure arbitrage strategy exploits the discrepancies between the credit default swap and equity markets. It assumes that both markets instantaneously react to new information, so it fails to take into account the lead-lag relationships between the prices in the two markets and their form of cointegration. Here we introduce three new alternative strategies that exploit the information provided by the time-varying price discovery of the equity and credit markets and the cointegration of the two markets. We implement the strategies for both US and European obligors and find that these outperform traditional arbitrage trading during the financial crisis. Furthermore, the returns of the new strategies have lower correlation with market returns than the standard capital structure arbitrage.
Information entropy and measures of market risk
Pele, D. T., Lazar, E.
In this paper we investigate the relationship between the information entropy of the distribution of intraday returns and intraday and daily measures of market risk. Using data on the EUR/JPY exchange rate, we find a negative relationship between entropy and intraday Value-at-Risk, and also between entropy and intraday Expected Shortfall. This relationship is then used to forecast daily Value-at-Risk, using the entropy of the distribution of intraday returns as a predictor.
Time varying price discovery
We show how multivariate GARCH models can be used to generate a time-varying “information share” (Hasbrouck, 1995) to represent the changing patterns of price discovery in closely related securities. We find that time-varying information shares can improve credit spread predictions.
Price discovery of credit spreads in tranquil and crisis periods
In this paper we investigate the price discovery process in single-name credit spreads obtained from bond, credit default swap (CDS), equity and equity option prices. We analyse short term price discovery by modelling daily changes in credit spreads in the four markets with a vector autoregressive model (VAR). We also look at price discovery in the long run with a vector error correction model (VECM). We find that in the short term the option market clearly leads the other markets in the sub-prime crisis (2007-2009). During the less severe sovereign debt crisis (2009-2012) and the pre-crisis period, options are still important but CDSs become more prominent. In the long run, deviations from the equilibrium relationship with the option market still lead to adjustments in the credit spreads observed or implied from other markets. However, options no longer dominate price discovery in any of the periods considered. Our findings have implications for traders, credit risk managers and financial regulators.
Forecasting VaR using analytic higher moments for GARCH processes
It is widely accepted that some of the most accurate Value-at-Risk (VaR) estimates are based on an appropriately specified GARCH process. But when the forecast horizon is greater than the frequency of the GARCH model, such predictions have typically required time-consuming simulations of the aggregated returns distributions. This paper shows that fast, quasi-analytic GARCH VaR calculations can be based on new formulae for the first four moments of aggregated GARCH returns. Our extensive empirical study compares the Cornish–Fisher expansion with the Johnson SU distribution for fitting distributions to analytic moments of normal and Student t, symmetric and asymmetric (GJR) GARCH processes to returns data on different financial assets, for the purpose of deriving accurate GARCH VaR forecasts over multiple horizons and significance levels.
Futures basis, inventory and commodity price volatility: an empirical analysis
Symeonidis, L., Prokopczuk, M.
We employ a large dataset of physical inventory data on 21 different commodities for the period 1993–2011 to empirically analyze the behavior of commodity prices and their volatility as predicted by the theory of storage. We examine two main issues. First, we analyze the relationship between inventory and the shape of the forward curve. Low (high) inventory is associated with forward curves in backwardation (contango), as the theory of storage predicts. Second, we show that price volatility is a decreasing function of inventory for the majority of commodities in our sample. This effect is more pronounced in backwardated markets. Our findings are robust with respect to alternative inventory measures and over the recent commodity price boom.
Modelling regime-specific stock price volatility
Option valuation with normal mixture GARCH models
Badescu, A., Kulperger, R. and Lazar, E.
Normal mixture GARCH(1,1): applications to exchange rate modelling
Time aggregation of normal mixture GARCH models