Forecasting Volatility of Turkish Markets: A Comparison of Thin and Thick Models

Volatility of financial markets is an important topic for academics, policy makers and market participants. In this study first I summarized several specifications for the conditional variance and also define some methods for combination of these specifications. Then assuming that the squared returns are the benchmark estimate for actual volatility of the day, I compare all of the models with respect to how much efficient they are to mimic the realized volatility. At the same time I used a VaR approach to compare these forecasts. With the help of these analyses I examine if combination of the forecast could outperform the single models.volatility, arch, garch, combination, VaR

Violation duration as a better way of VaR model evaluation : evidence from Turkish market portfolio

Financial crisis those we have been experienced during last two decades encouraged the efforts of both academicians and the market participants to develop clear representations of the risk exposure of a �nancial institute. As a useful tool for measuring market risk of a portfolio, Value-at-Risk has emerged as the standard. However, there are several alternative Value-at-Risk implementations which may pro- duce signi�cantly di¤erent Value-at-Risk forecasts. Thus, evaluation of Value-at-Risk forecasts is as crucial as VaR itself. In this paper I will use the methodology which has described by Christoffersen and Pelletier[6] and I extended the methodology to create duration based analogous of unconditional coverage, conditional coverage and inde- pendence tests. I evaluated 14 Value-at-Risk implementation by using a Turkish Market portfolio which contain foreing currency, stock and bonds.Value-at-Risk; model evaluation; conditional cover- age; duration based coverage testing

Secrecy Capacity of a Class of Broadcast Channels with an Eavesdropper

We study the security of communication between a single transmitter and multiple receivers in a broadcast channel in the presence of an eavesdropper. We consider several special classes of channels. As the first model, we consider the degraded multi-receiver wiretap channel where the legitimate receivers exhibit a degradedness order while the eavesdropper is more noisy with respect to all legitimate receivers. We establish the secrecy capacity region of this channel model. Secondly, we consider the parallel multi-receiver wiretap channel with a less noisiness order in each sub-channel, where this order is not necessarily the same for all sub-channels. We establish the common message secrecy capacity and sum secrecy capacity of this channel. Thirdly, we study a special class of degraded parallel multi-receiver wiretap channels and provide a stronger result. In particular, we study the case with two sub-channels two users and one eavesdropper, where there is a degradedness order in each sub-channel such that in the first (resp. second) sub-channel the second (resp. first) receiver is degraded with respect to the first (resp. second) receiver, while the eavesdropper is degraded with respect to both legitimate receivers in both sub-channels. We determine the secrecy capacity region of this channel. Finally, we focus on a variant of this previous channel model where the transmitter can use only one of the sub-channels at any time. We characterize the secrecy capacity region of this channel as well.Comment: Submitted to EURASIP Journal on Wireless Communications and Networking (Special Issue on Wireless Physical Layer Security

A Nonparametric Way of Distribution Testing

Testing the distribution of a random sample can be considered ,indeed, as a goodness-of-fit problem. If we use the nonparametric density estimation of the sample as a consistent estimate of exact distribution, the problem reduces, more specifically, to the distance of two functions. This paper examines the distribution testing from this point of view and suggests a nonparametric procedure. Although the procedure is applicable for all distributions, paper emphasizes on normality test.The critical values for this normality test generated by using Monte Carlo techniques.distribution testing, normality, monte carlo simulation