Doctor of Philosophy / Department of Economics / Steven P. Cassou / This dissertation consists of three essays in asymmetric empirical macroeconomics. Making macroeconomic policies has become increasingly difficult because of intricate relationships among macroeconomic variables. In this dissertation, we apply state-of-the-art macroeconometric techniques to investigate asymmetric relationships between key macroeconomic aggregates. Our findings have important macroeconomic policy implications. An analogue to the Phillips curve shows a positive relationship between inflation and capacity utilization. Some recent empirical work has shown that this relationship has broken down when using data after the mid-1980s and several popular explanations for this changing relationship, including advancements in technology and globalization, were put forward as possible explanations. In the first essay, we empirically investigate this issue using several threshold error correction models. We find, in the long run, a 1% increase in the rate of inflation leads to approximately a 0.0046% increase in capacity utilization. The asymmetric error correction structure shows that changes in capacity utilization show significant corrective measures only during booms while changes in inflation correct during both phases of the business cycle with the corrections being stronger during recessions. We also find that, in the short run, changes in the inflation rate do Granger cause capacity utilization while changes in capacity utilization do not Granger cause inflation. The Granger causality from inflation to capacity utilization can be interpreted as supporting recent calls made in the popular press by some economists that it may be desirable for the Federal Reserve Bank to try to induce some inflation in an effort to stimulate the economy. In the second essay, we examine the role of consumer confidence on economic activities like households’ consumption in good and bad economic times. We consider the “news” versus “animal spirit” approach interpretation of consumer confidence. In the wake of the Great Recession of 2008-09, many have called for confidence-boosting policies to help speed up the recovery. A recent study has reinforced these policy calls by showing that the Michigan Consumer Confidence Index contains important information about “news” on future productivity that has long-lasting effects on economic activities like aggregate consumption. Using US data, we show this conclusion is more nuanced when considering an economy that has different potential states. We investigate regime-switching models which use the National Bureau of Economic Research US business cycle expansion and contraction data to create an indicator series that distinguishes bad and good economic times and use this series to investigate impulse responses and variance decompositions. We show the connection between consumer confidence to some types of consumer purchases is important during good economic times but is relatively unimportant during bad economic times. We also use this type of model to investigate the connection between news and consumer confidence and this connection is also shown to be state dependent. In the context of the animal spirits versus news debate, our findings show that during economic expansions, consumer confidence shocks likely reflect news, while during economic contractions, consumer confidence shocks are consistent with animal spirits. These findings also have important implications for recent policy debates which consider whether confidence boosting policies, like raising inflation expectations on big-ticket items such as automobiles or business equipment, would lead to a faster recovery. The third essay investigates expectation shocks and their effect on the economy. For instance, this essay investigates whether the economy responds to expectation shocks in an importantly asymmetric way. A growing literature shows that agents' expectation about the future can lead to boom-bust cycles. These studies so far ignore the transmission effects of expectations on current economic activities across the policy regimes. Using the Survey of Professional Forecasters and Livingstone Survey data, this study empirically investigates the effects of expectation shocks on macroeconomic activities when policy regimes shift. Identifying a structural shock to expectations by using the timing of information in the forecast surveys and actual data releases, we show that the effects of agents' expectations about the future on current macroeconomic activities are asymmetric across the policy regimes. In particular, we find that a perception of good times ahead typically leads to a significant rise in current measures of economic activity in a hawkish regime relative to a dovish regime. We also find that monetary policy's reactions to agents' expectations are asymmetric across the policy regimes. Our findings do not support the views of critics of the central banks, who argued that keeping monetary policy too easy for too long is responsible for fueling the booms. Instead, our findings support the traditional view that a positive (negative) expectation about the future coincides with an anticipatory tightening (easing) of monetary policy.

Mathematical and statistical modeling have been at the forefront of many significant advances in many disciplines in both the academic and industry sectors. From behavioral sciences to hard core quantum mechanics in physics, mathematical modeling has made a compelling argument for its usefulness and its necessity in advancing the current state of knowledge in the 21rst century. In Finance and Insurance in particular, stochastic modeling has proven to be an effective approach in accomplishing a vast array of tasks: risk management, leveraging of investments, prediction, hedging, pricing, insurance, and so on. However, the magnitude of the damage incurred in recent market crisis of 1929 (the great depression), 1937 (recession triggered by lingering fears emanating from the great depression), 1990 (one year recession following a decade of steady expansion) and 2007 (the great recession triggered by the sub-prime mortgage crisis) has suggested that there are certain aspects of financial markets not accounted for in existing modeling. Explanations have abounded as to why the market underwent such deep crisis and how to account for regime change risk. One such explanation brought forth was the existence of regimes in the financial markets. The basic idea of market regimes underscored the principle that the market was intrinsically subjected to many different states and can switch from one state to another under unknown and uncertain internal and external perturbations. Implementation of such a theory has been done in the simplifying case of Markov regimes. The mathematical simplicity of the Markovian regime model allows for semi-closed or closed form solutions in most financial applications while it also allows for economically interpretable parameters. However, there is a hefty price to be paid for such practical conveniences as many assumptions made on the market behavior are quite unreasonable and restrictive. One assumes for instance that each market regime has a constant propensity of switching to any other state irrespective of the age of the current state. One also assumes that there are no intermediate states as regime changes occur in a discrete manner from one of the finite states to another. There is therefore no telling how meaningful or reliable interpretation of parameters in Markov regime models are. In this thesis, we introduced a sound theoretical and analytic framework for Levy driven linear stochastic models under a semi Markov market regime switching process and derived It\'o formula for a general linear semi Markov switching model generated by a class of Levy It'o processes (1). It'o formula results in two important byproducts, namely semi closed form formulas for the characteristic function of log prices and a linear combination of duration times (2). Unlike Markov markets, the introduction of semi Markov markets allows a time varying propensity of regime change through the conditional intensity matrix. This is more in line with the notion that the market's chances of recovery (respectively, of crisis) are affected by the recession's age (respectively, recovery's age). Such a change is consistent with the notion that for instance, the longer the market is mired into a recession, the more improbable a fast recovery as the the market is more likely to either worsens or undergo a slow recovery. Another interesting consequence of the time dependence of the conditional intensity matrix is the interpretation of semi Markov regimes as a pseudo-infinite market regimes models. Although semi Markov regime assume a finite number of states, we note that while in any give regime, the market does not stay the same but goes through an infinite number of changes through its propensity of switching to other regimes. Each of those separate intermediate states endows the market with a structure of pseudo-infinite regimes which is an answer to the long standing problem of modeling market regime with infinitely many regimes. We developed a version of Girsanov theorem specific to semi Markov regime switching stochastic models, and this is a crucial contribution in relating the risk neutral parameters to the historical parameters (3). Given that Levy driven markets and regime switching markets are incomplete, there are more than one risk neutral measures that one can use for pricing derivative contracts. Although much work has been done about optimal choice of the pricing measure, two of them jump out of the current literature: the minimal martingale measure and the minimum entropy martingale measure. We first presented a general version of Girsanov theorem explicitly accounting for semi Markov regime. Then we presented Siu and Yang pricing kernel. In addition, we developed the conditional and unconditional minimum entropy martingale measure which minimized the dissimilarity between the historical and risk neutral probability measures through a version of Kulbach Leibler distance (4). Estimation of a European option price in a semi Markov market has been attempted before in the restricted case of the Black Scholes model. The problems encountered then were twofold: First, the author employed a Markov chain Monte Carlo methods which relied much on the tractability of the likelihood function of the normal random sequences. This tractability is unavailable for most Levy processes, hence the necessity of alternative pricing methods is essential. Second, the accuracy of the parameter estimates required tens of thousands of simulations as it is often the case with Metropolis Hasting algorithms with considerable CPU time demand. Both above outlined issues are resolved by the development of a semi-closed form expression of the characteristic function of log asset prices, and it opened the door to a Fourier transform method which is derived on the heels of Carr and Madan algorithm and the Fourier time stepping algorithm (5). A round of simulations and calibrations is performed to better capture the performance of the semi Markov model as opposed to Markov regime models. We establish through simulations that semi Markov parameters and the backward recurrence time have a substantial effect on option prices ( 6). Differences between Markov and Semi Markov market calibrations are quantified and the CPU times are reported. More importantly, interpretation of risk neutral semi Markov parameters offer more insight into the dynamic of market regimes than Markov market regime models ( 7). This has been systematically exhibited in this work as calibration results obtained from a set of European vanilla call options led to estimates of the shape and scale parameters of the Weibull distribution considered, offering a deeper view of the current market state as they determine the in-regime dynamic crucial to determining where the market is headed. After introducing semi Markov models through linear Levy driven models, we consider semi Markov markets with nonlinear multidimensional coupled asset price processes (8). We establish that the tractability of linear semi Markov market models carries over to multidimensional nonlinear asset price models. Estimating equations and pricing formula are derived for historical parameters and risk neutral parameters respectively (9). The particular case of basket of commodities is explored and we provide calibration formula of the model parameters to observed historical commodity prices through the LLGMM method. We also study the case of Heston model in a semi Markov switching market where only one parameter is subjected to semi Markov regime changes. Heston model is one the most popular model in option pricing as it reproduces many more stylized facts than Black Scholes model while retaining tractability. However, in addition to having a faster deceasing smiles than observed, one of the most damning shortcomings of most diffusion models such as Heston model, is their inability to accurately reproduce short term options prices. An avenue for solving these issues consists in generalizing Heston to account for semi Markov market regimes. Such a solution is implemented and a semi analytic formula for options is obtained.

This paper proposes the basic predictive regression and Markov Regime-Switching regression to predict the excess stock returns in both US and Sweden stock markets. The analysis shows that the Markov Regime-Switching regression models out perform the linear ones in out-of-sample forecasting, which is due to the fact that the regime-switching models capture the economic expansion and recession better.

As the demand for electricity increases throughout the globe while we want to reduce the use of fossil fuels, the need for renewable energy sources is bigger than ever. In countries where solar power makes up a large part of the total energy production, the overall electricity spot price level has become lower. This thesis investigates the underlying mechanism that drives the energy market, and in specific, how the solar power impacts the electricity spot price. We present results from studies made in other markets, and introduce a Regime Switching model for explaining the impact in Sweden. We show that an increase of photovoltaics power has a price lowering effect on the daily price pattern in price area SE3 and SE4.

The objective of this dissertation is to study the dynamics of size and value risk premia in an equilibrium model with belief dependent preferences and to analyze the impact of investor attention on asset pricing. There is ample evidence that size and value risk premia are non-constant and vary over the business cycle. Empirical patterns, however, are unknown and traditional equilibrium models cannot fit the observed dynamic patterns. The representative agent model with belief dependent preferences is known to fit both unconditional moments such as the equity premium as well as times-series features of volatilities and market prices of risk. The basic model is extended to capture the dynamics of size and value risk premia. The representative agent in this model is a rational Bayesian decision maker who updates her beliefs continuously when new information arrives. However, information processing costs are non-zero and opportunity costs of non-continuous updating of beliefs are higher during times of crisis. In the second part of this dissertation, the representative agent model with beliefs dependent preferences is extended to incorporate the notion of investor attention. The attention version of the model is shown to increase the dynamic fit of equilibrium asset pricing quantities by dampening the volatility of bond yields, market prices of risk, and stock volatility. As such the inattention version of the model with belief dependent preferences is shown to improve the intertemporal fit. Chapter 1 provides a overview of existing studies about the dynamics of size and value risk premia and investor attention. Chapter 2 investigates the dynamic features for size and value risk premia. An asset pricing model with regime dependent risk aversion and incomplete information about economic regimes is introduced to derive closed-form formulas for market prices of risk, asset prices, their volatilities, and risk premia of value and size style indices. Both size and value risk premia vary across normal, recession and boom periods. The premia amplify in recession times but tend to reverse or disappear during boom times. Such findings match the historical performances of small-minus-big (SMB) and high-minus-low (HML) portfolios. Chapter 3 integrates investor attention into regime-switching learning model with regime-dependent risk aversion. The model provides a good fit to the time series of stock volatility, bond volatility and bond yields. Investor attention at the aggregate level is captured by a new representative agent measure which combines the continuously updated beliefs about regimes of a rational Bayesian decision maker with those of a decision maker using steady state regime probabilities. The new representative agent measure can capture the scenario where investor updates her beliefs about economic regimes according to time-varying attention to the available market information. Equilibrium asset pricing quantities are obtained in closed form in the extended model with investor attention. Unconditional asset pricing model moments match their empirical counterparts including the equity premium, the stock volatility and the correlations between stock returns and consumption and dividends. Dynamics features of the data can be well captured. Stock and bond volatilities, bond yield and interest rate time series all have smaller mean square errors compared to the model which does not consider investor attentions. The scale and volatilities for these financial time series are also close to real financial data.

京都大学 / 0048 / 新制・課程博士 / 博士(経済学) / 甲第19953号 / 経博第540号 / 新制||経||279(附属図書館) / 33049 / 京都大学大学院経済学研究科経済学専攻 / (主査)教授 江上 雅彦, 教授 若井 克俊, 教授 原 千秋 / 学位規則第4条第1項該当 / Doctor of Economics / Kyoto University / DFAM

The lumber industry in the South is an important sector, and has connections with many other key industries. The dynamics of the southern lumber market and its linkage with other related markets can be examined by the price transmissions. The first part of this study investigates vertical price transmission traced back to delivered sawlog market and stumpage market, and arrives at the conclusion that the supply chain is generally efficient with positive asymmetric transmission involved in one product. The second part explores the relationship between markets of the South and Pacific Northwest and concludes that the two markets are more balanced with each other after various demand and supply shocks with two regime switching models. This research will benefit market participants and policy makers to update their knowledge and obtain efficient information before decision making.

This paper focuses on how to improve strategic asset allocation in practice. Strategic asset allocation is perhaps the most fundamental issue in portfolio management and it has been thoroughly discussed in previous research. We take our starting point in the traditional work of Markowitz within portfolio optimization. We provide a new solution of how to perform portfolio optimization in practice, or more specifically how to estimate the covariance matrix, which is needed to perform conventional portfolio optimization. Many researchers within this field have noted that the return distribution of financial assets seems to vary over time, so called regime switching, which makes it dicult to estimate the covariance matrix. We solve this problem by using a Bayesian approach for developing a Markov chain Monte Carlo algorithm that detects break points in the return distribution of financial assets, thus enabling us to improve the estimation of the covariance matrix. We find that there are two break points during the time period studied and that the main difference between the periods are that the volatility was substantially higher for all assets during the period that corresponds to the financial crisis, whereas correlations were less affected. By evaluating the performance of the algorithm we find that the algorithm can increase the Sharpe ratio of a portfolio, thus that our algorithm can improve strategic asset allocation over time. / Detta examensarbete fokuserar på hur man kan förbättra tillämpningen av strategisk tillgångsslagsallokering i praktiken. Hur man allokerar kapital mellan tillgångsslag är kanske de mest fundamentala beslutet inom kapitalförvaltning och ämnet har diskuterats grundligt i litteraturen. Vårt arbete utgår från Markowitz traditionella teorier inom portföljoptimering och utifrån dessa tar vi fram ett nytt angreppssätt för att genomföra portföljoptimering i praktiken. Mer specifikt utvecklar vi ett nytt sätt att uppskatta kovar-iansmatrisen för avkastningsfördelningen för finansiella tillgångar, något som är essentiellt för att kunna beräkna de optimala portföljvikterna enligt Markowitz. Det påstås ofta att avkastningens fördelning förändras över tid; att det sker så kallade regimskiften, vilket försvårar uppskattningen av kovariansmatrisen. Vi löser detta problem genom att använda ett Bayesiansk angreppssätt där vi utvecklar en Markov chain Monte Carlo-algoritm som upptäcker brytpunkter i avkastningsfördelningen, vilket gör att uppskattningen av kovar-iansmatrisen kan förbättras. Vi finner två brytpunkter i fördelningen under den studerade tidsperioden och den huvudsakliga skillnaden mellan de olika tidsperioderna är att volatiliten var betydligt högre för samtliga tillgångar under den tidsperiod som motsvaras av finanskrisen, medan korrelationerna mellan tillgångsslagen inte påverkades lika mycket. Genom att utvärdera hur algoritmen presterar finner vi att den ökar en portföljs Sharpe ratio och således att den kan förbättra den strategiska allokeringen mellan tillgångsslagen över tid.

This thesis investigates the application of Hidden Markov Models (HMMs) to model financial volatility-regimes and presents a parameter learning approach using real-world data. Although HMMs as regime-switching models are established, empirical studies regarding the parameter estimation of such models remain limited. We address this issue by creating a systematic approach (algorithm) for parameter learning using Python programming and the hmmlearn library. The algorithm works by initializing a wide range of random parameter values for an HMM and maximizing the log-likelihood of an observation sequence, obtained from market data, using expectation-maximization; the optimal number of volatility regimes for the HMM is determined using information criterion. By training models on historical market and volatility index data, we found that a discrete model is favored for volatility modeling and option pricing due to its low complexity and high customizability, and a Gaussian model is favored for asset allocation and price simulation due to its ability to model market regimes. However, practical applications of these models were not researched, and thus, require further studies to test and calibrate.

Recently, the financial world witnessed a series of major defaults by several institutions and investment banks. Therefore, it is not at all surprising that credit risk analysis have turned out to be one of the most important aspect among the finance community. As credit derivatives are long term instruments, it is affected by the changes in the market conditions. Thus, it is a appropriate to take into consideration the effects of the market economy. This thesis addresses some of the important issues in credit risk analysis in a regime switching market. The main contribution in this thesis are the followings: (1) We determine the price of default able bonds in a regime switching market for structural models with European type payoff. We use the method of quadratic hedging and minimal martingale measure to determine the defaultble bond prices. We also obtain hedging strategies and the corresponding residual risks in these models. The defaultable bond prices are obtained as solution to a system of PDEs (partial differential equations) with appropriate terminal and boundary conditions. We show the existence and uniqueness of the system of PDEs on an appropriate domain. (2) We carry out a similar analysis in a regime switching market for the reduced form models. We extend some of the existing models in the literature for correlated default timings. We price single-name and multi-name credit derivatives using our regime switching models. The prices are obtained as solution to a system of ODEs(ordinary differential equations) with appropriate terminal conditions. (3) The price of the credit derivatives in our regime switching models are obtained as solutions to a system of ODEs/PDEs subject to appropriate terminal and boundary conditions. We solve these ODEs/PDEs numerically and compare the relative behavior of the credit derivative prices with and without regime switching. We observe higher spread in our regime switching models. This resolves the low spread discrepancy that were prevalent in the classical structural models. We show further applications of our model by capturing important phenomena that arises frequently in the financial market. For instance, we model the business cycle, tight liquidity situations and the effects of firm restructuring. We indicate how our models may be extended to price various other credit derivatives.