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Quantitative finance

#1
Quantitative Models of Commercial Policy
What tariffs would countries impose if they did not have to fear any retaliation? What would occur if there was a complete breakdown of trade policy cooperation? What would be the outcome if countries engaged in fully efficient trade negotiations? And what would happen to trade policy cooperation if the world trading system had a different institutional design? While such questions feature prominently in the theoretical trade policy literature, they have proven difficult to address empirically, because they refer to what-if scenarios for which direct empirical counterparts are hard to find. In this chapter, I introduce research which suggests overcoming this difficulty by applying quantitative models of commercial policy.
Attachments
Quantitative Models of Commercial Policy.pdf
(934.46 KiB) Downloaded 69 times


Quantitative finance

#2
This paper shows that the quantitative predictions of a DSGE model with an endogenous collateral constraint are consistent with key features of the emerging markets' Sudden Stops. Business cycle dynamics produce periods of expansion during which the ratio of debt to asset values raises enough to trigger the constraint. This sets in motion a deflation of Tobin's Q driven by Irving Fisher's debt-deflation mechanism, which causes a spiraling decline in credit access and in the price and quantity of collateral assets. Output and factor allocations decline because the collateral constraint limits access to working capital financing. This credit constraint induces significant amplification and asymmetry in the responses of macro-aggregates to shocks. Because of precautionary saving, Sudden Stops are low probability events nested within normal cycles in the long run.
Attachments
Sudden Stops, Financial Crises and Leverage - A Fisherian Deflation of Tobin's Q.pdf
(484.96 KiB) Downloaded 70 times

Review of Statistical Arbitrage, Cointegration, and Multivariate Ornstein-Uhlenbeck

#3
We introduce the multivariate Ornstein-Uhlenbeck and discuss how it generalizes a vast class of continuous-time and discrete-time multivariate processes. Relying on the simple geometrical interpretation of the dynamics of the Ornstein-Uhlenbeck process we introduce cointegration and its relationship to statistical arbitrage. We illustrate an application to swap contract strategies. Fully documented code illustrating the theory and the applications is available at MATLAB Central.
Attachments
Review of Statistical Arbitrage, Cointegration, and Multivariate Ornstein-Uhlenbeck.pdf
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Re: Review of Statistical Arbitrage, Cointegration, and Multivariate Ornstein-Uhlenbeck

#4
seekers wrote:
Wed May 31, 2017 1:50 am
We introduce the multivariate Ornstein-Uhlenbeck and discuss how it generalizes a vast class of continuous-time and discrete-time multivariate processes. Relying on the simple geometrical interpretation of the dynamics of the Ornstein-Uhlenbeck process we introduce cointegration and its relationship to statistical arbitrage. We illustrate an application to swap contract strategies. Fully documented code illustrating the theory and the applications is available at MATLAB Central.
Thanks for this - was looking for something similar :)

Technical Analysis in Financial Markets

#5
The efficient markets hypothesis states that in highly competitive and developed markets it is impossible to derive a trading strategy that can generate persistent excess profits after correction for risk and transaction costs. Andrew Lo, in the introduction of Paul Cootner's "The Random Character of Stock Prices" (2000 reprint, p.xi), suggests even to extend the definition of efficient markets so that profits accrue only to those who acquire and maintain a competitive advantage. Then, those profits may simply be the fair reward for unusual skill, extraordinary effort or breakthroughs in financial technology. The goal of this thesis is to test the weak form of the efficient markets hypothesis by applying a broad range of technical trading strategies to a large number of different data sets. In particular, we focus on the question whether, after correcting for transaction costs, risk and data snooping, technical trading rules have statistically significant forecasting power and can generate economically significant profits.

In Chapter 2, a large set of 5350 trend-following technical trading rules is applied to the price series of cocoa futures contracts traded at the London International Financial Futures Exchange (LIFFE) and the New York Coffee, Sugar and Cocoa Exchange (CSCE), in the period January 1983 through June 1997. The trading rule set is also applied to the Pound-Dollar exchange rate in the same period. It is found that 58% of the trading rules generate a strictly positive excess return, even if a correction is made for transaction costs, when applied to the LIFFE cocoa futures prices. Moreover, a large set of trading rules exhibits statistically significant forecasting power if applied to the LIFFE cocoa futures series. On the other hand, the same set of strategies performs poor on the CSCE cocoa futures prices, with only 12% generating strictly positive excess returns and hardly showing any statistically significant forecasting power. Bootstrap techniques reveal that the good results found for the LIFFE cocoa futures price series cannot be explained by several popular null models like a random walk, autoregressive and GARCH model, but can be explained by a structural break in trend model. The large difference in the performance of technical trading may be attributed to a combination of the demand/supply mechanism in the cocoa market and an accidental influence of the Pound-Dollar exchange rate, reinforcing trends in the LIFFE cocoa futures but weakening trends in the CSCE cocoa futures. Furthermore, our case study suggests a connection between the success or failure of technical trading and the relative magnitudes of trend, volatility and autocorrelation of the underlying series.

In the next three chapters, Chapters 3-5, a set of trend-following technical trading rules is applied to the price history of several stocks and stock market indices. Two different performance measures are used to select the best technical trading strategy, namely the mean return and the Sharpe ratio criterion. Corrections are made for transaction costs. If technical trading shows to be profitable, then it could be the case that these profits are merely the reward for bearing the risk of implementing technical trading. Therefore Sharpe-Lintner capital asset pricing models (CAPMs) are estimated to test this hypothesis. Furthermore, if technical trading shows economically and statistically significant forecasting power after corrections are made for transaction costs and risk, then it is tested whether the selected technical trading strategy is genuinely superior to the buy-and-hold benchmark also after a correction is made for data snooping. Tests utilized to correct for data snooping are White's (2000) Reality Check (RC) and Hansen's (2001) test for superior predictive ability (SPA). Finally, it is tested with a recursively optimizing and testing method whether technical trading shows true out-of-sample forecasting power. For example, recursively at the beginning of each month, the strategy with the highest performance during the preceding six months is selected to generate trading signals in that month.

In Chapter 3, a set of 787 trend-following technical trading rules is applied to the Dow-Jones Industrial Average (DJIA) and to 34 stocks listed in the DJIA in the period January 1973 through June 2001. Because numerous research papers found that technical trading rules show economically and statistically significant forecasting power in the era until 1987, but not in the period thereafter, we split our sample in two subperiods: 1973-1986 and 1987-2002. For the mean return, as well as the Sharpe ratio selection criterion, it is found that in all periods for each data series a technical trading rule can be found that is capable of beating the buy-and-hold benchmark, even if a correction is made for transaction costs. Furthermore, if no transaction costs are implemented, then for most data series it is found by estimating Sharpe-Lintner CAPMs that technical trading generates risk-corrected excess returns over the risk-free interest rate. However, as transaction costs increase the null hypothesis that technical trading rule profits are just the reward for bearing risk is not rejected for more and more data series. Moreover, if as little as 0.25% transaction costs are implemented, then the null hypothesis that the best technical trading strategy found in a data set is not superior to the buy-and-hold benchmark after a correction is made for data snooping, is neither rejected by the RC nor by the SPA-test for all data series examined. Finally, the recursive optimizing and testing method does not show economically and statistically significant risk-corrected out-of-sample forecasting power of technical trading. Thus, in this chapter, no evidence is found that trend-following technical trading rules can forecast the direction of the future price path of the DJIA and stocks listed in the DJIA.

In Chapter 4, the same technical trading rule set is applied to the Amsterdam Stock Exchange Index (AEX-index) and to 50 stocks listed in the AEX-index in the period January 1983 through May 2002. For both selection criteria, it is found that for each data series a technical trading strategy can be selected that is capable of beating the buy-and-hold benchmark, also after correction for transaction costs. Furthermore, by estimating Sharpe-Lintner CAPMs, it is found for both selection criteria in the presence of 1% transaction costs that for approximately half of the data series the best technical trading strategy has statistically significant risk-corrected forecasting power and even reduces risk of trading. Next, a correction is made for data snooping by applying the RC and the SPA-test. If the mean return criterion is used for selecting the best strategy, then both tests lead for almost all data series to the same conclusion if as little as 0.10% transaction costs are implemented, namely that the best technical trading strategy selected by the mean return criterion is not capable of beating the buy-and-hold benchmark after correcting for the specification search that is used to select the best strategy. In contrast, if the Sharpe ratio selection criterion is used, then for one third of the data series the null of no superior forecasting power is rejected by the SPA-test, even after correction for 1% transaction costs. Thus, in contrast to the findings for the stocks listed in the DJIA in Chapter 3, we find that technical trading has economically and statistically significant forecasting power for a group of stocks listed in the AEX-index, after a correction is made for transaction costs, risk and data snooping, if the Sharpe ratio criterion is used for selecting the best technical trading strategy. Finally, the recursive optimizing and testing method does show out-of-sample forecasting profits of technical trading. Estimation of Sharpe-Lintner CAPMs shows, after correction for 0.10% transaction costs, that the best recursive optimizing and testing method has statistically significant risk-corrected forecasting power for more than $40\%$ of the data series examined. However, if transaction costs increase to 0.50% per trade, then for almost all data series the best recursive optimizing and testing procedure has no statistically significant risk-corrected forecasting power anymore. Thus, only for sufficiently low transaction costs technical trading is economically and statistically significant for a group of stocks listed in the AEX-index.

In Chapter 5, the set of 787 trend-following technical trading strategies is applied to 50 local main stock market indices in Africa, North and South America, Asia, Europe, the Middle East and the Pacific, and to the MSCI World Index in the period January 1981 through June 2002. We consider the case of a U.S.-based trader and recompute all profits in U.S. Dollars. It is found that half of the indices could not even beat a continuous risk-free investment. However, as in Chapters 3 and 4, it is found for both selection criteria that for each stock market index a technical trading strategy can be selected that is capable of beating the buy-and-hold benchmark, also after correction for transaction costs. Furthermore, after implementing 1% costs per trade, still for half of the indices a statistically significant risk-corrected forecasting power is found by estimating CAPMs. If also a correction is made for data snooping, then we find, as in Chapter 4, that both selection criteria yield different results. In the presence of 0.50% transaction costs, the null hypothesis of no superior predictive ability of the best technical trading strategy selected by the mean return criterion over the buy-and-hold benchmark after correcting for the specification search is not rejected for most indices by both the RC and SPA-test. However, if the Sharpe ratio criterion is used to select the best strategy, then for one fourth of the indices, mainly the Asian ones, the null hypothesis of no superior forecastability is rejected by the SPA-test, even in the presence of 1% transaction costs. Finally, the recursive optimizing and testing method does show out-of-sample forecasting profits, also in the presence of transaction costs, mainly for the Asian, Latin American, Middle East and Russian stock market indices. However, for the U.S., Japanese and most Western European stock market indices the recursive out-of-sample forecasting procedure does not show to be profitable, after implementing little transaction costs. Moreover, for sufficiently high transaction costs it is found, by estimating CAPMs, that technical trading shows no statistically significant risk-corrected out-of-sample forecasting power for almost all of the stock market indices. Only for low transaction costs (<=0.25% per trade) economically and statistically significant risk-corrected out-of-sample forecasting power of trend-following technical trading techniques is found for the Asian, Latin American, Middle East and Russian stock market indices.

In Chapter 6, a financial market model with heterogeneous adaptively learning agents is developed. The agents can choose between a fundamental forecasting rule and a technical trading rule. The fundamental forecasting rule predicts that the price returns back to the fundamental value with a certain speed, whereas the technical trading rule is based on moving averages. The model in this chapter extends the Brock and Hommes (1998) heterogeneous agents model by adding a moving-average technical trading strategy to the set of beliefs the agents can choose from, but deviates by assuming constant relative risk aversion, so that agents choosing the same forecasting rule invest the same fraction of their wealth in the risky asset. The local dynamical behavior of the model around the fundamental steady state is studied by varying the values of the model parameters. A mixture of theoretical and numerical methods is used to analyze the dynamics. In particular, we show that the fundamental steady state may become unstable due to a Hopf bifurcation. The interaction between fundamentalists and technical traders may thus cause prices to deviate from their fundamental value. In this heterogeneous world, the fundamental traders are not able to drive the moving average traders out of the market, but fundamentalists and technical analysts coexist forever with their relative importance changing over time.
Attachments
Technical Analysis in Financial Markets.pdf
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Linear Factor Models: Theory, Applications and Pitfalls

#6
We clarify the rationale and differences between the two main categories of linear factor models, namely dominant-residual and systematic-idiosyncratic. We discuss the five different, yet interconnected areas of quantitative finance where linear factor models play an essential role: multivariate estimation theory, asset pricing theory, systematic strategies, portfolio optimization, and risk attribution. We present a comprehensive list of common pitfalls and misunderstandings on linear factor models. An appendix details all the calculations. Supporting code is available for download.
Attachments
Linear Factor Models - Theory, Applications and Pitfalls.pdf
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Uncovering Trend Rules

#7
Trend rules are widely used to infer whether financial markets show an upward or downward trend. By taking suitable long or short positions, one can profit from a continuation of these trends. Conventionally, trend rules are based on moving averages (MAs) of prices rather than returns, which obscures how much weight is assigned to different historical time periods. In this paper, we show how to uncover the underlying historical weighting schemes of price MAs and combinations of price MAs. This leads to surprising and useful insights about popular trend rules, for example that some trend rules have inverted information decay (i.e., distant returns have more weight than recent ones) or hidden mean-reversion patterns. This opens the possibility for improving the trend rule by analyzing the added value of the mean reversion part. We advocate designing trend rules in terms of returns instead of prices, as they offer more flexibility and allow for adjusting trend rules to autocorrelation patterns in returns.
Attachments
Uncovering Trend Rules.pdf
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Integrated Time-Series Analysis of Spot and Option Prices

#8
This paper examines the joint time series of the S&P 500 index and near-the-money short-dated option prices with an arbitrage-free model, capturing both stochastic volatility and jumps. Jump-risk premia uncovered from the joint data respond quickly to market volatility, becoming more prominent during volatile markets. This form of jump-risk premia is important not only in reconciling the dynamics implied by the joint data, but also in explaining the volatility "smirks" of cross-sectional options data. Further diagnostic tests suggest a stochastic-volatility model with two factors --- one strongly persistent, the other quickly mean-reverting and highly volatile.
Attachments
Integrated Time-Series Analysis of Spot and Option Prices.pdf
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Automated Trading with Boosting and Expert Weighting

#9
We propose a multi-stock automated trading system that relies on a layered structure consisting of a machine learning algorithm, an online learning utility, and a risk management overlay. Alternating decision tree (ADT), which is implemented with Logitboost, was chosen as the underlying algorithm. One of the strengths of our approach is that the algorithm is able to select the best combination of rules derived from well-known technical analysis indicators and is also able to select the best parameters of the technical indicators. Additionally, the online learning layer combines the output of several ADTs and suggests a short or long position. Finally, the risk management layer can validate the trading signal when it exceeds a specified non-zero threshold and limit the application of our trading strategy when it is not profitable. We test the expert weighting algorithm with data of 100 randomly selected companies of the S&P 500 index during the period 2003–2005. We find that this algorithm generates abnormal returns during the test period. Our experiments show that the boosting approach is able to improve the predictive capacity when indicators are combined and aggregated as a single predictor. Even more, the combination of indicators of different stocks demonstrated to be adequate in order to reduce the use of computational resources, and still maintain an adequate predictive capacity.
Attachments
Automated Trading with Boosting and Expert Weighting.pdf
(454.31 KiB) Downloaded 64 times

Band-Pass Filtering, Cointegration, and Business Cycle Analysis

#10
This paper critically assesses the practice of band-pass filtering (the non-structural, frequency-domain based decomposition of economic time series into trend and cyclical components), making two main points. First, it is shown that: (a) depending on the stochastic properties of the filtered process, the band-pass filtered cyclical component is entirely authentic, partly or mostly spurious, or even entirely spurious; and (b) as a simple consequence of the Lucas critique, the degree of authenticity of band-pass filtered cyclical components crucially depends on the monetary rule followed by the policy-maker.

Second, taking a number of macroeconomic models as data-generation processes it is shown that band-pass filtering: (a) may markedly distort key business cycle stylised facts, as captured by the cross-correlations and the cross-spectral statistics between the cyclical components of the variables of interest and the cyclical component of GDP; and (b) may well create entirely spurious stylised facts. For example: both productivity and the money supply may appear procyclical even when they follow random walks by construction; the real wage may appear procyclical when in fact it is countercyclical; in general, the Phillips correlation between inflation and the cyclical component of economic activity will appear weaker than it is in reality. Again, the degree of authenticity of business cycle stylised facts uncovered via band-pass filtering crucially depends on the monetary rule followed by the policy-maker.
Attachments
Band-Pass Filtering, Cointegration, and Business Cycle Analysis.pdf
(202.87 KiB) Downloaded 59 times


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