Market mikro yapısının faktörlerinin analizi: fiyat etkisi, likidite ve oynaklık

First chapter of this thesis is an attempt to model the price impact by extending themodel proposed by Kyle [54]. It is assumed that the market is not perfectly efficientso that it takes to time to adjust new equilibrium price. Thus, in order to model theprice impact, two new concepts are introduced which are market resiliency and speedof price informativeness. It is showed that market resiliency and price impact tend toraise as speed of price information increases which emphasizes the fact that speed ofinformation matters in financial markets and market resiliency is not a phenomenonthat can be neglected.In the second chapter, it is tried to stress the importance of the liquidity which is con-sidered as the neglected dimension of the financial risk. To do that, a new approachcalled Liquidity Augmented Stochastic Volatility with Jump (LASVJ) model is in-troduced and it is compared with the Stochastic Volatility with Jump (SVJ) model interms of stability and performance. This analysis includes both simulation and cali-bration analysis. The simulation results suggest that LASVJ model outperforms SVJas it has lower bias and Root Mean Square Error. In the calibration part, ten compa-nies listed in Dow-Jones 30 are used and it is found that the estimated probability ofdefault and credit spread with LASVJ model are higher than those with SVJ model.The 2008 Crisis period is even aggravated this result. The findings imply that thevii probability of default and credit spread are underestimated if liquidity dimension ofrisk is neglected and this partly accounts for why 2007-2008 financial crisis and itsfull-scale effect could not be predicted.In the third chapter, it is aimed to improve the volatility prediction which includedin the financial risk management. As a well-performing volatility prediction shedslight on the uncertainty in the financial market, it is an important task to model it. Tothis end, GARCH-type models as well as SVR-GARCH model are used to model thevolatility and the results are compared based on the performance metrics. In part ofempirical analysis thirty stocks listedS&P-500 are included and the period coveredis between 01/01/2010-09/01/2019. The finding indicates that SVR-GARCH outper-forms the traditional models in predicting volatility and also produce more reliableresult in Value-at-Risk estimation based on Proportion of Failures and Basel’s TrafficLight Backtesting approaches.Thesis (Ph.D.) -- Graduate School of Applied Mathematics. Financial Mathematics

Volatility Prediction and Risk Management: An SVR-GARCH Approach

his study aims first at improving volatility prediction using a machine learning model called support vector regression GARCH (SVR-GARCH) using selected 30 stocks listed on the S&P 500. The authors compare the prediction results of the SVR-GARCH model with the GARCH family models and find that SVR-GARCH outperforms these models based on the performance metrics. The second goal of this study is to calculate value-at-risk (VaR) using predictions obtained in the previous part. Moreover, backtesting is applied to check the accuracy of the VaR results. The findings suggest that using predictions obtained from the SVR-GARCH model boosts VaR calculations and hence provides better financial risk management

İlişkili Brown Hareketi varsayımı ile Alım-Satım Fiyat Farkının (Bid-Ask Spread) Hesaplanması ve Etkililiğinin Değerlendirilmesi

‘Market Microstructure’ son yıllarda oldukça önem kazanmış bir alan olup, hem akademik hem iş hayatında giderek daha çok dikkat çekmektedir. Literatürde, market micro structure belirli kurallar altında değerlerin değişiminin sonucu ve sürecini inceleyen bilim dalı’ olarak tanımlanmıştır. Bu çerçevede, biz projede alım-satım fiyat farkının belirlenmesi üzerine araştırma yapılacaktır.Bu fark piyasada işlem yapan yatırımcılar tarafından yüksek olarak algılanması durumunda, işlem hacmini dolayısıyla piyasadaki likidite miktarını düşürebilir. Aksi durumda, yani, alım-satım fiyat farkının olması gerekenden düşük belirlenmesi durumunda, piyasa yapıcısının karını düşürür, bu ise piyasada yeniliklerin ortaya çıkması konusunda isteksizlik yaratmakla birlikte yüksek frekanslı alım-satım yapan yatırımcıların çok fazla işlem yapmasına neden olabilmektedir. Çalışmada Borsa İstanbul’da işlem gören hisse senetleri verilerinden yararlanılarak belli bir dönem içindeki en yüksek ve en düşük hisse senedi fiyatları analizde kullanılacaktır. En yüksek ve en düşük verilerin kullanılması difüzyon katsayısının hesaplanmasında geleneksel metotlara göre %80 daha az veri kullanmasına rağmen 2,5-5 kat daha iyi sonuçlar verebilmektedir. Bu veriler yardımıyla simülasyon yapılarak kullanılan hesaplamanın başarısı değerlendirilecektir. Bu çalışmanın özgün katkısı geometrik Brown hareket varsayımı altında alım-satım fiyat farkı hesabını korelasyon halindeki çoklu değişkenler yardımıyla hesaplanmasıdır

Investigating the effects of illiquidity on credit risks via new liquidity augmented stochastic volatility jump diffusion model

Liquidity is extremely important not only within the context of financial markets but also in every scale of economic transactions. In this study, within the realm of financial markets, we configure liquidity as an independent stochastic process moderating the fluidity of all transactions and hence dynamically changing asset values. This study's asset value process ignoring liquidity is modelled with a stochastic volatility jump-diffusion (SVJ) model and that model is augmented with the incorporation of a liquidity process. The new model is called liquidity augmented stochastic volatility jump-diffusion (LASVJ) model. The simulation results suggest that LASVJ model outperforms the models without liquidity. The application of LASVJ model on the estimation of probabilities of default and credit risk spreads, using actual financial data of the selected companies listed in Dow Jones 30, reveals that neglecting the liquidity dimension in asset valuation might lead to inefficient assessments of risks. The models without an illiquidity process underestimate the probabilities of default and credit spread risks in comparison to the liquidity augmented model, LASVJ model, and we believe this might have accounted for the ignored risks that caused the 2007-2008 financial crisis in a great degree