Author: Lidan GrossmassPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Three Essays on Using High Frequency Data in Estimating Financial Risks
Author: Lidan GrossmassThree Essays on High Frequency Financial Data and Their Use for Risk Management
Author: Maria PacurarPublisher:
ISBN:
Category : Monte Carlo method
Languages : en
Pages : 0
Book Description
Three Essays on Financial Risks Using High Frequency Data
Author: Serge Luther Nyawa WomoPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
This thesis is about financial risks and high frequency data, with a particular focus on financial systemic risk, the risk of high dimensional portfolios and market microstructure noise. It is organized on three chapters. The first chapter provides a continuous time reduced-form model for the propagation of negative idiosyncratic shocks within a financial system. Using common factors and mutually exciting jumps both in price and volatility, we distinguish between sources of systemic failure such as macro risk drivers, connectedness and contagion. The estimation procedure relies on the GMM approach and takes advantage of high frequency data. We use models' parameters to define weighted, directed networks for shock transmission, and we provide new measures for the financial system fragility. We construct paths for the propagation of shocks, firstly within a number of key US banks and insurance companies, and secondly within the nine largest S&P sectors during the period 2000-2014. We find that beyond common factors, systemic dependency has two related but distinct channels: price and volatility jumps. In the second chapter, we develop a new factor-based estimator of the realized covolatility matrix, applicable in situations when the number of assets is large and the high-frequency data are contaminated with microstructure noises. Our estimator relies on the assumption of a factor structure for the noise component, separate from the latent systematic risk factors that characterize the cross-sectional variation in the frictionless returns. The new estimator provides theoretically more efficient and finite-sample more accurate estimates of large-scale integrated covolatility, correlation, and inverse covolatility matrices than other recently developed realized estimation procedures. These theoretical and simulation-based findings are further corroborated by an empirical application related to portfolio allocation and risk minimization involving several hundred individual stocks. The last chapter presents a factor-based methodology to estimate microstructure noise characteristics and frictionless prices under a high dimensional setup. We rely on factor assumptions both in latent returns and microstructure noise. The methodology is able to estimate rotations of common factors, loading coefficients and volatilities in microstructure noise for a huge number of stocks. Using stocks included in the S&P500 during the period spanning January 2007 to December 2011, we estimate microstructure noise common factors and compare them to some market-wide liquidity measures computed from real financial variables. We obtain that: the first factor is correlated to the average spread and the average number of shares outstanding; the second and third factors are related to the spread; the fourth and fifth factors are significantly linked to the closing log-price. In addition, volatilities of microstructure noise factors are widely explained by the average spread, the average volume, the average number of trades and the average trade size.
Three Essays on Estimation and Dynamic Modelling of Multivariate Market Risks Using High Frequency Financial Data
Author: Valeri VoevThree Essays on High Frequency Financial Econometrics and Individual Trading Behavior
Author: Essays on High-frequency Financial Data Analysis
Author: Yingjie DongPublisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 137
Book Description
"This dissertation consists of three essays on high-frequency financial data analysis. I consider intraday periodicity adjustment and its effect on intraday volatility estimation, the Business Time Sampling (BTS) scheme and the estimation of market microstructure noise using NYSE tick-by-tick transaction data. Chapter 2 studies two methods of adjusting for intraday periodicity of highfrequency financial data: the well-known Duration Adjustment (DA) method and the recently proposed Time Transformation (TT) method (Wu (2012)). I examine the effects of these adjustments on the estimation of intraday volatility using the Autoregressive Conditional Duration-Integrated Conditional Variance (ACD-ICV) method of Tse and Yang (2012). I find that daily volatility estimates are not sensitive to intraday periodicity adjustment. However, intraday volatility is found to have a weaker U-shaped volatility smile and a biased trough if intraday periodicity adjustment is not applied. In addition, adjustment taking account of trades with zero duration (multiple trades at the same time stamp) results in deeper intraday volatility smile..."--Author's abstract.
Three Essays on Financial Liberalization, Country Risk and Low Growth Traps in Argentina, Mexico and Turkey
Author: Firat DemirPublisher:
ISBN:
Category : Argentina
Languages : en
Pages : 292
Book Description
Three Essays in Monetary and Financial Economics
Author: Liang MaPublisher:
ISBN:
Category : Economics
Languages : en
Pages : 0
Book Description
This dissertation consists of three essays in the field of monetary and financial economics. Specifically, we use high-frequency financial data to study monetary policies with a focus on the information effect, namely, that some of the interest rate movements around central bank announcements are not policy-driven, but are results of the market becoming aware of the central bank's view about future economic prospects. Understanding the role played by the information effect will help us apprehend monetary policy implications in both normal times and extraordinary situations. Chapter 1 evaluates the impact of unconventional monetary policy in the newly developed instrumental variable structural Vector Autoregression (VAR) framework. In the current low interest rate environment, central banks must resort to using unconventional monetary policies, such as forward guidance and quantitative easing, to flight recessions. To empirically evaluate the effectiveness of these unconventional policies, we need to rely on the clean policy shock. A prominent concern is that the often used high-frequency interest rate surprises not only reflect unexpected policy changes, but also contain the information effect. We contribute to the literature by using a heteroskedasticity identification approach, taking advantage of changes in the relative dominance of economic shocks around different macroeconomic announcements. Analysis based on clean policy shocks suggests that the unconventional policies successfully aided the recovery in the U.S. More importantly, we show that the information effect, while it may introduce bias, is rather modest when it comes to estimating the real impact of unconventional monetary policies. Chapter 2 studies the stock return pattern after the U.S. Federal Open Market Committee (FOMC) announcement. This research is motivated by recent literature that documents stock returns drifts, both before and after FOMC announcements, according to policy rate surprises. Indeed, research has shown that the information contained in the central bank announcement is multifaceted: its current monetary policy stances (monetary policy news) and news about future economic prospects (non-monetary policy news). Our contribution is to combine these two strands of literature. To the best of our knowledge, no study has looked at stock market reactions to the non-monetary news stemming from policy announcements. We identify both good and bad news events using a combination of sign restriction with high-frequency financial prices. The novel finding is that following bad FOMC announcements, that is the market interpreted the Fed announcements as revealing negative information about the economy, we observe significant positive stock returns in a 20-day period. We call this the ``post-FOMC drift.'' Further analysis suggests that the drift is likely caused by relatively heightened risks associated with bad announcements, although the drift is consistent with market overreactions as well. Moreover, the post FOMC drift is a market-wide phenomenon and can be exploited in an easy-to-implement trading strategy with a historical record of earning 40\% of the annual equity premium. In Chapter 3, we explore the channels through which the FOMC announcements affect the financial market. While much of the existing literature measures the surprise components with only changes in policy rates (surrounding the announcement), we contribute to the existing literature by taking a broader view through examining unexpected changes in longer-term yields, corporate credit spreads, and inflation expectations (a proxy for growth prospects), using high-frequency financial data. Through a regression analysis, our findings show that these additional surprises provide orthogonal information and sharply increase the goodness of fit in explaining stock returns around FOMC announcements, with the inclusion of inflation expectations having the biggest contribution. The important role of inflation expectation suggests that the current literature, which uses stock prices together with nominal rates to disentangle the information contents of central bank announcements, may be too limited in the scope of information it uses.
Author: 
Author: Kai Yao