Asset pricing models are well established and have been used extensively by practitioners both for pricing options as well as for hedging them. Though Black-Scholes is the original and most commonly communicated asset pricing model, alternative asset pricing models which incorporate additional features have since been developed. We present three asset pricing models here - the Black-Scholes model, the Heston model and the Merton (1976) model. For each asset pricing model we test the hedge effectiveness of delta hedging, minimum variance hedging and static hedging, where appropriate. The options hedged under the aforementioned techniques and asset pricing models are down-and-out call options, lookback options and cliquet options. The hedges are performed over three strikes, which represent At-the-money, Out-the-money and In-the-money options. Stock prices are simulated under the stochastic-volatility double jump diffusion (SVJJ) model, which incorporates stochastic volatility as well as jumps in the stock and volatility process. Simulation is performed under two ’Worlds’. World 1 is set under normal market conditions, whereas World 2 represents stressed market conditions. Calibrating each asset pricing model to observed option prices is performed via the use of a least squares optimisation routine. We find that there is not an asset pricing model which consistently provides a better hedge in World 1. In World 2, however, the Heston model marginally outperforms the Black-Scholes model overall. This can be explained through the higher volatility under World 2, which the Heston model can more accurately describe given the stochastic volatility component. Calibration difficulties are experienced with the Merton model. These difficulties lead to larger errors when minimum variance hedging and alternative calibration techniques should be considered for future users of the optimiser.

The stock market have received a fair amount of attention in the media recently as a result of the ongoing covid-19 pandemic. The question arouse if there is one month in the year that outperforms all other months in the stock market. A well known anomaly in the world of finance referred to as, the January effect, came up to discussion. Earlier studies of this subject have achieved different results and conclusions. Therefore, this study aims to examine if the January effect exists on mid cap and large cap companies on the Swedish stock market. To achieve this, one large cap portfolio and one mid cap portfolio both equally weighted with ten companies each were created. These two portfolios were analyzed with, among others, a well known regression model for season anomalies. The results of this study concludes that the January effect does not exist in neither of the portfolios.

Anomalier ligger till grund för investeringsstrategier som används för att förvalta biljontals dollar. Anomalier frångår vedertagen teori som implicerar att abnormal avkastning inte är möjlig över tid. En anomali som bevisats ge långsiktig abnormal avkastning är lågriskanomalin. Det råder brist på studier som undersökt om lågriskanomalin existerar på den svenska aktiemarknaden. En studie inom ämnet är av praktisk relevans då resultaten kan ligga till grund för investeringsstrategier som genererar abnormal avkastning. Denna studies syfte är att undersöka om en lågriskanomali existerar på den svenska aktiemarknaden under tidsperioden 2008/01/07 - 2019/12/27. För studien används totalavkastningsdata för bolag noterade på Stockholmsbörsen. Studien skapar hedgeportföljer och undersöker genom regression av prissättningsmodellen CAPM om lågriskportföljer ger högre signifikant abnormal avkastning än högriskportföljer. Studien finner inga statistiskt säkerställda resultat för en lågriskanomali. Det antyder att lågriskanomalin inte existerar på den svenska aktiemarknaden och därför saknar praktisk relevans som investeringsstrategi på nämnda marknad.

How are the prices of financial assets determined? In this dissertation, I test various theories empirically, focusing on several classes of bonds. In the first chapter, I test whether asset prices reflect the risk-exposures of financial intermediaries in a setting that is well suited to tackling concerns about omitted risk factors. I analyze catastrophe bonds whose cash flows are linked to the occurrence of natural disasters and find that 71% of the variation in their expected returns can be explained by a theoretically-motivated measure of financial intermediaries’ marginal rate of substitution. Assuming that natural disasters are independent of aggregate wealth, this pricing result is inconsistent with any explanation based on macroeconomic risk factors. However, the result is consistent with intermediary asset pricing models that suggest that financial intermediaries are marginal investors in capital markets. I also show that the premium on natural disaster risk has decreased significantly in recent years and has become less responsive to the occurrence of disasters, suggesting that intermediaries’ access to outside capital has improved over time. In the second chapter, which is coauthored with Robert J. Hodrick, we examine the statistical term structure model of Cochrane and Piazzesi (2005) and its affine counterpart, developed in Cochrane and Piazzesi (2008), in several out-of-sample analyzes. The model’s one-factor forecasting structure across bonds with two, three, four, and five years to maturity characterizes the term structures of additional major currencies in samples ending in 2003. In post-2003 data such one-factor structures again characterize each currency’s term structure, but we reject equality of the coefficients across the two samples. We derive currency return forecasting implications from the Cochrane and Piazzesi (2008) affine model showing that the term structure forecasting variables in each currency should predict cross-currency investments, but we find no support for these predictions in either pre-2004 or post-2003 data, whereas the interest differentials do predict currency returns. Here too, though, we find strong evidence of parameter instability as the parameter estimates on the interest differentials change sign. In recursive out-of-sample forecasts of excess rates of return on bonds in each currency, the Cochrane and Piazzesi (2008) term structure forecasting models fail to beat forecasts from the historical average excess rates of return. Graphical analysis indicates that the instability in the forecasting models’ parameters begins in the global financial crisis.

This dissertation studies topics in Passive Investing and Asset Pricing. The first chapter, "When do flows matter for asset prices: Evidence from adoption of ETF creation in Israel," focuses on the effect of capital allocation to passive investments on asset prices. We study a 2012 reform in Israel where all exchange traded products listed on the Tel Aviv Stock Exchange (TASE) adopted the Exchange Traded Fund (ETF) creation mechanism wherein designated market makers arbitrage between the index price and the net asset value of its benchmark. The reform greatly decreased the cost of this arbitrage activity and translated into a significant increase in demand for passive investments. The effect was stronger for illiquid indices containing smaller stocks. We show that the price effects of the reform were dramatically higher for stocks located at the top of indices composed of smaller stocks relative to stocks at the bottom of indices composed of bigger stocks. A 1 p.p. increase in passive ownership as a percent of market capitalization leads to an 11.7% increase in the price of stocks. Our findings provide new evidence on how passive inflows can change the distribution of capital across indices, and in turn impact price efficiency. In the second chapter, "Passive Investing and Algorithmic Trading," I examine the trading behavior of market participants against the growing demand for passive investing. I show that the growth of passive investing and algorithmic trading is complementary and mutually reinforcing. Algorithmic traders respond to a spike in demand for passive investments listed on the TASE following a major reform in the Israeli index market in 2012 by front-running the index inflows. Algorithmic traders accumulate stocks when index inflows are low and sell stocks when they are high. Based on this strategy, algorithmic traders earn a 12.7% annualized return in realized gains over passive strategies in the same period. Instead, Mutual Funds load on the index when inflows are high.

Asset prices are forward looking. Therefore, expectations play a central role in shaping asset prices. In this dissertation, I challenge the rational expectation assumption that has been influential in the field of asset pricing over the past few decades. Different from previous approaches, which typically build on behavioral theories originated from psychology literature, my approach takes data on subjective beliefs seriously and proposes empirically grounded models of subjective beliefs to evaluate the merits of the rational expectation assumption. Specifically, this dissertation research: 1). collects and analyzes data on investors' actual subjective return expectations; 2). builds models of subjective expectation formation; 3). derives and tests the models' implications for asset prices. I document the results of the research in two chapters. In summary, the dissertation shows that investors do not hold full-information rational expectations. On the other hand, their subjective expectations are not necessarily irrational. Rather, they are bounded by the information environment investors face and reflect investors' personal experiences and preferences. The deviation from fully-rational expectations can explain asset pricing anomalies such as cross-sectional anomalies in the U.S. stock market. In the first chapter, I provide a framework to rationalize the evidence of extrapolative return expectations, which is often interpreted as investors being irrational. I first document that subjective return expectations of Wall Street (sell-side, buy-side) analysts are contrarian and counter-cyclical. I then highlight the identification problem investors face when theyform return expectations using imperfect predictors through Kalman Filters. Investors differ in how they impose subjective priors, the same way rational agents differ in different macro-finance models. Estimating the priors using surveys, I find Wall Street and Main Street (CFOs, pension funds) both believe persistent cash flows drive asset prices but disagree on how fundamental news relates to future returns. These results support models featuring heterogeneous agents with persistent subjective growth expectations. In the second chapter, I propose and test a unifying hypothesis to explain both cross-sectional return anomalies and subjective return expectation errors: some investors falsely ignore the dynamics of discount rates when forming return expectations. Consistent with the hypothesis: 1) stocks' expected cash flow growth and idiosyncratic volatility explain significant cross-sectional variation of analysts' return forecast errors; 2). a measure of mispricing at the firm level strongly predicts stock returns, even among stocks in the S&P500 and at long horizon; 3). a tradable mispricing factor explains the CAPM alphas of 12 leading anomalies including investment, profitability, beta, idiosyncratic volatility and cash flow duration.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第20350号 / 工博第4287号 / 新制||工||1664(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 小林 潔司, 教授 大津 宏康, 准教授 松島 格也 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

A thesis submitted to the School of Economic and Business Sciences, Faculty of Commerce, Law and Management, University of the Witwatersrand in fulfilment of the requirements for the degree of Master of Commerce (M.Com) in Finance, Johannesburg June 2017 / Traditional finance theory surrounding the risk-return relationship is underpinned by the CAPM which posits that a single risk factor, specifically market risk, is priced into asset returns. Even though it is a popular asset pricing model, the CAPM has been widely criticised due to its unrealistic assumptions and the APT was developed to address the CAPM’s weaknesses. The APT framework allows for a multitude of risk factors to be priced into asset returns; implying that it can be used to model returns using either macroeconomic or microeconomic factors. As such, the APT allows for non-traditional factors, such as investor sentiment, to be included. A macroeconomic APT framework was developed for nine countries using the variables outlined by Chen, Roll, and Ross (1986) and investor sentiment was measured by the FEARS index (Da, Engelberg, & Gao, 2015). Regression testing was used to determine whether FEARS is a statistically significant explanatory variable in the APT model for each country. The results show that investor sentiment is a statistically significant explanatory variable for market returns in five out of the nine countries examined. These results add to the existing APT literature as they show that investor sentiment has a significant explanatory role in explaining asset prices and their associated returns. The international nature of this study allows it to be extended by considering the role that volatility spill-over or the contagion effect would have on each model. / XL2018

Thesis (M.M. (Finance & Investment)--University of the Witwatersrand, Faculty of Commerce, Law and Management, Wits Business School, 2016 / This study investigates how interest rates impact the South African Stock market. We investigate how the selected interest rates proxies predict the level of the FTSE/JSE All Share Index returns. The vector auto-regression (VAR) model was estimated and interpreted, based on the monthly data from June 1995 to September 2014. Using tools such as Granger causality, impulse response function and variance decomposition, we found that the selected variables did not significantly influence the FTSE/JSE All Share Index returns. Consequently, these variables are not useful as predictive tools for the South African stock market returns. / MT2017

The random walk is a powerful model. Chemistry, Physics, and Finance are just a few of the disciplines that model with the random walk. It is clear from its varied uses that despite its simplicity, the simple random walk it very flexible. There is one major drawback, however, to the simple random walk and the geometric random walk. The limiting distribution is either normal, lognormal, or a levy process with infinite variance. This thesis introduces an new random walk aimed at overcoming this drawback. Because the simple random walk and the geometric random walk are special cases of the proposed walk, it is called a generalized random walk. Several properties of the generalized random walk are considered. First, the limiting distribution of the generalized random walk is shown to include a large class of distributions. Second and in conjunction with the first, the generalized random walk is compared to the geometric random walk. It is shown that when parametrized properly, the generalized random walk does converge to the lognormal distribution. Third, and perhaps most interesting, is one of the limiting properties of the generalized random walk. In the limit, generalized random walks are closely connected with a u function. The u function is the key link between generalized random walks and its difference equation. Last, we apply the generalized random walk to option pricing.