Viljan hintariskiä vastaan suojautuminen Suomessa

Tässä tutkielmassa tarkastellaan viljan hintariskin merkitystä suomalaisen viljanviljelyyn keskittyneen maatalousyrittäjän tulonmuodostukseen ja vertaillaan keinoja, joilla tältä riskiltä voidaan tarvittaessa suojautua. Tutkielman tuloksena voidaan todeta seuraavaa: 1) Tarkasteluajanjaksolla 2000-2014 viljan hinnan volatiliteetti Suomessa on pääosin pysytellyt maltillisella tasolla, muutamaa poikkeusvuotta lukuunottamatta. 2) Suomalaisesta näkökulmasta maailman hyödykepörssien futuureista vain muutama soveltuu hintasuojauksen tehokkaaseen toteuttamiseen. 3) Viljan hintariskin kriittisyys viljelijälle riippuu vahvasti tämän tulonmuodostuksesta, joten on tapauskohtaista voidaanko hintasuojaksen toteutus katsoa tarpeelliseksi. 4) Mahdollisuudet viljan hintasuojauksen toteuttamiseen ovat Suomessa varsin rajalliset verrattuna esimerkiksi edelläkävijään Yhdysvaltoihin.

How Is Credit Risk Estimated? Discussion and Evidence based on Cross-Industry Sample Utilizing the KMV Model

The purpose of this thesis is to focus on credit risk estimation. Different credit risk estimation methods and characteristics of credit risk are discussed. The study is twofold, including an interview of a credit risk specialist and a quantitative section. Quantitative section applies the KMV model to estimate credit risk of 12 sample companies from three different industries: automobile, banking and financial sector and technology. Timeframe of the estimation is one year. On the basis of the KMV model and the interview, implications for analysis of credit risk are discussed. The KMV model yields consistent results with the existing credit ratings. However, banking and financial sector requires calibration of the model due to high leverage of the industry. Credit risk is considerably driven by leverage, value and volatility of assets. Credit risk models produce useful information on credit worthiness of a business. Yet, quantitative models often require qualitative support in the decision-making situation.

Effects of overvaluation and exchange rate volatility over industrial investment: empirical evidence and economic policy proposals for Brazil

The present article aims to analyze the recent behavior of real exchange rate in Brazil and its effects over investment per worker in Brazilian manufacturing and extractive industry. Preliminary estimates presented in the article shows an over-valuation of 48% of real exchange rate in Brazil. The reaction between the level (and volatility) of real exchange rate and investment (per worker) in Brazil is analyzed by means of a panel data econometric model for 30 sectors of Brazilian manufacturing and extractive industry. The empirical results show that the level and volatility of real exchange rate has a strong effect over investment per worker in Brazilian industry. Finally, we conclude the article presenting a proposal for a new macroeconomic regime that aims to produce an acceleration of economic growth of Brazilian economy and, by that, a catching-up process with developed countries.

Alhaisen volatiliteetin anomalia Suomen osakemarkkinoilla

Tutkielman tavoitteena on selvittää esiintyykö Suomen osakemarkkinoilla alhaisen volatiliteetin anomaliaa. Tutkielman tavoitteeseen vastataan työn empiirisessä osassa analysoimalla Suomen osakemarkkinoilla listattujen osakkeiden tuottoaikasarjoja. Tutkielmassa tarkastellaan myös finanssikriisin vaikutusta anomalian ilmenemiseen. Tutkimus sijoittuu aikavälille tammikuusta 2001 tammikuuhun 2015. Tutkielmassa muodostetaan portfolioita osakkeiden historiallisen volatiliteetin mukaan. Näiden portfolioiden menestymistä suhteessa markkinoihin arvioidaan absoluuttisten tuottojen, Sharpen luvun sekä Jensenin alfan avulla. Markkinaindekseinä käytetään OMXH CAP -indeksiä sekä tutkimusaineiston pohjalta muodostettua markkinaportfoliota. Kaikkein parhaimman absoluuttisen tuoton on saanut vuodesta 2001 vuoteen 2015 sijoittamalla keskiverron volatiliteetin osakkeisiin. Parhaan riskikorjatun tuoton on kuitenkin saavuttanut sijoittamalla alhaisen volatiliteetin osakkeisiin. Tutkielmassa löydetään todisteita alhaisen volatiliteetin anomalian esiintymisestä Suomen osakemarkkinoilla koko tutkimusaineisto huomioon ottaen. Tutkielman ehkä mielenkiintoisin löydös on kuitenkin huomio alhaisen volatiliteetin anomalian häviämisestä Suomen osakemarkkinoilta finanssikriisin jälkeen. Ennen finanssikriisiä esiintynyt erittäin vahva alhaisen volatiliteetin osakkeiden ylisuoriutuminen hävisi täysin finanssikriisin jälkeen. Toisin sanoen riskin ja tuoton suhde on kääntynyt päälaelleen finanssikriisin jälkeen, eikä alhaisen volatiliteetin anomaliaa voida enää sanoa esiintyvän.

Return and volatility linkages between the S&P 500 and emerging stock markets in Eastern Europe

The aim of this thesis is to research mean return spillovers as well as volatility spillovers from the S&P 500 stock index in the USA to selected stock markets in the emerging economies in Eastern Europe between 2002 and 2014. The sample period has been divided into smaller subsamples, which enables taking different market conditions as well as the unification of the World’s capital markets during the financial crisis into account. Bivariate VAR(1) models are used to analyze the mean return spillovers while the volatility linkages are analyzed through the use of bivariate BEKK-GARCH(1,1) models. The results show both constant volatility pooling within the S&P 500 as well as some statistically significant spillovers of both return and volatility from the S&P 500 to the Eastern European emerging stock markets. Moreover, some of the results indicate that the volatility spillovers have increased as time has passed, indicating unification of global stock markets.

Margin requirements and volatility : evidence from Canadian stocks

Margin policy is used by regulators for the purpose of inhibiting exceSSIve volatility and stabilizing the stock market in the long run. The effect of this policy on the stock market is widely tested empirically. However, most prior studies are limited in the sense that they investigate the margin requirement for the overall stock market rather than for individual stocks, and the time periods examined are confined to the pre-1974 period as no change in the margin requirement occurred post-1974 in the U.S. This thesis intends to address the above limitations by providing a direct examination of the effect of margin requirement on return, volume, and volatility of individual companies and by using more recent data in the Canadian stock market. Using the methodologies of variance ratio test and event study with conditional volatility (EGARCH) model, we find no convincing evidence that change in margin requirement affects subsequent stock return volatility. We also find similar results for returns and trading volume. These empirical findings lead us to conclude that the use of margin policy by regulators fails to achieve the goal of inhibiting speculating activities and stabilizing volatility.

Volume-Synchronized Probability of Informed Trading (VPIN), Market Volatility, and High-Frequency Liquidity

We assess the predictive ability of three VPIN metrics on the basis of two highly volatile market events of China, and examine the association between VPIN and toxic-induced volatility through conditional probability analysis and multiple regression. We examine the dynamic relationship on VPIN and high-frequency liquidity using Vector Auto-Regression models, Granger Causality tests, and impulse response analysis. Our results suggest that Bulk Volume VPIN has the best risk-warning effect among major VPIN metrics. VPIN has a positive association with market volatility induced by toxic information flow. Most importantly, we document a positive feedback effect between VPIN and high-frequency liquidity, where a negative liquidity shock boosts up VPIN, which, in turn, leads to further liquidity drain. Our study provides empirical evidence that reflects an intrinsic game between informed traders and market makers when facing toxic information in the high-frequency trading world.

Aggregations and Marginalization of GARCH and Stochastic Volatility Models

The GARCH and Stochastic Volatility paradigms are often brought into conflict as two competitive views of the appropriate conditional variance concept : conditional variance given past values of the same series or conditional variance given a larger past information (including possibly unobservable state variables). The main thesis of this paper is that, since in general the econometrician has no idea about something like a structural level of disaggregation, a well-written volatility model should be specified in such a way that one is always allowed to reduce the information set without invalidating the model. To this respect, the debate between observable past information (in the GARCH spirit) versus unobservable conditioning information (in the state-space spirit) is irrelevant. In this paper, we stress a square-root autoregressive stochastic volatility (SR-SARV) model which remains true to the GARCH paradigm of ARMA dynamics for squared innovations but weakens the GARCH structure in order to obtain required robustness properties with respect to various kinds of aggregation. It is shown that the lack of robustness of the usual GARCH setting is due to two very restrictive assumptions : perfect linear correlation between squared innovations and conditional variance on the one hand and linear relationship between the conditional variance of the future conditional variance and the squared conditional variance on the other hand. By relaxing these assumptions, thanks to a state-space setting, we obtain aggregation results without renouncing to the conditional variance concept (and related leverage effects), as it is the case for the recently suggested weak GARCH model which gets aggregation results by replacing conditional expectations by linear projections on symmetric past innovations. Moreover, unlike the weak GARCH literature, we are able to define multivariate models, including higher order dynamics and risk premiums (in the spirit of GARCH (p,p) and GARCH in mean) and to derive conditional moment restrictions well suited for statistical inference. Finally, we are able to characterize the exact relationships between our SR-SARV models (including higher order dynamics, leverage effect and in-mean effect), usual GARCH models and continuous time stochastic volatility models, so that previous results about aggregation of weak GARCH and continuous time GARCH modeling can be recovered in our framework.

Jumps in the Volatility of Financial Markets.

Recent work suggests that the conditional variance of financial returns may exhibit sudden jumps. This paper extends a non-parametric procedure to detect discontinuities in otherwise continuous functions of a random variable developed by Delgado and Hidalgo (1996) to higher conditional moments, in particular the conditional variance. Simulation results show that the procedure provides reasonable estimates of the number and location of jumps. This procedure detects several jumps in the conditional variance of daily returns on the S&P 500 index.

Testing Normality : A GMM Approach

In this paper, we consider testing marginal normal distributional assumptions. More precisely, we propose tests based on moment conditions implied by normality. These moment conditions are known as the Stein (1972) equations. They coincide with the first class of moment conditions derived by Hansen and Scheinkman (1995) when the random variable of interest is a scalar diffusion. Among other examples, Stein equation implies that the mean of Hermite polynomials is zero. The GMM approach we adopted is well suited for two reasons. It allows us to study in detail the parameter uncertainty problem, i.e., when the tests depend on unknown parameters that have to be estimated. In particular, we characterize the moment conditions that are robust against parameter uncertainty and show that Hermite polynomials are special examples. This is the main contribution of the paper. The second reason for using GMM is that our tests are also valid for time series. In this case, we adopt a Heteroskedastic-Autocorrelation-Consistent approach to estimate the weighting matrix when the dependence of the data is unspecified. We also make a theoretical comparison of our tests with Jarque and Bera (1980) and OPG regression tests of Davidson and MacKinnon (1993). Finite sample properties of our tests are derived through a comprehensive Monte Carlo study. Finally, three applications to GARCH and realized volatility models are presented.

Correcting the Errors : A Note on Volatility Forecast Evaluation Based on High-Frequency Data and Realized Volatilities

This note develops general model-free adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit the recent asymptotic distributional results in Barndorff-Nielsen and Shephard (2002a), are both easy to implement and highly accurate in empirically realistic situations. On properly accounting for the measurement errors in the volatility forecast evaluations reported in Andersen, Bollerslev, Diebold and Labys (2003), the adjustments result in markedly higher estimates for the true degree of return-volatility predictability.

Arbitrage-Based Pricing when Volatility is Stochastic.

The paper investigates the pricing of derivative securities with calendar-time maturities.